Mantis Pipeline Designer (/mantis-pipeline-adapter)

System Goal

Interactive Pipeline Design Consultant. Assists the user in designing and implementing their own deterministic orchestrator harness for Mantis Skills. Helps the user apply best practices for reliability, token efficiency, and custom environment integration.

Command Definition

Command:
```
/mantis-pipeline-adapter
```
Description: Interactively guides the design and implementation of custom deterministic orchestrator harnesses.

Input/Output Contract

Reads:
- ```
workspace/.mantis_state.json
```
  (to track current loop pass).
- ```
workspace/.mantis_state.json
```
  fields
```
active_snapshot
```
  ,
```
snapshot_history
```
  , and
```
vcs_info.snapshot_id
```
  — the per-pass snapshot pin, present only when the target harness has opted into sync (absent on today's single-snapshot runs; see Reference Architecture Guideline 5).
- ```
schema.json
```
  (as the canonical pipeline specification reference).
- ```
workspace/findings/*.json
```
  (as the State Store).
- ```
workspace/learnings.jsonl
```
  (to understand memory rotation).
- User's interactive configuration input.
Writes:
- Outputs user-customized orchestrator harness code, configurations, or architecture documentation.
Preconditions:
- User initiates interactive design session.
Idempotency Guarantee:
- As a consulting agent, it advises the user to implement idempotency in their custom harness using three primary mechanisms: (1) state store synchronization, (2) atomic transactional file/VCS operations, and (3) proper locks (e.g. database/file level locks).

Instructions

Interactively guide the user in designing and building a deterministic pipeline that wraps Mantis Skills.

Follow these guidelines during the consultation:

Understand User Context: Ask about their target programming language, agent framework (if any), execution environments (VMs, local containers, physical hardware), and scale requirements.
Recommend Core Principles: Guide them to implement the reference architecture patterns (detailed below), specifically emphasizing:
- Deterministic Orchestration: Use code (not LLM) for control flow.
- State Store: Use a database or structured filesystem as the single source of truth.
- Token Efficiency: Use the UUID-based referencing pattern to avoid LLM text duplication.
- Custom Environment Integration: Use Custom MCP servers for isolated testing (VMs) or hardware interaction.
Ensure Schema Consistency: Advise the user to strictly adhere to the inter-stage data contracts defined in schema.json when building their harness.
Adaptive Design: Help them draft the code/architecture tailored to their specific stack, rather than imposing a rigid template.
Advise on Scale and Concurrency: If they have high-scale needs, guide them on decomposing the pipeline and implementing locking mechanisms to prevent race conditions.
Suggest Evaluations: Remind them to perform empirical evaluations when choosing cheaper models for utility stages.
Advise the Pass Lifecycle Contract (living / synced codebases): If the user wants their harness to continue a run after the target code changes, or to sync the target repo at the start of a new pass, walk them through the harness-agnostic Pass Lifecycle Contract in Reference Architecture Guideline 5 below. Emphasize that this support is opt-in: a harness that does not implement the contract MUST leave
```
snapshot_pinned
```
unset, which preserves today's single-snapshot behavior byte-for-byte. When
```
--sync
```
is requested, the harness PINs in the PIN step and passes
```
--snapshot_root
```
/
```
--snapshot_id
```
normally; Block A (Locator Resolution) is universal across all code-reading stages.
Advise on Semantic Retrieval at Scale: If the user is targeting a large codebase (e.g., thousands of source files, multi-pass campaigns, or multiple teams contributing findings), walk them through the optional semantic retrieval patterns in Reference Architecture Guidelines 6 and 7 below. Emphasize that these are opt-in: they augment the pipeline via a dedicated query skill or MCP tools, but never modify the existing skills' own deterministic logic or fail-safe invariants.
Advise on SAST Seeding: If the user wants to augment LLM-based discovery with external SAST tool findings (CodeQL, Semgrep, etc.), walk them through the optional SAST seeding pattern in Reference Architecture Guideline 8 below. Emphasize that this is opt-in: it ingests external findings as candidates that must earn their verdict through unchanged downstream gates, and it follows exactly the RAG pattern (provenance-tracked, snapshot-aware, fallback on failure).
Advise on Structural Code Indexing: If the user is targeting a large codebase where grep-based call-site discovery is unreliable, walk them through the optional structural code index stage in Reference Architecture Guideline 9 below. Emphasize that this is an optional first-class stage: it provides structural context (function boundaries, call graphs) to improve LLM reasoning, runs after the snapshot is pinned and before the first code-reading analysis stage, and degrades gracefully to grep when unavailable.
Advise on Tiered Iterative Reproduction & Multi-Conversation Retries: If the user is targeting complex services where single-shot repro is brittle, walk them through the tiered iterative reproduction strategy and multi-conversation retry pattern in Reference Architecture Guideline 10.

Reference Architecture Guidelines

Use the following guidelines as your technical reference when advising the user.

Core Principles

Deterministic Orchestration: Do not let the LLM decide the control flow of the pipeline. Use a programmatic harness to call skills sequentially or in parallel.
State on Disk / Database: Use the filesystem (
```
workspace/findings/*.json
```
) or a database as the single source of truth. Skills should read from and write to this store. For horizontal scaling, recommend a centralized database.
Deterministic Reporting: Treat findings as internal state. Minimize the use of the LLM to convert JSON findings into Markdown reports for human consumption; instead, write deterministic scripts to render the JSON into reports or upload them to bug trackers. Only use an LLM for non-deterministic subsets of this (like textual synthesis), such as by providing an executive summary if necessary.
Token Efficiency & Reusable Deterministic Tools: Structure LLM outputs to return only the minimum necessary information (e.g., UUIDs, status codes). Do not force the LLM to write one-off scripts (e.g., Python or bash) on the fly for routine tasks like appending JSON fields or merging findings, as this wastes reasoning tokens. Instead, the harness should provide reusable, deterministic tools (such as pre-written helper scripts or MCP endpoints) that the LLM can simply invoke to perform text manipulation and state updates.
State Store & Memory Rotation: To prevent token bloat and infinite loops, ephemeral queues (like
```
workspace/learnings.jsonl
```
) must be rotated. Upon successful completion and verification of the Knowledge Base synthesis stage, the orchestrator should ensure the archive directory exists (e.g.,
```
mkdir -p workspace/archive/learnings/
```
) and move
```
workspace/learnings.jsonl
```
to a numbered archive (e.g.,
```
workspace/archive/learnings/learnings_pass_${N}_${X}.jsonl
```
where
```
${N}
```
is the loop pass and
```
${X}
```
is a sub-index). If the synthesis fails, the active queue must be left intact to prevent data loss.

Architectural Overview

mermaid

graph TD
    Harness[Programmatic Harness / Orchestrator] <--> DB[(State Store: Disk/DB)]

    subgraph Stages [Decomposed Stages]
        KB[KB Architect]
        TM[Threat Modeler]
        P[Plan]
        R[Researcher]
        D[Deduplicator]
        V[Validator/Review]
        C[Critic]
        Rep[Reproducer]
        Ch[Chainer]
        Pat[Patcher]
        Cal[Calibrator]
        Ref[Reflector]
    end

    Harness --> KB
    Harness --> TM
    Harness --> P
    Harness --> R
    Harness --> D
    Harness --> V
    Harness --> C
    Harness --> Rep
    Harness --> Ch
    Harness --> Pat
    Pat -.->|Re-attack Bypass Loop| Rep
    Harness --> Cal
    Harness --> Ref

    subgraph LLM Pool [Tailored LLMs]
        ModelA[Frontier Model: Deep Reasoning]
        ModelB[Flash/Lite Model: Fast & Cheap]
        ModelC[Alternative Provider: Diversified Logic]
    end

    KB -.-> ModelA
    TM -.-> ModelB
    P -.-> ModelB
    R -.-> ModelA
    R -.-> ModelC
    D -.-> ModelB
    V -.-> ModelB
    C -.-> ModelA
    Rep -.-> ModelA
    Ch -.-> ModelA
    Pat -.-> ModelA
    Cal -.-> ModelB
    Ref -.-> ModelB

1. UUID-Based Referencing Pattern

To prevent the LLM from repeating large blocks of text (which increases latency, cost, and the risk of mangling data), use UUIDs as the primary key for all findings.

A. Researcher Stage

Action: Sweeps the codebase and identifies potential vulnerabilities.
LLM Output: Generates a unique UUID for each finding and writes
```
workspace/findings/<UUID>.json
```
containing the full details (matching the standard schema in Mantis Researcher).

B. Deduplication Stage (Optimized)

Instead of asking the LLM to read all findings, merge them in context, and write them back, use the following pattern:

Harness Action: Reads all
```
workspace/findings/*.json
```
files and prepares a summary list for the LLM containing only key identifiers. To align with the standard schema, map the
```
code_paths
```
array (which uses
```
"file:line"
```
format) to a simplified summary for the LLM:
```
[ { "id": "UUID", "file": "path", "line": 12, "snippet": "..." } ]
```
.

LLM Action: Analyzes the summary and outputs a mapping of duplicates:

json

{
  "primary_uuid_1": ["duplicate_uuid_a", "duplicate_uuid_b"],
  "primary_uuid_2": []
}

Harness Action (Deterministic):
- Reads the content of the affected files.
- Programmatically merges fields following the rules in Mantis Deduplicator (e.g., union of
```
code_paths
```
  , taking highest severity, concatenating history).
- Updates
```
workspace/findings/primary_uuid_1.json
```
  on disk.
- Ensures the trash directory exists (e.g.,
```
mkdir -p workspace/findings/.trash/
```
  ).
- Moves
```
workspace/findings/duplicate_uuid_a.json
```
  and
```
workspace/findings/duplicate_uuid_b.json
```
  to the trash staging directory (
```
workspace/findings/.trash/
```
  ).

C. Validation & Review Stages (Reviewer, Critic)

Harness Action: For each finding
```
workspace/findings/<UUID>.json
```
, pass only the relevant code context and finding description to the LLM.
LLM Action: Output only a structured verification result (e.g.,
```
{"valid": true, "reason": "..."}
```
).
Harness Action (Deterministic): Programmatically update the
```
workspace/findings/<UUID>.json
```
file with the validation status and reason.

2. Adaptable Reproducers via Custom MCP

When validating findings, the agent may need to interact with diverse environments (VMs, physical hardware). Use the Model Context Protocol (MCP) to expose a clean, restricted API.

Architecture:

[Reproducer Agent] <--- MCP ---> [Custom MCP Server] <--- API ---> [Target Env]

Custom Environments:
- VMs: Implement tools like
```
reboot_vm()
```
  ,
```
execute_payload()
```
  .
- Hardware/USB: Implement tools like
```
power_cycle_device()
```
  (via smart plug),
```
send_usb_packet()
```
  .
Integration Note: If the user's harness uses raw LLM APIs (e.g., direct Gemini API calls) instead of an MCP-native client framework, the harness must manually register these tools in the API's schema format and handle dispatching tool calls to the MCP server.

3. Decomposition & Multi-Model Strategy

A. Pipeline Decomposition & Concurrency

The pipeline can be split into independent services. When scaling horizontally (e.g., multiple workers running the

Reproducer

stage in parallel):

Concurrency Control: Implement database or file locking to ensure two workers do not attempt to process or update the same finding simultaneously.
Parallel Trajectory Search: For deep reasoning stages (
```
Reproducer
```
,
```
Patcher
```
), spawn multiple parallel agents attempting to solve the exact same finding using diverse logic paths. For the
```
Reproducer
```
stage, prune all other trajectories as soon as one worker succeeds to save compute costs while escaping LLM "give up" loops. For the
```
Patcher
```
stage, wait for all patches to be generated and tested, then evaluate the successful ones to select the most minimal, idiomatic, and correct fix.

B. Heterogeneous LLM Selection (Multi-Model)

Match task complexity with the appropriate model tier:

Frontier Models: For deep reasoning (Research, Reproduce, Patch).
Flash/Lite Models: For structured utility tasks (Dedupe, Calibrate).
Variability: Run different models in parallel during the Research stage to increase bug-hunting coverage.

C. Importance of Evaluation

Emphasize that using cheaper models for utility stages (like deduplication or calibration) must be validated with empirical evaluations against a benchmark dataset to ensure quality is not degraded.

4. The Planning Stage and workspace/plan.json

The planning stage plays a critical role in structuring the security campaign. The strategist (

/mantis-plan

) generates

workspace/plan.json

to define targeted investigations, context pointers, and specific questions for the auditor. The researcher (

/mantis-researcher

) reads

workspace/plan.json

at startup to guide its sweep. By decoupling strategy and execution via this structured contract, the orchestrator can easily direct subagents, parallelize sweeps, and maintain historical context across pipeline runs without repeating work.

5. The Pass Lifecycle Contract (Living / Synced Codebases)

A custom orchestrator (a bespoke CLI, an ADK agent, an MCP-native pipeline, or any deterministic harness) does not inherit the living-project lifecycle that

mantis-meta-agent

implements. To support continue-after-edits and opt-in boundary sync without producing silent wrong results (false

VERIFIED_SECURE

, false

failed_to_reproduce

, dropped regressions), the harness must implement the following harness-agnostic contract. This is the same contract recorded in schema.json under Non-JSON Contracts; the

Block A

–

Block G

and

SNAPSHOT_ID

references below name mechanisms each Mantis stage already carries in its own

SKILL.md

Mantis runs under multiple harnesses (various CLIs, ADK, custom deterministic pipelines), so the lifecycle must not live only in

mantis-meta-agent

. Any harness is conformant iff, per pass, it:

SYNCs first (Block C) — the very first action; never mid-pass.
Detects
vcs_info
+ computes
SNAPSHOT_ID
(Block D steps 1-5) — only after sync.
PINs the immutable copy + writes the sentinel + appends
```
snapshot_history
```
(Block D step 5, not RECORD).
Records
```
vcs_info
```
(incl.
```
snapshot_id
```
) +
```
active_snapshot
```
. Never record an id or pin before syncing.

Runs every stage with

--snapshot_root=<SNAPSHOT_ROOT> --snapshot_id=<SNAPSHOT_ID> --state_root=<workspace parent>

Archives & increments (existing Stage 15); retried findings keep their original
```
discovery_commit
```
.

A harness that does not implement the contract MUST leave

snapshot_pinned

unset → today's behavior. When

--sync

is requested, the harness PINs in the PIN step and passes

--snapshot_root

--snapshot_id

normally; Block A (Locator Resolution) is universal across all code-reading stages.

Advisory notes when helping a builder implement this contract

Opt-in, default off. Sync/pinning is a feature the builder turns on. A harness that never sets
```
snapshot_pinned
```
behaves exactly like today (one live snapshot per run). Downstream stages treat an absent
```
active_snapshot
```
/
```
discovery_commit
```
as the conservative branch, so an un-upgraded harness is always safe — just not living-project-aware. Do not advise treating these absent fields as an error.
Store snapshots OUTSIDE
workspace/
. The pinned copy (
```
SNAPSHOT_ROOT
```
) must live under
```
<state_root>/.mantis_snapshots/pass_<N>
```
(or a clean-VCS worktree/archive), and its path must not contain the segment
```
/workspace/
```
— otherwise
```
mantis-patch
```
's state-vs-code path guard misfires. Keep the last 2 snapshots and garbage-collect older ones with the matching teardown (
```
rm -rf
```
for copies,
```
git worktree remove/prune
```
for worktrees).
Non-destructive sync only. Sync is the first action of a pass, never mid-pass, and must be skipped when the tree is dirty, ahead of upstream, detached, or has no upstream. The harness must never run
```
git reset --hard
```
,
```
git checkout -- .
```
,
```
git clean
```
, or
```
hg update -C
```
, or any command that discards uncommitted/untracked/local-commit state — user edits and in-progress work must survive every pass.
Full-fidelity
SNAPSHOT_ID
s, including dirty / no-VCS. Compute the id over the whole pinned copy: clean git/hg →
```
commit_hash
```
; dirty git/hg →
```
commit_hash + ":" + content_hash
```
; multi-vcs →
```
revision + ":" + content_hash
```
; no-VCS / unknown copyable tree →
```
"content:" + content_hash
```
. The embedded content hash is exactly what lets an unchanged dirty or no-VCS tree MATCH across passes and still receive verification + dedup — and what makes a
```
repo sync
```
that advances commits under an unchanged manifest
```
revision
```
compare unequal. Never trust a bare branch name or manifest revision string as an identity.
Pass the three roots to EVERY stage. Include the findings-only stages (report, calibrate, reflect): they do not read target code, but they still read
```
active_snapshot
```
for provenance/annotation. When the harness archives and increments, retried findings must keep their original
```
discovery_commit
```
.

Conformance scenarios

The scenarios below expose nearly every issue in the snapshot model. They are reference checks, not features: the harness is responsible for preventing or handling each one in its own environment. The table is a quick-reference; prose detail follows for each scenario. The State column uses the 3-STATE RULE (MODE-OFF / HALT / PINNED, branched on

active_snapshot

presence — see the global backward-compat rule in schema.json and the advisory notes above);

SNAPSHOT_ID

formats follow the ladder in the advisory notes above (e.g.

live:<ts>

signals an unpinned/HALT pass).

Invariant legend (the labels below name safety properties enforced by the blocks and the global backward-compat rule in schema.json):

Label	Property	Enforced by
INV-1	No false `VERIFIED_SECURE`	Block G + HALT ceiling
INV-2	No false `failed_to_reproduce`	Block F + HALT ceiling
INV-3	No dropped regression	Block B NOT_MATCHED + POSSIBLE REGRESSION
INV-4	Within-pass consistency	Block A sentinel + single pinned snapshot
INV-5	No user data loss	Block C non-destructive sync + Block A step 4
INV-6	Fail-safe on missing data	Global backward-compat rule

Quick-reference table:


active_snapshot.root
active_snapshot.{root, snapshot_id, snapshot_pinned, pass}
active_snapshot.{snapshot_id, snapshot_pinned}
target_root
discovery_commit
repro_status

#	Scenario	State	Harness behavior	Stage behavior	Block / INV	Key fields
1	Colocated state	PINNED	HALT-and-yield (safe default), or relocate `state_root` outside `CODE_ROOT` when explicitly authorized (e.g. `--auto_relocate_state` ); `SNAPSHOT_ROOT` path must not contain `/workspace/`	`mantis-patch` state-vs-code guard misfires; Block A step 3 confuses SNAPSHOT- vs STATE-relative paths	A:3, D:3; INV-5	`active_snapshot.root` , `snapshot_root` , `state_root`
2	Stale active_snapshot ( `active_snapshot.pass != state.pass_number` )	PINNED → STOP or HALT-degrade	Block D step 0: handles same-pass re-entry only; if dir missing → STOP, yield to user	Block A step 2 sentinel may still MATCH (dir retained); CURRENT-PASS CHECK ( `active_snapshot.pass == state.pass_number` ) required: mismatch → STOP or HALT-degrade (Block B NOT_MATCHED, no authoritative verdicts)	A:2, D:0, B; INV-1, INV-3, INV-4, INV-6	`active_snapshot.{root, snapshot_id, snapshot_pinned, pass}` , `state.pass_number` , `discovery_commit`
3	Pin failure	HALT	Block D step 2/4: skip copy on ENOSPC/error → step 5b; still write `active_snapshot` + pass roots	Authoritative verdicts forbidden; Block B always NOT_MATCHED; reproduce `not_attempted` ; patch `VERIFICATION_INCOMPLETE`	D:2, D:4, D:5b; INV-1, INV-2, INV-6	`active_snapshot.{snapshot_id, snapshot_pinned}`
4	Patched shadows	PINNED (pass); `--snapshot_pinned=false` arg	Pass `--target_root=<PATCHED_SHADOW_ROOT>` + `--snapshot_pinned=false` to reattack sub-agent	Block A step 1a: `CODE_ROOT=--target_root` (authoritative); step 2 sentinel SKIPPED (sentinel-EXEMPT)	A:1a, A:2; INV-4	`target_root` , `snapshot_pinned` (arg), `snapshot_root` , `discovery_commit`
5	Different-snapshot duplicate candidates	PINNED	No special action — both passes pinned correctly; dedupe handles it	Block B pairwise: `discovery_commit` differs → NOT_MATCHED → keep ACTIVE + `possible_duplicate_of` ; POSSIBLE REGRESSION if archived was RESOLVED	B; INV-3, INV-6	`discovery_commit` , `possible_duplicate_of` , `status` , `patch_status`
6	Absent sink evidence	Any	No special action — Block F is a stage-level mechanical gate	Block F: evidence absent (build error, exit 127, sink unreached) → `not_attempted` (retry-eligible), NEVER `failed_to_reproduce` ; HALT ceiling additionally forces `not_attempted`	F; INV-2, INV-6	`repro_status` , `reattack_status` , `repro_hints`

Per-scenario detail:

1. Colocated state (

state_root

nested inside

CODE_ROOT

/ snapshot root) — The pinned

SNAPSHOT_ROOT

must live under

<state_root>/.mantis_snapshots/pass_<N>

(or a clean-VCS worktree/archive), and its path must not contain the segment

/workspace/

— otherwise

mantis-patch

's state-vs-code path guard misfires (state files appear to be "under

CODE_ROOT

"). If

state_root

itself is inside

CODE_ROOT

, the harness must HALT-and-yield (safe default) or, when explicitly authorized (e.g.

--auto_relocate_state

), relocate it outside the snapshot before pinning. Block A step 3 distinguishes SNAPSHOT-RELATIVE path fields (read under

CODE_ROOT

) from STATE-RELATIVE fields (read under

state_root/workspace

, never prefixed with

CODE_ROOT

); colocation breaks this separation.

2. Stale active_snapshot (

active_snapshot.pass != state.pass_number

—

active_snapshot

was preserved across the Stage 15 pass increment) — Block D step 0 (crash-resume) handles only the SAME-pass re-entry case (

active_snapshot.pass == N

→ reuse). It does NOT catch a stale snapshot carried across the Stage 15 pass increment, because Stage 15 deliberately preserves

active_snapshot

while bumping

pass_number

(see Stage 15). Two sub-cases:

(a) The prior snapshot dir is now MISSING: Block D step 0 STOPs and yields to the user (never re-pin to a possibly-drifted live tree). (b) The prior snapshot dir still EXISTS (default keep-2 retention) and its sentinel matches the preserved

active_snapshot.snapshot_id

: Block A step 2 sentinel check SUCCEEDS (it only compares the sentinel file to

SNAPSHOT_ID

, not to the current pass). Block B's pairwise

discovery_commit

check would MATCH a carried-forward finding against a new finding stamped with the same stale

SNAPSHOT_ID

, silently dropping it as

DUPLICATE

— a false authoritative verdict.

To prevent (b), the HARNESS MUST guarantee that

active_snapshot.pass == state.pass_number

before any consumer stage reads it. The reference harness (

mantis-meta-agent

) satisfies this by re-pinning every pass (Block D step 0 sees

active_snapshot.pass != N

→ re-pins → refreshes

active_snapshot.pass

before any stage runs), so sub-case (b) never fires there. A custom harness that preserves

active_snapshot

across the Stage 15 pass increment WITHOUT re-pinning MUST either (a) re-pin every pass (the reference behavior), or (b) inject an equivalent pre-stage gate that refreshes

active_snapshot.pass

or clears

active_snapshot

entirely before invoking stages. Stages CANNOT self-detect this staleness via Block B (which is

snapshot_id

-only, not

pass

-aware): a carried-forward finding and a new finding stamped with the same stale

SNAPSHOT_ID

will MATCH in Block B despite the snapshot being stale. The

active_snapshot.pass

field is defined in

schema.json

#/$defs/state/active_snapshot/pass

for exactly this check. The harness's Block D step 0 reuse check is NOT a substitute: it only fires on same-pass re-entry. (Stages that read

active_snapshot

MAY additionally self-check defensively — see each stage's Step 0 sentinel check — but the binding guarantee is on the harness.)

3. Pin failure (snapshot copy fails — disk full, permissions, too-large tree) — Block D step 2 (free-space precheck): compare

du -s

of the live tree to

df

free space at

state_root

; if it won't fit → skip copy → step 5b. Block D step 4 (failure-tolerant verify): check copy exit status + sanity check (file count/size within ~90%); on failure → step 5b (unpinned/HALT). Step 5b:

SNAPSHOT_ROOT=<live root>

snapshot_pinned=false

SNAPSHOT_ID="live:"+ISO8601

. The harness still writes

active_snapshot

and still passes

--snapshot_root

--snapshot_id

to stages so they see the HALT signal. Every stage then degrades conservatively: authoritative verdicts forbidden (

VERIFIED_SECURE

failed_to_reproduce

DUPLICATE

FALSE_POSITIVE

NON_VIABLE

); Block B always returns NOT_MATCHED; reproduce records

not_attempted

; patch's best attainable is

VERIFICATION_INCOMPLETE

4. Patched shadows (

--target_root

pointing at a pre-mutated tree; sentinel-exempt path 1a in Block A) —

mantis-patch

passes

--target_root=<PATCHED_SHADOW_ROOT>

and

--snapshot_pinned=false

to the reproduce sub-agent for re-attack verification. Block A step 1a:

CODE_ROOT = --target_root

(authoritative override, overrides

--snapshot_root

and state fallback). Block A step 2: sentinel check SKIPPED (a

--target_root

tree is deliberately mutated and is sentinel-EXEMPT). The

--snapshot_pinned=false

argument is the sentinel-exemption, NOT a HALT signal — detect HALT by reading STATE (

active_snapshot.snapshot_id

starts with

live:

, equivalently

active_snapshot.snapshot_pinned

false

in state), never from the argument passed on this invocation. The finding's

discovery_commit

is unaffected — it retains the pass-level

SNAPSHOT_ID

from when it was discovered; only the

--snapshot_pinned=false

argument is local to the reattack invocation.

5. Different-snapshot duplicate candidates (cross-pass dedupe where

discovery_commit

differs — the pairwise Block B NOT_MATCHED path) — Both passes pinned correctly; the findings simply come from different snapshots.

mantis-dedupe

Block B pairwise check compares the CURRENT finding's

discovery_commit

against the ARCHIVED finding's

discovery_commit

(NOT against the global

SNAPSHOT_ID

). If they differ → NOT_MATCHED. NOT_MATCHED keeps the current finding ACTIVE and sets

possible_duplicate_of

(a soft, non-terminal hint — the finding is NOT filtered or trashed). If the archived finding was RESOLVED (

patch_status

in {

VERIFIED_SECURE

MITIGATION_PROPOSED

} OR

status

FALSE_POSITIVE

production_viability

NON_VIABLE

) AND the pair is NOT_MATCHED → POSSIBLE REGRESSION: keep ACTIVE, add a history note, never filter (a reverted fix re-discovered on new code must never be trashed).

6. Absent sink evidence (Block F — PoC compiles but produces no reached-sink evidence;

not_attempted

failed_to_reproduce

) —

mantis-reproduce

Block F: if EVIDENCE is ABSENT (any compiler/build nonzero exit, exit 127 command-not-found, exit 2 "No such file", or the sink was never reached) →

repro_status = not_attempted

(retry-eligible), STOP. NEVER

failed_to_reproduce

. In

--reattack

mode: leave

reattack_status

UNSET with a history note "setup_failed" — NEVER

failed_to_bypass

failed_to_reproduce

is reserved for when the harness PROVABLY reached the vulnerable entrypoint — i.e. reached-sink evidence, not setup evidence — but the bug did not fire. Reached-sink evidence must originate INSIDE the invoked path or from target-produced tracing/backtraces: (a) a PoC script/source harness writes

MANTIS_REACHED_ENTRYPOINT

to a sidecar file at the point just before the sink call, within its own execution flow (the marker write is part of the invoked path, not a pre-launch step); OR (b) for binary/firmware/raw-payload targets, the captured crash backtrace or sanitizer trace (ASan/UBSan/MSan/TSan) explicitly names the target sink function (target-produced tracing). A marker written by an external wrapper BEFORE invoking the target is SETUP EVIDENCE ONLY (proves "launch attempted," not "sink reached") and does NOT by itself justify

failed_to_reproduce

— treat it as EVIDENCE ABSENT for the decision gate. Evidence is recorded in

repro_hints

. In HALT mode, the HALT ceiling additionally forces

not_attempted

(no

failed_to_reproduce

), since a negative result on an unpinned tree cannot be trusted as authoritative.

6. Semantic Retrieval (RAG) for Large Codebases

For small repositories, the planner can manually scan

workspace/kb/index.md

and the researcher can grep for call-sites. At scale (thousands of files, deep directory trees, multi-pass campaigns), these approaches miss relevant context and waste tokens reading irrelevant files. A semantic retrieval layer lets the planner and researcher query for relevant KB entries and code locations without reading everything.

Two implementations are supported, sharing the same data contract:

Option A (Default — Skill-Based): A dedicated skill that runs a BM25/TF-IDF helper script over
```
chunks.jsonl
```
. Zero external dependencies — works air-gapped, no vector embeddings or vector store required. Optional vector embedding support if available.
Option B (Maximum Scale — MCP-Based): The harness owns a persistent vector index using vector embeddings, serving persistent
```
semantic_search_kb
```
/
```
semantic_search_code
```
MCP tools. Better for very large codebases where per-invocation BM25 is too slow.

Both are opt-in. The existing skills are not modified; the planner and researcher receive runtime instructions to use whichever retrieval mechanism is available, falling back to today's manual behavior if neither is present. Retrieval results are coverage HINTs only — they decide ordering and prioritization, never the membership of the audit set. A miss must never cause a file, call-site, or investigation to be skipped or dropped.

A. Shared Data Contract:

chunks.jsonl

After Stage 2 (

/mantis-architecture

) completes, chunks are extracted into

workspace/kb/chunks.jsonl

(one JSON object per line). The harness can do this post-hoc by reading

workspace/kb/*.md

, or the architecture skill can be instructed to write it during synthesis as a text-only side effect. Two chunk types are produced:

KB chunks from the existing

workspace/kb/*.md

files:

json

{"id": "auth_module:0", "source_file": "workspace/kb/entities/auth_module.md", "entity_type": "entity", "chunk_text": "The auth module handles..."}

Code chunks from
```
CODE_ROOT
```
(the pinned snapshot). Each chunk includes the file path and line range so the researcher can request specific files from the snapshot:
json
```
{"id": "src/parser.c:0", "source_file": "src/parser.c", "start_line": 1, "end_line": 80, "chunk_text": "int parse_input(..."}
```

The first line of

chunks.jsonl

is a provenance header recording the

SNAPSHOT_ID

the chunks were built against:

json

{"_provenance": true, "snapshot_id": "abc123", "kb_snapshot_id": "abc123"}

Before serving queries, check

snapshot_id

in the provenance header against the current

SNAPSHOT_ID

; rebuild if they differ. In MODE-OFF (no

active_snapshot

kb_snapshot_id

is never stamped — skip the index entirely and let skills fall back to manual scanning. Never build code chunks from the live tree — they must reflect the pinned copy the skills are reading.

B. Option A: Skill-Based Retrieval (Default — No Infrastructure)

A dedicated skill reads

chunks.jsonl

and writes+runs a helper script (e.g.

workspace/helpers/search_chunks.py

) that performs BM25/TF-IDF similarity search. The script is generated by the agent at runtime — no code is shipped with the skill (same pattern as

mantis-dedupe

merge_findings.py

). This requires zero external dependencies — no embedding model, no vector store, no MCP server. It works in air-gapped and VPC-SC environments.

A complete reference blueprint for this skill is available at references/mantis-kb-query.md. Builders can adapt it to their environment. The blueprint includes Block A (Locator Resolution), chunk provenance checking, the versioned helper script contract (

MANTIS_HELPER_VERSION = 1

), and the JSON output schema.

Invocation: The planner or researcher spawns the skill as a sub-agent with a query string. The skill writes the helper if not already present, runs it, and returns top-K matching chunks as JSON.
Optional embeddings: If vector embeddings are available, the agent can be instructed to use cosine similarity instead of BM25. This is a runtime configuration toggle, not a different skill.
Snapshot safety: The skill reads
```
active_snapshot
```
from state via Block A (same as every other skill) and checks chunk provenance before serving.

C. Option B: MCP-Based Retrieval (For Maximum Scale)

For very large codebases where per-invocation BM25 is too slow, the harness can own a persistent vector index using vector embeddings, serving two MCP tools (following the same pattern as Guideline 2's Custom MCP for VMs/hardware):

semantic_search_kb(query: string) → [{id, source_file, entity_type, chunk_text, score}]

— Searches KB chunks. Returns relevant entity/vulnerability markdown context.

semantic_search_code(query: string) → [{file, start_line, end_line, snippet, score}]

— Searches code chunks from the pinned snapshot. Returns relevant code locations.

The harness manages the vector index lifecycle: build from

chunks.jsonl

(or directly from

CODE_ROOT

), rebuild when

SNAPSHOT_ID

changes, and handle freshness checks. In HALT mode, serve with a

STALE

flag or refuse. In MODE-OFF, skip entirely.

D. Per-Skill Augmentation Guidance

When a retrieval mechanism (skill or MCP) is available, instruct the following skills to use it. These are runtime instructions passed by the harness or meta-agent when invoking the skill — the skill files themselves are not modified:

mantis-architecture: No changes needed. The harness chunks the existing
```
workspace/kb/*.md
```
files after the architect completes Stage 2. If the builder prefers, they may instruct the architect to also write
```
workspace/kb/chunks.jsonl
```
during synthesis (Step 3) as a text-only side effect — but this is optional, since the harness can extract chunks post-hoc.
mantis-plan: If a retrieval mechanism is available, instruct the planner to use it to discover
```
kb_references
```
for each investigation instead of only manually scanning
```
workspace/kb/index.md
```
. For each investigation, query with the investigation title and target file names, then add the top-K matching KB entity/vulnerability files to the
```
kb_references
```
array. Manual scanning of
```
index.md
```
remains the fallback when no mechanism is available.
mantis-researcher: If a retrieval mechanism is available, instruct Wave 1 sub-agents to use it to PRIORITIZE relevant call-sites and cross-module data flows into sinks (e.g., "where does untrusted input reach
```
memcpy
```
in the parser module"). Semantic search SUPPLEMENTS grep as a ranking HINT ONLY — it decides ORDER, never MEMBERSHIP of the audit set. It MUST NEVER replace the exhaustive Step-3 call-site sweep; every call-site or data-flow that a full grep would reach must still be audited whether or not it ranks in top-K. Audit the union of grep results and semantic search results. The researcher's existing Wave 1/Wave 2 structure is unchanged.

E. Snapshot Safety

The retrieval index — whether served by the skill or the harness — is a cache of the pinned snapshot, never a live view:

Build code chunks from
```
CODE_ROOT
```
(the pinned snapshot), not the live tree.
Rebuild when
```
SNAPSHOT_ID
```
changes (new pass, new pin).
In HALT mode (
```
snapshot_pinned=false
```
), serve results with a
```
STALE
```
flag or refuse to serve — same conservative degradation as every other stage.
In MODE-OFF, skip the index entirely.

7. Embedding-Based Deduplication Pre-Filtering

When the pipeline runs many passes over a large codebase, the deduplicator (

/mantis-dedupe

) must compare each current finding against every archived finding — an O(n×m) comparison performed by an LLM reading summaries. At scale (hundreds of findings across many passes), this is token-expensive and slow.

The harness can use embeddings as a fast pre-filter to reduce the candidate space before invoking

/mantis-dedupe

. The skill's existing deterministic matching (

code_paths

title

discovery_commit

) remains the sole authority for hard dedup decisions.

This is entirely harness-side and opt-in.

/mantis-dedupe

is not modified.

A. How It Works

Harness Action: Reads all
```
workspace/findings/*.json
```
(current) and
```
workspace/archive/findings_pass_*/*.json
```
(archived). For each finding, computes an embedding from a normalized text representation (e.g.,
```
title + description + first code_paths entry with line stripped
```
).
Harness Action: Computes pairwise cosine similarity between current and archived findings. Surfaces candidate pairs above a configurable threshold (e.g., 0.85).
Harness Action: Writes a candidate-pairs manifest (e.g.,
```
workspace/helpers/dedup_candidates.json
```
) containing the UUID pairs and similarity scores.
mantis-dedupe invocation: The harness invokes
```
/mantis-dedupe
```
as usual. If the candidate manifest exists, instruct the skill to read it and prioritize those pairs for the LLM's pairwise comparison, instead of comparing every finding against every archived finding. All existing deterministic matching rules apply unchanged — the manifest only narrows the search space.

B. Safety Guardrails

Pre-filter only, never the decision. The embedding similarity score can never cause a
```
DUPLICATE
```
verdict, a trash move, or a
```
possible_duplicate_of
```
assignment. Only the skill's existing
```
code_paths
```
,
```
title
```
, and
```
discovery_commit
```
checks can do that. A miss in the pre-filter only over-retains a duplicate (safe — the LLM sees it and skips it); it never under-retains (never drops a real duplicate).
No false negatives. The threshold should be set low enough (e.g., 0.75) to avoid missing true duplicates. Better to surface too many candidates than to miss a real one — the LLM and deterministic matching will filter false positives.
Fallback on failure. If the embedding computation is unavailable, the candidate manifest is absent, or any error occurs,
```
/mantis-dedupe
```
falls back to its existing O(n×m) comparison. The skill must not stop or error if the manifest is missing.
Snapshot awareness. The harness should not compute embeddings across different
```
discovery_commit
```
values without flagging them as cross-snapshot candidates — the skill's Block B pairwise check will handle the final MATCHED/NOT_MATCHED decision.

C. Shared Infrastructure

If the builder also implements Guideline 6 (Semantic Retrieval), reuse the same vector embedding infrastructure for finding embeddings. The finding embedding is a different payload (finding JSON, not KB chunks) but the same embedding capability can serve both.

8. SAST Seeding (External Tool Ingestion)

Mantis's discovery engine is 100% LLM-generative (grep swarm + reasoning). A weak LLM can structurally under-detect whole-program taint classes (injection, path traversal, deserialization, UAF, format-string) that mature SAST tools (CodeQL, Semgrep-taint) encode as interprocedural queries. A SAST seeding adapter ingests external tool findings as

PROVISIONALLY_VALID

NEEDS_RESEARCH

candidates that must earn their verdict through the unchanged downstream gates. This is purely additive (INV-2-strengthening) — it expands detection breadth without weakening any verification gate.

This follows exactly the RAG pattern from Guideline 6: opt-in, default off, provenance-tracked, snapshot-aware, fallback on failure.

Platform-agnostic IR (not SARIF): Rather than tying the adapter to SARIF (a complex, tool-specific format), the adapter consumes a minimal JSONL intermediate representation (IR). Any SAST tool's output (SARIF, Semgrep JSON, Bandit JSON, etc.) is converted to this IR by a thin wrapper. This maximizes platform agnosticism — the adapter works with any tool that can produce the simple JSONL format.

Two implementations are supported, sharing the same data contract:

Option A (Default — Skill-Based): A dedicated skill (
```
/mantis-sast-seed
```
) reads the IR and uses LLM reasoning to normalize findings into mantis finding JSONs. The LLM reads actual source code at each reported location under CODE_ROOT to verify the finding and enrich the description with root-cause analysis. Zero external dependencies — works air-gapped.
Option B (Maximum Control — Harness-Based): The harness directly normalizes SAST output into finding JSONs using deterministic code (e.g., a SARIF-to-finding converter script), bypassing the LLM for the normalization step. The harness writes finding JSONs to
```
workspace/findings/
```
before invoking
```
/mantis-dedupe
```
.

Both are opt-in. The existing skills are not modified. Seeded findings enter

workspace/findings/

alongside researcher findings and flow through the unchanged downstream gates (dedupe -> review -> critic -> reproduce -> patch -> calibrate).

A complete reference blueprint is available at references/mantis-sast-seed.md.

A. Shared Data Contract:

sast_findings.jsonl

The IR is a JSONL file at

workspace/sast_findings.jsonl

(STATE-RELATIVE). It follows the same provenance-header pattern as

chunks.jsonl

(Guideline 6A).

Line 1 — Provenance header:

json

{"_provenance": true, "scan_snapshot_id": "abc123def456", "tool": "codeql", "tool_version": "2.15.0", "scan_timestamp": "2026-07-22T10:00:00Z"}

```
scan_snapshot_id
```
: The SNAPSHOT_ID the scan was run against. This is the primary provenance anchor — compared byte-for-byte to the current pass
```
SNAPSHOT_ID
```
(same comparison as Block B). If the harness ran the SAST tool against the pinned CODE_ROOT, it sets this to
```
SNAPSHOT_ID
```
.
```
tool
```
: Tool name (e.g.,
```
codeql
```
,
```
semgrep
```
,
```
bandit
```
). Informational.
```
tool_version
```
: Tool version. Informational.
```
scan_timestamp
```
: ISO 8601. Informational.

Lines 2+ — One finding per line:

json

{"rule_id": "cpp/sql-injection", "rule_name": "SQL injection", "cwe": "CWE-89", "severity": "HIGH", "code_paths": ["src/db/query.c:42"], "message": "User input flows into SQL query without sanitization"}

Field	Type	Required	Description
`rule_id`	string	Yes	Tool-specific rule identifier
`severity`	string	Yes	`CRITICAL` , `HIGH` , `MEDIUM` , `LOW` (Mantis scale)
`code_paths`	array	Yes	Array of `"file:line"` strings (SNAPSHOT-RELATIVE)
`message`	string	Yes	Original SAST finding message
`rule_name`	string	No	Human-readable rule name
`cwe`	string	No	CWE identifier (e.g., `CWE-89` )

IR conversion (harness responsibility): The harness converts SAST tool output to this IR before invoking the adapter. Conversion examples:

SARIF: extract

ruleId

, map

level

to severity (

error

HIGH

warning

MEDIUM

note

LOW

), extract

locations

code_paths

, copy

message.text

Semgrep JSON: extract

check_id

rule_id

, map

extra.severity

to Mantis severity, extract

path:start.line

code_paths

, copy

extra.message

B. Option A: Skill-Based Ingestion (Default — No Infrastructure)

A dedicated skill reads

sast_findings.jsonl

, applies allow-listing, verifies provenance, computes

signature

lineage_id

discovery_commit

, and writes finding JSONs. The LLM reads actual source code at each reported location under CODE_ROOT to verify the finding and enrich the description.

Invocation: The harness invokes
```
/mantis-sast-seed
```
with
```
--snapshot_root
```
/
```
--snapshot_id
```
/
```
--state_root
```
after Stage 6 (Research) and before Stage 7 (Dedupe). The seeded findings land in
```
workspace/findings/
```
before
```
/mantis-dedupe
```
runs.
Inert until wired: If
```
sast_findings.jsonl
```
is absent, the skill outputs nothing and notifies the caller. It never fails — it simply returns empty.
Anti-hallucination: The LLM MUST read the actual code at each reported location before writing the finding JSON. It MUST NOT invent findings not present in the SAST output. The
```
sast_provenance.line_verified
```
field records whether verification succeeded.
Snapshot safety: The skill reads
```
active_snapshot
```
from state via Block A and stamps
```
discovery_commit
```
only when the snapshot is pinned and the finding's location is verified under CODE_ROOT.

C. Option B: Harness-Based Ingestion (For Deterministic Ingestion)

For maximum determinism, the harness can normalize SAST output into finding JSONs using deterministic code. This bypasses the LLM for the normalization step:

Harness Action: Reads the SAST tool output, parses it deterministically, and writes finding JSONs to
```
workspace/findings/
```
.
Harness Action: Stamps
```
discovery_commit
```
only if the scan provably ran against the pinned CODE_ROOT.
Harness Action: Applies allow-listing filters.
mantis-dedupe invocation: Proceeds as usual — the seeded findings are indistinguishable from researcher findings.

D. Provenance Verification

The adapter stamps

discovery_commit

ONLY if the scan provably ran against a line-identical pinned CODE_ROOT. Verification ladder:

Read
```
active_snapshot
```
from
```
workspace/.mantis_state.json
```
(via Block A). If absent, this is MODE-OFF — skip to step 5.
If
```
snapshot_pinned
```
is false, this is HALT — skip to step 6.
Read the IR provenance header's
```
scan_snapshot_id
```
.

VERIFIED:

scan_snapshot_id

present AND exactly equals

SNAPSHOT_ID

. Stamp

discovery_commit = SNAPSHOT_ID

. Status =

PROVISIONALLY_VALID

MODE-OFF:
```
active_snapshot
```
absent. OMIT
```
discovery_commit
```
. Status =
```
PROVISIONALLY_VALID
```
(MODE-OFF permits all verdicts).

HALT:

snapshot_pinned

is false. OMIT

discovery_commit

. Status =

NEEDS_RESEARCH

DRIFT:
```
scan_snapshot_id
```
present but differs. OMIT
```
discovery_commit
```
entirely. Status =
```
NEEDS_RESEARCH
```
.

UNVERIFIED:

scan_snapshot_id

absent. OMIT

discovery_commit

. Status =

NEEDS_RESEARCH

Line-existence verification (additional check when VERIFIED or DRIFT and CODE_ROOT is resolved): For each finding, verify each

code_paths

entry: strip trailing

:line

, check file exists under CODE_ROOT, check line number is within file's line count. If file missing or line out of range -> downgrade to DRIFT (omit

discovery_commit

, status =

NEEDS_RESEARCH

This is the exact same "UNTRUSTED-IF-ABSENT" pattern as

discovery_commit

(schema.json) and

signature

lineage_id

E. Allow-Listing and Noise Control

workspace/sast_allowlist.json

(optional config file, STATE-RELATIVE):

json

{
  "enabled": true,
  "severity_filter": ["CRITICAL", "HIGH"],
  "cwe_allowlist": {
    "enabled": true,
    "cwes": ["CWE-89", "CWE-78", "CWE-79", "CWE-22", "CWE-787", "CWE-416", "CWE-502"]
  },
  "rule_allowlist": {
    "enabled": false,
    "rules": []
  },
  "per_rule_cap": 5,
  "total_cap": 50
}

If the config file is absent, defaults apply: CRITICAL+HIGH only,

per_rule_cap=5

total_cap=50

How allow-listing protects the retry cap: The reproduce stage (

/mantis-reproduce

) has a hard ceiling of 6 attempts per finding (absolute, never reset). If 1000 SAST findings are seeded without filtering, the reproduce stage would need up to 6000 attempts — starving the retry budget. The allow-listing chain (severity filter -> CWE/rule filters -> per-rule cap -> total cap) ensures only a bounded, high-signal set of candidates enters the pipeline. The review (13-rule negative filter) and critic (production viability) stages further filter before reproduce runs.

F. Per-Skill Augmentation Guidance

When the SAST seed skill is available, instruct the harness to invoke it between Stage 6 (Research) and Stage 7 (Dedupe). These are runtime instructions passed by the harness — the skill files themselves are not modified:

mantis-meta-agent: Invoke

/mantis-sast-seed

with

--snapshot_root

--snapshot_id

--state_root

after

/mantis-researcher

completes and before

/mantis-dedupe

mantis-dedupe: No changes needed. Seeded findings are in
```
workspace/findings/
```
alongside researcher findings. Dedupe processes them identically (signature-based matching, Block B pairwise check).
mantis-review: No changes needed. The 13-rule negative filter applies to seeded findings identically.
mantis-report: No changes needed. The
```
sast_provenance
```
field is informational and can be displayed in reports.

G. Snapshot Safety

The SAST seed adapter follows Block A (Locator Resolution) exactly like every other code-reading skill:

Resolve CODE_ROOT from
```
--snapshot_root
```
/ state
```
active_snapshot
```
.
Honor the sentinel check (Block A step 2).
Read source files under CODE_ROOT (SNAPSHOT-RELATIVE) for line-existence verification.
Write findings under
```
state_root/workspace/findings/
```
(STATE-RELATIVE).
Never write under CODE_ROOT when pinned.
In MODE-OFF: proceed without
```
discovery_commit
```
. All verdicts permitted.
In HALT: omit
```
discovery_commit
```
. Seeded findings get
```
NEEDS_RESEARCH
```
.
In PINNED: verify each finding's location under CODE_ROOT, stamp
```
discovery_commit = SNAPSHOT_ID
```
if verified.

The SAST tool output itself is NOT snapshot-aware — it may have been produced against a different tree. The adapter's provenance verification is what bridges the gap: it re-grounds each finding against the pinned CODE_ROOT before stamping

discovery_commit

H. Safety Guardrails

Purely additive. Seeded findings are
```
PROVISIONALLY_VALID
```
/
```
NEEDS_RESEARCH
```
candidates. They must pass through the unchanged downstream gates: dedupe (Block B), review (13-rule filter), critic (viability), reproduce (Block F reached-sink evidence + HALT ceiling), patch (Block G re-attack), calibrate (sanity caps). No gate is weakened.
No false
VERIFIED_SECURE
. Seeded findings start at
```
PROVISIONALLY_VALID
```
— they can never reach
```
VERIFIED_SECURE
```
without passing through the full patch + re-attack pipeline.
No false
failed_to_reproduce
. Seeded findings that reach reproduce are subject to the same Block F evidence gate and HALT ceiling.
No dropped regression. If a SAST-seeded finding matches an archived finding with a different
```
discovery_commit
```
, Block B returns NOT_MATCHED ->
```
possible_duplicate_of
```
-> finding stays active.
Fail-safe on missing data. If
```
sast_findings.jsonl
```
is absent, no findings written. If a finding can't be verified,
```
NEEDS_RESEARCH
```
.
No existing skills modified. The adapter writes findings to
```
workspace/findings/
```
before
```
/mantis-dedupe
```
runs.
sast_provenance
is informational only. It does not affect any safety- critical invariant, gate, or verdict. The finding's
```
discovery_commit
```
is the field that governs Block B snapshot matching.

9. Structural Code Index (AST-Level Context)

For small repositories, the researcher can grep for call-sites and the planner can infer dependencies. At scale, grep-based call-site discovery is unreliable (misses indirect calls, cannot distinguish calls from comments/strings, no function boundary awareness). A structural code index provides AST-level context (function boundaries, call graphs, symbol tables) to improve LLM reasoning quality during discovery.

This follows the RAG pattern from Guideline 6: optional, provenance-tracked, snapshot-aware, fallback on failure. The structural index is a coverage HINT only — it decides ordering and prioritization, never the membership of the audit set. A miss must never cause a file, call-site, or investigation to be skipped or dropped.

The full specification — including the manifest schema, SQLite serving store, capability-based per-partition backend selection, canonical symbol IDs, query interface, baseline-plus-delta overlay, deterministic partial coverage, and safety guardrails — lives in a single source of truth:

→ ../mantis-structural-index/SKILL.md

A thin reference blueprint is at references/mantis-structural-index.md.

Two implementations are supported, sharing the same query contract:

Option A (Default — Skill-Based): The
```
mantis-structural-index
```
skill generates and runs helper scripts (
```
build_structural_index.py
```
and
```
query_structural_index.py
```
, both
```
# MANTIS_HELPER_VERSION = 5
```
) using capability-based per-partition backend selection, degrading to grep.
Option B (Maximum Power — MCP-Based): The harness owns a persistent structural index serving
```
find_callers(symbol)
```
,
```
find_callees(function)
```
,
```
get_function_boundary(file, line)
```
MCP tools, backed by the same SQLite serving store.

Both are optional. A non-conformant harness simply skips the structural index stage. The structural index supplements grep as a ranking HINT ONLY — it decides ORDER, never MEMBERSHIP of the audit set.

Consumers MUST use the query interface (

query_structural_index.py

) rather than filtering JSONL directly. The query interface provides bounded results, pagination, explicit name resolution, precision/backend metadata, and coverage on empty results.

D. Per-Skill Augmentation Guidance

When a structural index is available, instruct the following skills to use it. These are the consumption contract — runtime instructions passed by the harness. The structural index is a HINT-only enhancement; skills that do not use it behave exactly as they do today:

mantis-architecture: Optionally read the pre-built structural index (built by
```
mantis-structural-index
```
at Stage 0.5) during KB synthesis, cross-referencing it with
```
dependencies.json
```
.
mantis-plan: Use the structural index for function-level dependency fan-out (more precise than file-level
```
dependencies.json
```
).
mantis-researcher: Wave 1 sub-agents use
```
find_callers()
```
to SUPPLEMENT grep as a ranking HINT for call-site discovery — it decides ORDER, never MEMBERSHIP. It MUST NEVER replace the exhaustive Step-3 call-site sweep; every call-site that a full grep would reach must still be audited whether or not it ranks in the structural index. Audit the union of grep results and structural index results. Wave 2 deep auditors use
```
get_function_boundary()
```
to start with the enclosing function, expanding to callers/callees/file as needed for cross-function context. The researcher's existing Wave 1/Wave 2 structure is unchanged.

E. Integration with RAG (Guideline 6)

Structural index entries can be added to
```
chunks.jsonl
```
as
```
entity_type: "structural"
```
chunks.
Code chunks can become function-boundary-aligned (instead of dumb line-range slices) by using function boundaries from the structural index.
The structural index and the RAG index can share the same vector embedding infrastructure if both are implemented.

10. Tiered Iterative Reproduction & Multi-Conversation Retry Strategy

For complex services, attempting a single-shot reproduction directly against a full sandboxed service often suffers from high search entropy, brittle configuration, and hard-to-debug failures. A tiered strategy breaks reproduction into incremental milestones, while an inter-conversation retry architecture prevents reasoning deadlocks and context bloat.

A. Orchestration & Inter-Conversation Retries

Intra-Conversation Retries (Local Agent Trajectory):
- The active subagent conversation retries 2–3 times locally within its context window to adjust parameters, fix setup bugs, or refine payloads.
Inter-Conversation Retries (Fresh Context + Accumulated Artifacts):
- Trigger: If intra-conversation retries fail to reach Tier 3 (
```
reproduced
```
  ), the orchestrator terminates the stalled conversation and launches a new subagent conversation (a fresh context window).
- Context Provisioning: The orchestrator populates the new prompt with structured attempt data from
```
state_root/workspace/archive/.repro_attempts.json
```
  and trajectory learnings from
```
workspace/learnings.jsonl
```
  (e.g., "Attempt 1 failed due to missing auth header X; Attempt 2 proved parser strips unescaped quotes").
- Benefit: Eliminates context bloat and reasoning inertia ("hallucination traps"), enabling a fresh agent to solve the problem using prior empirical observations without repeating past mistakes.
Attempt Cap Accounting & Arithmetic:
- The orchestrator maintains
```
state_root/workspace/archive/.repro_attempts.json
```
  .
- Tier 3 Increment Only: Only Tier-3 full sandboxed service executions (or full end-to-end reproducer runs) increment the per-finding attempt counter toward the absolute hard ceiling of 6.
- Stepping-Stone Sub-Budget: Internal Tier-1 and Tier-2 trial runs are bounded by a local sub-budget (max 3 trial executions per conversation) and do not consume the absolute 6-attempt cap.

B. Harness-Enforced Tier 4 (Staging & Live Pre-Production Execution)

To prevent un-gated exploit execution on live infrastructure, Tier 4 (Staging / Pre-Production verification) is strictly owned and enforced by the programmatic orchestrator harness, never by LLM discretion:

Sandbox Boundary:
```
mantis-reproduce
```
executes strictly within isolated local sandboxes (Tiers 1–3) ending at Tier 3 (
```
reproduced
```
/
```
failed_to_reproduce
```
).
Deterministic Gate: The harness intercepts a Tier 3
```
reproduced
```
verdict. If live/staging validation (Tier 4) is configured, the harness MUST NOT automatically invoke remote execution. It must enforce a programmatic Human-in-the-Loop gate:
- Prompt the human operator for explicit interactive confirmation, OR
- Require a cryptographically signed approval token / authorization callback.
Fail-Closed Default: If human approval is missing or denied, Tier 4 is skipped and the Tier 3 sandboxed verdict remains authoritative.

C. Local Ingress / Middlebox Edge Annotation (Post-Tier-3 Sandbox Verification)

To eliminate false-positive findings caused by default edge filters ("Works on localhost:8080, but dies at the WAF/proxy"), the orchestrator can optionally execute a Tier 3 PoC through a local reverse proxy or API gateway (e.g. NGINX, Envoy, ModSecurity) running inside the local sandbox:

Annotation Only: A payload blocked by a local middlebox MUST NOT downgrade a Tier 3
```
reproduced
```
verdict to
```
failed_to_reproduce
```
.
Critic Integration: The harness records
```
ingress_blocked: true
```
(or edge filter details) in the finding's
```
repro_hints
```
or history. This provides empirical evidence for
```
/mantis-critic
```
to classify
```
production_viability
```
as
```
"CONDITIONAL_VIABLE"
```
(mitigated by default edge proxy configuration).

mantis-pipeline-adapter

NPX Install

Tags

SKILL.md Content

Mantis Pipeline Designer (/mantis-pipeline-adapter)

System Goal

Command Definition

Input/Output Contract

Instructions

Reference Architecture Guidelines

Core Principles

Architectural Overview

1. UUID-Based Referencing Pattern

A. Researcher Stage

B. Deduplication Stage (Optimized)

C. Validation & Review Stages (Reviewer, Critic)

2. Adaptable Reproducers via Custom MCP

3. Decomposition & Multi-Model Strategy

A. Pipeline Decomposition & Concurrency

B. Heterogeneous LLM Selection (Multi-Model)

C. Importance of Evaluation

4. The Planning Stage and workspace/plan.json

5. The Pass Lifecycle Contract (Living / Synced Codebases)

Advisory notes when helping a builder implement this contract

Conformance scenarios

6. Semantic Retrieval (RAG) for Large Codebases

A. Shared Data Contract: chunks.jsonl

B. Option A: Skill-Based Retrieval (Default — No Infrastructure)

C. Option B: MCP-Based Retrieval (For Maximum Scale)

D. Per-Skill Augmentation Guidance

E. Snapshot Safety

7. Embedding-Based Deduplication Pre-Filtering

A. How It Works

B. Safety Guardrails

C. Shared Infrastructure

8. SAST Seeding (External Tool Ingestion)

A. Shared Data Contract: sast_findings.jsonl

B. Option A: Skill-Based Ingestion (Default — No Infrastructure)

C. Option B: Harness-Based Ingestion (For Deterministic Ingestion)

D. Provenance Verification

E. Allow-Listing and Noise Control

F. Per-Skill Augmentation Guidance

G. Snapshot Safety

H. Safety Guardrails

9. Structural Code Index (AST-Level Context)

D. Per-Skill Augmentation Guidance

E. Integration with RAG (Guideline 6)

10. Tiered Iterative Reproduction & Multi-Conversation Retry Strategy

A. Orchestration & Inter-Conversation Retries

B. Harness-Enforced Tier 4 (Staging & Live Pre-Production Execution)

C. Local Ingress / Middlebox Edge Annotation (Post-Tier-3 Sandbox Verification)

A. Shared Data Contract:
`chunks.jsonl`

A. Shared Data Contract:
`sast_findings.jsonl`