agentic-os

Original：🇺🇸 English

Translated

3 scripts

Framework-agnostic persistent memory and self-improvement loops for AI agents. Scaffolds shared state, task queues, and learnings files that can be read/written by Claude, Gemini, and Antigravity. Use this to initialize an Agentic OS layer in any workspace and instruct agents on how to use it.

4installs

Sourcebaphomet480/claude-skills

Added on2026-05-14

NPX Install

npx skill4agent add baphomet480/claude-skills agentic-os

SKILL.md Content

View Translation Comparison →

Agentic OS

This skill establishes a framework-agnostic Agentic Operating System layer in a project workspace. It enables Claude, Gemini, and Antigravity to share persistent operational memory, coordinate via task queues, and continuously improve through structured feedback loops.

The Core Concept

An Agentic OS solves the "blank slate" problem. Without it, every agent session starts fresh, requiring manual context loading and repeating past mistakes. By centralizing state and learnings into an

.agent/

directory, any agent runtime can pick up exactly where the last one left off and apply compounded knowledge.

Initialization

To set up the Agentic OS scaffolding in a new project, run:

bash

bash ~/.agents/skills/agentic-os/scripts/init-os.sh

This creates the following structure at the root of the project:

.agent/
  ├── state/
  │   ├── last-run.json      # Global log of the last agent actions
  │   ├── tasks.json         # Shared queue of pending/completed tasks
  │   ├── errors.json        # Log of unresolved task failures
  │   └── status.md          # Output of the command-center skill
  ├── learnings/             # Per-skill feedback loops
  │   └── template.json
  └── evals/                 # Per-skill evaluation criteria
      └── template.json

Agent Directives

Whenever you operate in a project that has an

.agent/

directory, you MUST adhere to the following workflow:

1. Pre-Task: Load State & Learnings

Before executing a specialized task or skill:

Read State: Check
```
.agent/state/last-run.json
```
to understand what was recently completed. Check
```
.agent/state/tasks.json
```
if you need to pull a task from the queue.
Read Learnings: If you are about to perform a specific skill (e.g.,
```
write-draft
```
), check if
```
.agent/learnings/write-draft.json
```
exists. If it does, read it and explicitly apply its
```
rule_change
```
entries to your approach.
Read Evals: Check if
```
.agent/evals/<skill>.json
```
exists to understand the definition of "done".

2. Execution & Evaluation (The Learnings Loop)

After generating your output but before finalizing:

If an

eval.json

exists for your task, grade your own output against its criteria. (Use

python3 ~/.agents/skills/agentic-os/scripts/eval.py list --skill <skill>

)

If you fail any criteria, revise your output.

Once you pass, log your verification using

python3 ~/.agents/skills/agentic-os/scripts/eval.py verify --skill <skill> --task-id <id> --notes "..."

Consider what worked well and what didn't during generation.

3. Post-Task: Write State & Learnings

Before you conclude your session or step:

Update Learnings: If you learned something new about how to do this task better in this specific project, use
```
python3 ~/.agents/skills/agentic-os/scripts/learn.py add --skill <skill> --worked "..." --didnt "..." --rule "..."
```
to record it.
Update State: Overwrite
```
.agent/state/last-run.json
```
with a summary of what you just did, what decisions you made, and what needs to happen next.
Update Queue: If you completed a task from
```
tasks.json
```
, mark its status as
```
completed
```
.

Data Schemas

last-run.json

Used for operational continuity.

json

{
  "timestamp": "2026-04-25T12:00:00Z",
  "task_id": "task-123",
  "status": "completed",
  "description": "Built the new Hero component.",
  "assigned_skill": "build-component",
  "project_id": "generic-service",
  "agent_id": "antigravity",
  "user_id": "matthias",
  "outcome": {
    "result": "Success"
  },
  "trace_id": "req-12345",
  "decision_log": "Used standard Tailwind utility classes instead of custom CSS for faster rendering"
}

errors.json

Used by the

command-center

skill to track unresolved failures.

json

[
  {
    "timestamp": "2026-04-25T12:05:00Z",
    "task_id": "task-124",
    "assigned_skill": "osint",
    "reason": "Target API returned 429 Too Many Requests after 3 retries",
    "trace_id": "req-12346",
    "decision_log": "Attempted to backoff but exceeded max wait time.",
    "resolved": false
  }
]

learnings.json

(per skill)

Used to permanently correct agent behavior without modifying the base skill prompt.

json

{
  "skill": "build-component",
  "history": [
    {
      "date": "2026-04-21",
      "what_worked": "Extracting the SVG into a separate file kept the component clean.",
      "what_didnt": "Trying to use Next.js Image for inline SVGs caused layout shifts.",
      "rule_change": "Always put complex SVGs in a separate .tsx file and import as a React component. Do not use next/image for vectors."
    }
  ]
}

eval.json

(per skill)

Used to define rigid quality gates.

json

{
  "skill": "build-component",
  "criteria": [
    "Component must be responsive down to 320px",
    "Must not use inline styles",
    "Must be exported as a default export"
  ]
}

agentic-os

NPX Install

Tags

SKILL.md Content

Agentic OS

The Core Concept

Initialization

Agent Directives

1. Pre-Task: Load State & Learnings

2. Execution & Evaluation (The Learnings Loop)

3. Post-Task: Write State & Learnings

Data Schemas

`last-run.json`

`errors.json`

`learnings.json`
(per skill)

`eval.json`
(per skill)