audit-context

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Translated

Diagnose the health of a nao context at any stage of its lifecycle. Use when the user wants a structured review of what's been synced, how RULES.md compares to the target structure, whether every table is documented, whether the data model is MECE, whether tests exist and what their failures reveal, and whether context files are bloated. Outputs a structured audit report with ranked recommendations. Do not use for first-time setup (setup-context) or routine rule writing (write-context-rules).

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Sourcegetnao/nao

Added on2026-05-01

NPX Install

npx skill4agent add getnao/nao audit-context

SKILL.md Content

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audit-context

Diagnose a nao context. Find gaps, MECE violations, failure root causes, and bloat. Output is a short in-conversation report ending in a prioritized plan. Diagnose only — never fix. Route fixes to

write-context-rules

/

add-semantic-layer

/

create-context-tests

.

Run any time: right after

setup-context

, mid-build, before a release, or when the agent's behavior gets surprising.

Six checks (run in order)

1. Synced context

Read

nao_config.yaml

. What's wired in (warehouse, repos, Notion, semantic layer, MCPs)? What's missing (dbt repo, ETL configs, BI repo, internal docs)? Has

nao sync

run — are

databases/

,

repos/

,

docs/

,

semantics/

populated?

Scope check: <100 tables is the hard ceiling, ≤20 is the target. Better 12 well-documented tables than 80 half-documented ones. Flag oversized scope explicitly — it's the biggest predictor of reliability failure.

2.

RULES.md

vs target structure

Six standard sections (from

write-context-rules

): Business overview, Data architecture, Core data models (Most Used + Tables detail), Key Metrics Reference, Date filtering, Analysis Process. Per section, mark present / missing / thin. Flag placeholders,

TODO:

markers, and metric entries with no source-of-truth pointer.

3. Context coverage (per table)

For every table in

databases/

: is it in

## Most Used Tables

? Does it have a

## Tables detail

block? Is there dbt context (

repos/<dbt>/models/**/schema.yml

)? Any extra

.md

?

Then per-table gaps: undocumented columns the agent will reference, calculated fields with no explanation, foreign keys with no relation, common WHERE filters not mentioned anywhere. A table with no docs anywhere is a high-priority finding.

4. Data model consistency (MECE)

Mutually exclusive? Two tables computing the same metric differently (worst issue — the agent picks one unpredictably).
Collectively exhaustive? Asked metrics that no in-scope table can answer.
Duplicated columns? Same logical field under different names (
```
user_id
```
/
```
customer_id
```
/
```
account_id
```
).
Ambiguous columns?
```
amount
```
without unit,
```
status
```
without enum values.

5. Test coverage

If

tests/

is empty → recommend

create-context-tests

. Otherwise read

tests/outputs/

(most recent run) and categorize each failure:

Category	Looks like	Fix
Data model	Wrong column / wrong table	Add column descriptions; clarify granularity
Date selection	Wrong period / week start	Add DO/DON'T SQL in `## Date filtering`
Test issue	Test SQL itself is wrong	Fix the test, not the context
Interpretation	Reasonable but different reading	Add to naming conventions or `## Key Metrics Reference`
Metric definition	Wrong formula / source	Tighten `## Key Metrics Reference` or add a semantic layer

Propose the smallest rule change per failure. Sort by impact (tests affected).

6. Token optimization

Files >40KB (flag).
```
## Tables detail
```
blocks exceeding the 10-column cap.
Duplication between
```
RULES.md
```
and
```
databases/<table>.md
```
.
In-scope tables with no mention in any test or recent question (trim candidates).
Raw / staging tables that snuck into scope.

If

RULES.md

is bloated, suggest moving per-table detail to

databases/<table>.md

and keeping only the one-line pointer in

## Most Used Tables

. For multi-domain bloat, propose a per-domain file map referenced from

RULES.md

. Show the proposed structure before moving anything.

Output (in conversation, not a file)

Lead with a one-paragraph summary: sync state | scope wideness (N tables vs ≤100 ceiling) | rules quality (N/6 sections substantive) | test coverage (N tests, X% passing).

Then deep-dive only the sections with findings. Skip clean ones. Format hints:

Synced / RULES.md / token bloat → bulleted gaps.

Context coverage → table:

Table | RULES.md | dbt docs | Extra .md | Gap

.

MECE → bullets.
Test failures → table:
```
Test | Category | Proposed fix
```
.

End with a prioritized plan (easiest-win → biggest-work), each item naming the skill that does the work:

## Plan
1. (easy / 5 min) ... → write-context-rules
2. (small / 30 min) ... → create-context-tests
3. (medium / 1-2 hr) ... → audit-context (rerun after)
4. (large / multi-session) ... → add-semantic-layer

Guardrails

Apply one change at a time. Re-run tests between fixes.
Tests are the source of truth. If the user says "it's working," ask for the latest pass rate first.
Don't move or split files without confirmation. Show the file map first.
Don't fix in this skill — diagnose only.

audit-context

NPX Install

Tags

SKILL.md Content

audit-context

Six checks (run in order)

1. Synced context

2.
`RULES.md`
vs target structure

3. Context coverage (per table)

4. Data model consistency (MECE)

5. Test coverage

6. Token optimization

Output (in conversation, not a file)

Guardrails