codebase-teach

Original：🇺🇸 English

Translated

Analyzes project bounded contexts, extracts business rules and domain knowledge, writes ai-context/features/<context>.md files, and produces a teach-report.md with documentation coverage metrics. Trigger: /codebase-teach, teach codebase, extract domain knowledge, update feature docs.

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Sourcefearovex/claude-config

Added on2026-04-18

NPX Install

npx skill4agent add fearovex/claude-config codebase-teach

SKILL.md Content

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codebase-teach

Analyzes bounded contexts from the project's source tree, extracts domain knowledge per context, writes
ai-context/features/<context>.md
files, and produces
teach-report.md
with coverage metrics.

Triggers:

/codebase-teach

, teach codebase, extract domain knowledge, update feature docs, analyze bounded contexts

Purpose

codebase-teach

fills the

ai-context/features/

layer with structured domain knowledge derived from reading source code. It is the deep-read complement to

memory-update

(which records session decisions) and

memory-init

(which scaffolds empty stubs). It MUST only be invoked manually by the user.

Process

Step 0 — Load project context (non-blocking)

This step is non-blocking: any failure (missing file, unreadable file) MUST produce at most an INFO-level note. This step MUST NOT produce

status: blocked

status: failed

Read
```
ai-context/stack.md
```
— tech stack, versions, key tools.
Read
```
ai-context/architecture.md
```
— architectural decisions and rationale.
Read
```
ai-context/conventions.md
```
— naming patterns, code conventions.
Read the project's
```
CLAUDE.md
```
(at project root) and extract the
```
## Skills Registry
```
section.

For each file:

If absent: log

INFO: [filename] not found — proceeding without it.

If present: extract

Last updated:

Last analyzed:

date. If date is older than 30 days: log

NOTE: [filename] last updated [date] — context may be stale. Consider running /memory-update or /project-analyze.

Step 1 — Scan bounded contexts

Identify bounded context candidates by scanning the project directory tree at depth ≤ 2 under these root directories (in order):

```
src/
```
— subdirectories at depth 1 (e.g.,
```
src/auth/
```
,
```
src/payments/
```
)
```
app/
```
— subdirectories at depth 1
```
features/
```
— subdirectories at depth 1
```
domain/
```
— subdirectories at depth 1
```
ai-context/features/
```
— existing feature file names (each file stem is treated as a context name)

Exclusion rules — skip directories named:

shared

utils

common

lib

types

hooks

components

__tests__

test

tests

node_modules

.git

Cross-reference with existing feature files: Read the

ai-context/features/

directory (if it exists) and list all

.md

files, excluding files whose names begin with

(e.g.,

_template.md

For each detected context candidate, build a record:

{ slug: kebab-case of directory name, dir_path, existing_feature_file: bool }

Slug convention: lowercase the directory name; replace spaces and underscores with hyphens. Example:

UserProfile

→

user-profile

auth_service

→

auth-service

If no context candidates are found:

Log:

"No bounded context directories detected."

Write
```
teach-report.md
```
with a Summary noting "No bounded context directories detected" and a recommendation to run
```
/memory-init
```
first.
Stop here.

If
ai-context/features/
does not exist:

Log:

"INFO: ai-context/features/ not found — feature files will be created if possible."

Note in
```
teach-report.md
```
: "ai-context/features/ was absent at run time. Recommend running /memory-init to scaffold the directory before re-running /codebase-teach."
Continue processing (the skill creates the directory and files as needed).

Output:

context_list = [{ slug, dir_path, existing_feature_file: bool }, ...]

Log each context found:

"Detected context: [slug] — [dir_path] (feature file: [exists|absent])"

Step 2 — Read key files per context (sequential)

Process contexts one at a time. For each context in

context_list

Enumerate implementation files in
```
dir_path
```
(recursive), filtering for file extensions:
```
.ts
```
,
```
.tsx
```
,
```
.js
```
,
```
.jsx
```
,
```
.py
```
,
```
.java
```
,
```
.kt
```
,
```
.rb
```
,
```
.go
```
,
```
.ex
```
,
```
.exs
```
,
```
.cs
```
,
```
.rs
```
,
```
.php
```
,
```
.swift
```
- Prioritize files by recency (most recently modified first)
- Exclude files matching:
```
*.test.*
```
  ,
```
*.spec.*
```
  ,
```
test_*
```
  ,
```
*_test.*
```
  ,
```
*.d.ts
```
  ,
```
*.min.*
```

Apply file cap: read at most

max_files

files. If total enumerated >

max_files

, log:

"[slug]: [total] files found — sampling [max_files] (cap applied)"

For each file to read:
- Attempt to read the file
- If the file is binary or unreadable: skip it; record in
```
skipped_files
```
  with reason
- If readable: extract the following signals:
  - Business rules: explicit conditional constraints (if/when/unless logic that enforces domain rules)
  - Invariants: assertions or validation guards that are always enforced
  - Data model entities: class/struct/interface/type names with their key fields
  - Integration points: imports of external services, APIs, or infrastructure adapters

Accumulate:

context_knowledge = { slug, rules[], invariants[], entities[], integrations[], files_read[], skipped[] }

Step 3 — Write

ai-context/features/<slug>.md

For each context processed in Step 2:

If
ai-context/features/<slug>.md
does not exist (or

ai-context/features/

is absent):

Create the directory if needed
Write a new file using the six-section structure below
All AI-generated sections receive
```
[auto-updated]
```
markers

If
ai-context/features/<slug>.md
already exists:

Read the entire file

Identify

<!-- [auto-updated]: codebase-teach ... -->

...

<!-- [/auto-updated] -->

blocks

Overwrite content only inside
```
[auto-updated]
```
blocks
Preserve byte-for-byte all content outside any
```
[auto-updated]
```
block
If no
```
[auto-updated]
```
block exists for a section, append the section at the end of the file inside a new
```
[auto-updated]
```
block

Six-section feature file structure:

markdown

# [Context Name] — Domain Knowledge

Last updated by: codebase-teach
Last run: YYYY-MM-DD

---

## Domain Overview

<!-- [auto-updated]: codebase-teach — last run: YYYY-MM-DD -->
[AI-extracted 2–4 sentence summary of what this bounded context does and its primary responsibilities]
<!-- [/auto-updated] -->

---

## Business Rules and Invariants

<!-- [auto-updated]: codebase-teach — last run: YYYY-MM-DD -->
[AI-extracted explicit conditional constraints and always-true invariants from the source]

- Rule: [description]
- Invariant: [description]
<!-- [/auto-updated] -->

---

## Data Model Summary

<!-- [auto-updated]: codebase-teach — last run: YYYY-MM-DD -->
Key entities detected:

| Entity | Key Fields |
|--------|-----------|
| [Name] | [field1, field2, ...] |
<!-- [/auto-updated] -->

---

## Integration Points

<!-- [auto-updated]: codebase-teach — last run: YYYY-MM-DD -->
External dependencies and integration touchpoints:

- [service/API name]: [what it is used for]
<!-- [/auto-updated] -->

---

## Decision Log

<!-- [auto-updated]: codebase-teach — last run: YYYY-MM-DD -->
<!-- Append new AI-detected decisions below. Human entries above this marker are preserved. -->
<!-- [/auto-updated] -->

---

## Known Gotchas

<!-- [auto-updated]: codebase-teach — last run: YYYY-MM-DD -->
<!-- Append new AI-detected gotchas below. Human entries above this marker are preserved. -->
<!-- [/auto-updated] -->

_template.md
guard: never read, write, or treat any file whose name begins with

as a feature context. Skip entirely.

Step 4 — Evaluate coverage and write

teach-report.md

Coverage calculation:

documented_contexts = count of contexts that have an ai-context/features/<slug>.md file after Step 3
total_contexts      = count of contexts in context_list
coverage_pct        = (documented_contexts / total_contexts) * 100   (0 if total_contexts == 0)
gap_list            = contexts in context_list where existing_feature_file was false before Step 3
                      AND no file was created in Step 3

Write
teach-report.md
in the project working directory root (same level as

analysis-report.md

). Overwrite if it exists.

markdown

# Teach Report — [Project Name]

Last run: YYYY-MM-DD
Skill: codebase-teach

## Summary

Contexts detected: [total_contexts]
Contexts documented: [documented_contexts]
Coverage: [coverage_pct]%

## Coverage

[coverage_pct]% — [documented_contexts] of [total_contexts] contexts documented.

## Gaps

Contexts detected in code but not documented in ai-context/features/:

- [context-slug] — [dir_path]

[If no gaps: "None — all detected contexts are documented."]

## Files Read

### [context-slug]
- [file path] — sampled
- [file path] — sampled
- [file path] — SKIPPED: [reason]

[Repeat for each context]

## Sections Written / Updated

- ai-context/features/[context].md — [created|updated] — sections: [list of sections written]

ai-context/features/

was absent at run time, append to the Summary section:

Note: ai-context/features/ was absent at run time. Recommend running /memory-init to scaffold the directory before re-running /codebase-teach.

Rules

MUST NOT modify

ai-context/stack.md

ai-context/architecture.md

ai-context/conventions.md

ai-context/known-issues.md

, or

ai-context/changelog-ai.md

MUST NOT modify any file under
```
docs/
```
MUST NOT be invoked automatically by any other skill — user-initiated only
MUST skip any file or directory whose name begins with
```
_
```
in
```
ai-context/features/
```
MUST preserve all human-authored content outside
```
[auto-updated]
```
markers when updating existing feature files
MUST process contexts sequentially — never in parallel
MUST apply the
```
teach_max_files_per_context
```
cap (default 10) to every context
MUST list skipped files in
```
teach-report.md
```
under the "Files Read" section for the relevant context
MUST complete without error even when no bounded context directories are detected
MUST write
```
teach-report.md
```
on every successful run

[auto-updated]

marker format:

<!-- [auto-updated]: codebase-teach — last run: YYYY-MM-DD -->

...

<!-- [/auto-updated] -->

— consistent with

project-analyze

convention

Output

teach-report.md

(mandatory — written to project root on every run)

Required sections:

Section	Content
Summary	Contexts detected, documented, coverage %
Coverage	Percentage and ratio
Gaps	Contexts found in code but undocumented (or "None")
Files Read	Per-context list of files sampled and skipped
Sections Written / Updated	Per-feature-file: created or updated, which sections

ai-context/features/<context>.md

(one per bounded context)

Written or updated during Step 3. Six sections with

[auto-updated]

markers on all AI-generated content.

codebase-teach

NPX Install

Tags

SKILL.md Content

codebase-teach

Purpose

Process

Step 0 — Load project context (non-blocking)

Step 1 — Scan bounded contexts

Step 2 — Read key files per context (sequential)

Step 3 — Write
`ai-context/features/<slug>.md`

Step 4 — Evaluate coverage and write
`teach-report.md`

Rules

Output

`teach-report.md`
(mandatory — written to project root on every run)

`ai-context/features/<context>.md`
(one per bounded context)