learn-code

name	learn-code
description	Analyze a codebase for engineer onboarding. Detects language, maps modules, analyzes each module in parallel, discovers cross-module relationships, and produces an ONBOARDING.md guide.
argument-hint	<repo-path-or-url> [--exclude <glob>...]
allowed-tools	Read, Write, Bash, Glob, Grep, Agent

Learn-Code — Codebase Analysis for Onboarding

Single-skill pipeline that detects language, maps modules, analyzes each module in parallel via fan-out agents, discovers cross-module relationships, and produces a structured onboarding guide.

Usage

/docs-skills:learn-code /path/to/repo
/docs-skills:learn-code https://github.com/user/repo
/docs-skills:learn-code /path/to/repo --exclude "test/*" "vendor/*"

Arguments

• $1 — Path or URL of the repository to analyze (required). Local path or git URL. URLs are cloned to .agent_workspace/<repo-name>/_clone/
• --exclude <glob>... — Glob patterns to exclude from analysis

Pre-flight

1. Parse and validate arguments

Extract the repo path from the first positional argument. Extract any --exclude patterns.

2. Resolve repo path

Git URL (matches https://, http://, git@, git://): Derive REPO_NAME from the last path segment (strip .git). Set GIT_ROOT="$(git rev-parse --show-toplevel 2>/dev/null || pwd)". Clone to ${GIT_ROOT}/.agent_workspace/${REPO_NAME}/_clone via git_pr_reader.py clone. If clone dir exists, ask user to pull or use as-is. STOP on clone failure.

Local path: Validate it exists, is a directory, and has files. Resolve to absolute. Derive REPO_NAME from basename. STOP if path not found.

3. Set base path

GIT_ROOT="$(cd "$(git rev-parse --show-toplevel 2>/dev/null || pwd)" && pwd)"
BASE_PATH="${GIT_ROOT}/.agent_workspace/${REPO_NAME}"
mkdir -p "${BASE_PATH}"

4. Check for existing progress (resume)

Check for ${BASE_PATH}/workflow/learn-code_${REPO_NAME}.json. If found and in_progress: resume from first pending/in_progress step. If completed: ask user to re-run or show existing results. If not found: create new progress file.

5. Create progress file

Write to ${BASE_PATH}/workflow/learn-code_${REPO_NAME}.json. See output schemas for the JSON structure.

6. Show analysis plan

Log the repo name, absolute path, step order, and exclude patterns.

---

Step 1 — Detection

Detect the primary language, walk the file tree to build a module map, and read config files.

1.1 Set output path

OUTPUT_DIR="${BASE_PATH}/detection"
mkdir -p "$OUTPUT_DIR"

1.2 Detect language

python3 ${CLAUDE_SKILL_DIR}/scripts/detect_language.py --repo <REPO_PATH>

Capture the JSON output. If it contains an error field, STOP and report the error.

Extract primary_language from the result.

1.3 Build module map

python3 ${CLAUDE_SKILL_DIR}/scripts/build_module_map.py --repo <REPO_PATH> --lang <PRIMARY_LANGUAGE> [--exclude <PATTERNS>...]

Capture the JSON output. If it contains an error field, STOP and report the error.

1.4 Read config files

From the module map result, read each file listed in config_files. Read the actual file content from the repo (e.g., <REPO_PATH>/pyproject.toml). Truncate each config file to 5000 characters.

1.5 Write detection.json

Combine all detection and module map results. Write to ${OUTPUT_DIR}/detection.json. See output schemas for the JSON structure.

1.6 Write step-result.json

Write to ${OUTPUT_DIR}/step-result.json. See output schemas for the JSON structure.

1.7 Update progress

Update the progress file: set steps.detection.status to completed, set steps.detection.output to ${OUTPUT_DIR}/, set steps.detection.result to the step-result data. Update updated_at.

Log: "Detection complete: <primary_language>, <module_count> modules, <total_source_files> source files".

---

Step 2 — Module Registry

Dispatch the repo-mapper agent to produce a per-module registry with tailored analysis questions.

2.1 Set paths

INPUT_FILE="${BASE_PATH}/detection/detection.json"
OUTPUT_DIR="${BASE_PATH}/module-registry"
mkdir -p "$OUTPUT_DIR"

2.2 Read detection data

Read ${INPUT_FILE}. If it does not exist, STOP and report that the detection step must complete first.

Extract primary_language, modules, config_contents, module_count.

If module_count is 0, write an empty registry and step-result, then skip to Step 3.

2.3 Dispatch repo-mapper agent

Use subagent_type: docs-skills:repo-mapper. Include in the prompt: DETECTION_DATA (modules, module_count, config_files as JSON), CONFIG_CONTENTS (each config file with filename header), and REPO_PATH. Request a JSON array of module entries.

2.4 Parse agent response

The agent should return a JSON array. Parse it into registry.json.

If the agent response is not valid JSON:

1. Try to extract a JSON array from the response (look for [ ... ])
2. If that fails, create a fallback registry with minimal entries for each module

2.5 Write registry.json

Write the parsed JSON array to ${OUTPUT_DIR}/registry.json.

2.6 Write registry.md

Generate a human-readable markdown table. Write to ${OUTPUT_DIR}/registry.md. See output templates for the format.

2.7 Write step-result.json

Write to ${OUTPUT_DIR}/step-result.json. See output schemas for the JSON structure.

2.8 Update progress

Update progress file for module-registry step. Log: "Registry complete: <module_count> modules (low: N, medium: N, high: N)".

---

Step 3 — Module Analysis

Fan-out module-analyzer agents with size-aware tiering and batched dispatch.

3.1 Set paths

REGISTRY_FILE="${BASE_PATH}/module-registry/registry.json"
DETECTION_FILE="${BASE_PATH}/detection/detection.json"
OUTPUT_DIR="${BASE_PATH}/module-analysis"
mkdir -p "$OUTPUT_DIR"

3.2 Read upstream data

Read ${REGISTRY_FILE} and ${DETECTION_FILE}.

Extract primary_language, module file lists from detection.modules, and registry entries.

3.3 Classify modules into tiers

python3 ${CLAUDE_SKILL_DIR}/scripts/classify_modules.py \
  --detection "${DETECTION_FILE}" \
  --registry "${REGISTRY_FILE}"

Capture the JSON output. This produces three tiers:

Tier	Criteria	Agent strategy
full	≤3000 lines, non-low complexity	Full source in prompt
api-guided	3001–8000 lines, or low-complexity multi-file	API + truncated source (first 2000 lines). Agent reads more from disk if needed
api-only	>8000 lines, or auto-generated code, or single low-complexity file	No agent dispatch — generate entry from API + registry

Log: "Module tiers: <full_count> full, <api_guided_count> api-guided, <api_only_count> api-only".

3.4 Pre-extract public API (AST-aware)

For each module, run the language-appropriate AST extraction: Python uses extract_public_api.py --files <files> --lang python --module <name>. Go/JS/TS use uv run --script ${CLAUDE_SKILL_DIR}/scripts/extract_public_api_treesitter.py --files <files> --lang <lang> --module <name>. Log warning and continue if extraction fails for a module.

3.5 Generate api-only entries (no agent dispatch)

For each module in the api-only tier, generate a summary entry directly from the pre-extracted API and registry data. Write each to ${OUTPUT_DIR}/<safe-module-name>.json. See output schemas for the JSON structure.

3.6 Load source for agent-analyzed modules

For full and api-guided tiers, concatenate source files with ### FILE: <relative-path> headers. Keep all import statements (relationship signal). For api-guided: truncate to first 2000 lines with a ### [TRUNCATED] note.

3.7 Batch dispatch module-analyzer agents

Group full and api-guided modules into batches of max 10 agents per batch. Dispatch each batch as a single message for parallel execution. Wait for the batch to complete before dispatching the next.

Each agent uses subagent_type: docs-skills:module-analyzer. Include in the prompt: MODULE name, LANGUAGE, QUESTION from registry, PUBLIC_API (pre-extracted AST JSON or "Not available"), SOURCE (concatenated with ### FILE: headers), and output path <OUTPUT_DIR>/<safe-module-name>.json. For api-guided modules, add REPO_PATH and a note that source is truncated.

Critical: All Agent tool calls within a single batch MUST be in a single message for parallel execution.

3.8 Collect and merge results

After all batches complete, read each <OUTPUT_DIR>/<safe-module-name>.json file.

For modules where the agent failed or produced invalid JSON, create a fallback entry. See output schemas for the JSON structure.

3.9 Write summary.json

Combine all module results (api-only, agent-analyzed, and fallback) into a single JSON array. Write to ${OUTPUT_DIR}/summary.json.

3.10 Write summary.md

Generate a human-readable summary. Write to ${OUTPUT_DIR}/summary.md. See output templates for the format.

3.11 Write step-result.json

Write to ${OUTPUT_DIR}/step-result.json. See output schemas for the JSON structure.

3.12 Update progress

Update progress file for module-analysis step. Log: "Module analysis complete: <analyzed> modules (full: N, api-guided: N, api-only: N, failed: N)".

---

Step 4 — Relationships

Cross-module dependency analysis with prioritized pair selection and batched dispatch.

4.1 Set paths

SUMMARY_FILE="${BASE_PATH}/module-analysis/summary.json"
DETECTION_FILE="${BASE_PATH}/detection/detection.json"
REGISTRY_FILE="${BASE_PATH}/module-registry/registry.json"
OUTPUT_DIR="${BASE_PATH}/relationships"
mkdir -p "$OUTPUT_DIR"

4.2 Read upstream data

Read ${SUMMARY_FILE}, ${DETECTION_FILE}, and ${REGISTRY_FILE}.

4.3 Build dependency pairs

python3 ${CLAUDE_SKILL_DIR}/scripts/build_dep_pairs.py \
  --summaries "${SUMMARY_FILE}" \
  --registry "${REGISTRY_FILE}"

Capture JSON output. If total_pairs is 0, write empty results and step-result, then skip to Step 5.

4.4 Prioritize pairs

Priority (max 20): at least one module is "high" complexity or "read-first" priority, AND both have analysis_depth != "api-only". Sort by: both "high" first. Lightweight: all remaining pairs — generate entries directly (see output schemas).

4.5 Prepare source data for priority pairs

For each pair (module_a, module_b): Module A: if ≤3000 lines, concatenate source with ### FILE: headers; otherwise use pre-extracted API. Module B: always API surface only (via extract_public_api.py or extract_public_api_treesitter.py).

4.6 Read language guidance

Read language-specific relationship analysis guidance from ${CLAUDE_PLUGIN_ROOT}/reference/language-configs.md.

4.7 Batch dispatch relationship-analyzer agents

Group priority pairs into batches of max 10 agents per batch. Dispatch each batch as a single message for parallel execution. Wait for the batch to complete before dispatching the next.

Each agent uses subagent_type: docs-skills:relationship-analyzer. Include in the prompt: MODULE_A, MODULE_B, LANGUAGE, SOURCE_A (full source or API surface), API_B (public API JSON), LANGUAGE_GUIDANCE (from language-configs.md), REPO_PATH, and output path <OUTPUT_DIR>/<mod_a>--<mod_b>.json. For large module A (>3000 lines), note that source is API-only and agent should read from REPO_PATH.

Critical: All Agent tool calls within a single batch MUST be in a single message for parallel execution.

4.8 Collect and merge results

After all batches complete, read each <OUTPUT_DIR>/<mod_a>--<mod_b>.json file. For failed agents or missing files, create a fallback entry. See output schemas for the JSON structure.

Combine agent results with the lightweight entries from step 4.4.

4.9 Write relationships.json

Write the array of all relationship results (priority + lightweight) to ${OUTPUT_DIR}/relationships.json.

4.10 Write dependency-graph.json

Build a graph structure from the summaries and relationships. Write to ${OUTPUT_DIR}/dependency-graph.json. See output schemas for the JSON structure.

4.11 Write relationships.md

Generate a human-readable summary. Write to ${OUTPUT_DIR}/relationships.md. See output templates for the format.

4.12 Write step-result.json

Write to ${OUTPUT_DIR}/step-result.json. See output schemas for the JSON structure.

4.13 Update progress

Update progress file for relationships step. Log: "Relationship analysis complete: <priority_count> priority + <lightweight_count> lightweight pairs (tight: N, loose: N, none: N)".

---

Step 5 — Synthesis

Combine all module summaries and relationship data to produce the final ONBOARDING.md.

5.1 Set output path

OUTPUT_DIR="${BASE_PATH}/synthesis"
mkdir -p "$OUTPUT_DIR"

5.2 Build synthesis context

python3 ${CLAUDE_SKILL_DIR}/scripts/build_synthesis_context.py \
  --base-path "${BASE_PATH}" \
  --max-size 80000 > "${OUTPUT_DIR}/context.json"

If the output contains an error field, STOP and report the error.

The --max-size 80000 flag ensures the context stays within agent context limits by progressively compacting summaries and relationships.

Log: "Synthesis context: <context_size_bytes> bytes (truncated: <truncated or 'no'>)".

5.3 Dispatch synthesis-writer agent

Dispatch subagent_type: docs-skills:synthesis-writer. The context is written to ${OUTPUT_DIR}/context.json — the agent reads it from disk. Tell the agent to write ONBOARDING.md (and dependency-graph.json if relationships exist) to OUTPUT_DIR, following the template from ${CLAUDE_PLUGIN_ROOT}/reference/onboarding-template.md.

5.4 Verify output

Confirm ${OUTPUT_DIR}/ONBOARDING.md exists. If it does not, STOP and report the synthesis agent failed.

5.5 Write step-result.json

Scan ONBOARDING.md for level-2 headings (##) to determine sections. Write to ${OUTPUT_DIR}/step-result.json. See output schemas for the JSON structure.

5.6 Update progress

Update progress file for synthesis step. Log: "Synthesis complete: ONBOARDING.md written to ${OUTPUT_DIR}".

---

Failure Handling

If any step skill fails (throws an error or does not produce output):

• Set steps.<step-name>.status to failed in the progress file
• Log the error
• Ask the user: "Step <step-name> failed. Retry or skip?"
• If retry: reset to pending and re-run the step
• If skip: mark as failed and continue (downstream steps with this as input may also fail)

---

Completion

After all steps complete:

Update workflow status

Set status to completed. Update updated_at. Write progress file.

Print completion summary and suggest next steps

See output templates for the summary format and suggested next steps.