Skill

research

Research codebase comprehensively and emit SHA-tracked, line-referenced research docs at docs/research/. Decomposes the user's question, spawns parallel sub-agents to investigate, synthesizes findings with concrete file:line references, computes git hash-object SHA for every referenced source file, and writes a YAML-frontmatter doc. If a doc with the same topic/slug already exists, UPDATES that doc instead of duplicating (refreshes SHAs, appends a Follow-up Research section). Companion to the `docs` plugin (C4 docs). TRIGGER on phrases like "research X", "investigate Y", "deep dive into Z", "how does X work in this codebase", "audit X", "explore the codebase for X", "/research". SKIP if the user only wants a quick answer with no doc artifact, or for non-codebase questions.

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How this skill is triggered — by the user, by Claude, or both

Slash command

/research:research

User invocable

Model invocable

Inline context

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The summary Claude sees in its skill listing — used to decide when to auto-load this skill

Produces durable research docs at `docs/research/` that reference source code by `path + git hash-object SHA + line ranges`. Designed to grow incrementally and stay coherent with `docs/` (C4 hierarchy from the `docs` plugin) — research is the per-demand, narrower companion.

SKILL.md

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LanguagePython

Parent stars1

MaintenanceGood

Last CommitApr 27, 2026

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research — incremental, SHA-tracked codebase research

Produces durable research docs at docs/research/ that reference source code by path + git hash-object SHA + line ranges. Designed to grow incrementally and stay coherent with docs/ (C4 hierarchy from the docs plugin) — research is the per-demand, narrower companion.

Output location

docs/research/YYYY-MM-DD-<slug>.md
docs/research/YYYY-MM-DD-<TICKET>-<slug>.md   # if a ticket id is provided

slug: short kebab-case derived from the user's query (≤6 words).
Date: date +%Y-%m-%d in the user's local time.

Frontmatter schema (authoritative)

---
type: research
topic: "Free-form question or research target"
slug: kebab-case-slug
date: 2026-04-27T14:32:00-03:00
researcher: <git config user.name>
git_commit: <HEAD SHA at time of research>
branch: <current branch>
repository: <repo name, e.g. owner/repo>
status: complete | partial
tags: [research, <component-tags>]
last_updated: 2026-04-27
last_updated_by: <name>
last_updated_note: ""        # short why-changed string when updating
references:
  - path: src/auth/handler.py
    sha: <git hash-object SHA-1>
    lines: "12-45"
    note: "Defines the auth middleware entry point"
  - path: src/auth/tokens.ts
    sha: <git hash-object SHA-1>
    lines: "100-130,200"
    note: "Token expiry logic"
related:
  - docs/research/2026-04-20-other-topic.md
---

Required: type, topic, slug, date, git_commit, branch, references. The rest are recommended.

Workflow

Follow these steps in order.

1. Read explicitly mentioned files first

If the user references specific files, tickets, or docs, read them FULLY in the main context (no offset/limit). This grounds the decomposition.

2. Dedupe — look for an existing research doc on the same topic

Before doing any new work:

python3 ${CLAUDE_PLUGIN_ROOT}/scripts/find_existing.py --topic "<user query verbatim or close paraphrase>"
python3 ${CLAUDE_PLUGIN_ROOT}/scripts/find_existing.py --slug "<derived-slug>"

If either prints a path, prefer updating that doc (jump to step 7b — Update existing). Otherwise continue with a new doc.

Also scan docs/ (the C4 docs from the docs plugin) for related context — list candidate paths, but do not block on reading them. Surface relevant ones to sub-agents in step 4.

3. Decompose — ultrathink the question

Break the query into independent investigation lanes. Examples of useful lanes:

Locator: where does feature X live? entry points, modules, files.
Dataflow: how does data move through X? upstream callers, downstream consumers.
Persistence/storage: schemas, migrations, queries.
Tests: what coverage exists? what's missing?
History: relevant commits, CHANGELOG entries.
External boundary: API surface, MCP tools, CLI entrypoints.

Track lanes in TaskCreate so progress is visible.

4. Spawn parallel sub-agents

Use the Agent tool with subagent_type: "Explore" for codebase searches and subagent_type: "general-purpose" for synthesis-heavy lanes. Send all independent lanes in one message with multiple Agent tool calls so they run concurrently.

Each agent prompt should include:

The specific lane question (not the full user query).
Any pre-read files from step 1 quoted as context.
Instruction to return concrete path:line references and short quoted snippets.
Read-only stance (no edits).

5. Synthesize

Wait for all agents to finish. Compile findings:

Live code is the source of truth.
Connect findings across lanes.
Note patterns, conventions, surprising decisions.
Resolve contradictions by re-reading the relevant file (or spawning a focused agent).
Keep the main agent for synthesis, not deep file-reading.

6. Compute SHAs for every referenced file

For every file you cite in the doc:

git hash-object <path>

Batch where convenient. Record each result as a references[] entry with path, sha, lines, note. Use precise line ranges ("12-45") or comma-joined ranges ("12-45,80,120-130").

Also capture:

git rev-parse HEAD              # git_commit
git branch --show-current       # branch
git config user.name            # researcher
basename "$(git rev-parse --show-toplevel)"  # repository (or use owner/repo from `gh repo view`)
date +%Y-%m-%dT%H:%M:%S%z       # ISO date

7a. Write a new research doc

Filename: docs/research/YYYY-MM-DD-<slug>.md. Use the Write tool. Body sections:

# Research: <topic>

## Research Question
<verbatim or close paraphrase of user's query>

## Summary
<2–6 sentences answering the question>

## Detailed Findings

### <Component / lane 1>
- <claim> (`path/to/file.ext:LINE`)
- <claim> (`other/file.ext:RANGE`)

### <Component / lane 2>
...

## Code References
- `path/to/file.py:123` — <what's there>
- `another/file.ts:45-67` — <what's there>

## Architecture Insights
<patterns, conventions, design decisions>

## Related Docs
- `docs/<C4 path>` — <relation>
- `docs/research/<other>.md` — <relation>

## Open Questions
- <unresolved item>

If the current commit is pushed to a remote, you MAY also include GitHub permalinks (https://github.com/<owner>/<repo>/blob/<commit>/<file>#L<line>) alongside local refs. Always keep the local refs too.

7b. Update an existing research doc (same-topic match)

When step 2 found a match, do NOT create a new doc. Instead:

Read the existing doc fully.

Run the staleness check on it:

python3 ${CLAUDE_PLUGIN_ROOT}/scripts/stale.py <path>

Re-investigate the drifted refs (reuse step 4's parallel-agent pattern, scoped to changed files).
Update prior claims that are now wrong.
Recompute git hash-object for every reference (drifted + still-fresh, in case the doc had outdated SHAs from prior work). Update references[].sha.

Append:

## Follow-up Research <ISO timestamp>
<what triggered the update; what changed; new findings>

Update frontmatter: last_updated, last_updated_by, last_updated_note. Bump git_commit to current HEAD. Keep original date and filename.

8. Present a summary

Reply to the user with:

Path of the doc written/updated.
3–5 bullet key findings.
Any open questions or follow-up suggestions.

Companion commands

Command	Purpose
`/research <query>`	Run this skill on a query.
`/research-check [path]`	Staleness report. Empty arg → scans all of `docs/research/`.
`/research-update <path>`	Refresh a stale research doc; appends a Follow-up section.

Staleness algorithm

scripts/stale.py reads each doc's frontmatter and, for every references[] entry, runs git hash-object on the current file. Output JSON list per doc:

{
  "doc": "docs/research/...",
  "topic": "...",
  "slug": "...",
  "stale":   [{"path": "src/foo.py", "stored_sha": "...", "current_sha": "...", "lines": "1-30", "status": "stale"}],
  "missing": [{"path": "src/gone.py", "stored_sha": "...", "current_sha": null, "status": "missing"}],
  "fresh":   [{"path": "src/ok.py", "stored_sha": "...", "current_sha": "...", "status": "fresh"}],
  "is_stale": true
}

A doc is stale if any reference is stale or missing. Use is_stale to triage at a glance.

Same-topic detection rules

scripts/find_existing.py matches in this order:

Exact slug match (case-insensitive).
Exact topic match (case-insensitive).
topic substring match in either direction.

Slug match is preferred — keep slugs stable across updates so dedupe works.

Discipline

Always run fresh codebase research on update; do not trust prior conclusions.
Reference precise line numbers, not whole files.
Keep references[] minimal but exhaustive — every claim that cites code must trace back to a referenced file.
Tag thoroughly (tags) so future research can find this one via related.
Never write placeholder values (e.g. <commit>); compute them before writing.

research

Popularity

Invocation

Context Preview

SKILL.md

research

Popularity

Invocation

Context Preview

SKILL.md

research — incremental, SHA-tracked codebase research

Output location

Frontmatter schema (authoritative)

Workflow

1. Read explicitly mentioned files first

2. Dedupe — look for an existing research doc on the same topic

3. Decompose — ultrathink the question

4. Spawn parallel sub-agents

5. Synthesize

6. Compute SHAs for every referenced file

7a. Write a new research doc

7b. Update an existing research doc (same-topic match)

8. Present a summary

Companion commands

Staleness algorithm

Same-topic detection rules

Discipline

Similar Skills

research — incremental, SHA-tracked codebase research

Output location

Frontmatter schema (authoritative)

Workflow

1. Read explicitly mentioned files first

2. Dedupe — look for an existing research doc on the same topic

3. Decompose — ultrathink the question

4. Spawn parallel sub-agents

5. Synthesize

6. Compute SHAs for every referenced file

7a. Write a new research doc

7b. Update an existing research doc (same-topic match)

8. Present a summary

Companion commands

Staleness algorithm

Same-topic detection rules

Discipline

Similar Skills