research_codebase
Research the codebase before design — generate questions, dispatch parallel ticket-blind subagents, produce a factual research doc
Install / Use
npx skills add siraj-samsudeen/feather-flowInstalls into whichever agent you are using.
Claude Commands
Claude Code slash commands
Quality Score
Category
Data & AnalyticsSupported Platforms
Our assessment of research_codebase
research_codebase scores 54/100 on our quality scale, 579th of 595 Data & Analytics skills we index.
Its Claude Commands is 5.3 KB long, split into 4 sections and no code examples: a solid amount of guidance for an agent.
It has no GitHub stars yet, so there is no community track record; judge it on its content.
Maintenance, license and trust
- We could not determine when the repository was last updated.
- Our last check on 2026-10-20 found the source still online.
- No license is declared. By default that means all rights are reserved: you can read it, but reusing or redistributing it is not clearly permitted. Ask the author before building on it commercially.
- Its trust signals score 68/100, with 3 cautions from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.
AI review by kimi-k2.7-code on 2026-09-25. Automated pattern scan on 2026-09-24. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
research_codebase compared with similar skills
All 4 of these similar skills score higher than research_codebase; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| research_codebase (this skill)by siraj-samsudeen | 54 | 0 | — | Claude Commands |
| claude-memby thedotmack | 100 | 113.5k | today | CLAUDE.md |
| taste-skillby Leonxlnx | 100 | 110.6k | 1d ago | CLAUDE.md |
| Agent-Reachby Panniantong | 100 | 109.2k | today | CLAUDE.md |
| Understand-Anythingby Egonex-AI | 100 | 90.9k | 10d ago | CLAUDE.md |
Frequently asked questions
- How do I install research_codebase?
- Run
npx skills add siraj-samsudeen/feather-flow. The install tabs above show the steps for each supported agent. - Which AI agents does research_codebase work with?
- It is written for Claude Code, as a Claude Commands file. Other agents that read the same format can often use it too.
- Is research_codebase safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It declares no license and scores 68/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is research_codebase still maintained?
- We could not determine when the repository was last updated.
Skill content
View source on GitHubdescription: Research the codebase before design — generate questions, dispatch parallel ticket-blind subagents, produce a factual research doc argument-hint: '<short description of the area to research, e.g. "how database sources are structured">' disable-model-invocation: true allowed-tools: Read, Grep, Glob, Write, Bash(git:), Bash(mkdir:), Bash(date:*), Agent, AskUserQuestion
You are running the /research_codebase command. Produce a purely factual research doc for the area of the codebase the user wants to investigate. No opinions. No recommendations. No design proposals. No suggestions about what "should" happen next.
The research doc is the input to a later /brainstorming session. Your job is to map the terrain, not to plan the route.
User's request: $ARGUMENTS
If $ARGUMENTS is empty, stop and ask what the user wants researched.
Phase 1 — Generate questions (in chat)
Draft 5–10 research questions that, if answered, would map the area of the codebase relevant to the upcoming work.
Good questions are:
- Factual — "how does X work?" not "should we do Y?"
- Specific — name modules, protocols, contracts, file paths where you can
- Coverage-oriented — together they should cover the integration surface the upcoming work is likely to touch
Present the questions as a numbered list. Then ask the user to approve, edit, reword, add, or remove questions before dispatch. Wait for their response. Do not proceed until the user has explicitly approved the question list.
Phase 2 — Dispatch research (parallel, ticket-hidden)
Once questions are approved:
-
Cluster related questions into 2–4 logical groups (e.g. "protocol and base class," "registry," "test fixtures"). Each cluster becomes one subagent.
-
Dispatch the subagents in parallel — single message, multiple
Agenttool calls — usingsubagent_type: general-purpose. -
Critical discipline: each subagent prompt contains ONLY the cluster's questions and a factual framing. It MUST NOT contain:
- The user's original
$ARGUMENTS - What is being built or why
- Any hint of the upcoming work's direction or goal
- Any framing like "we are about to add X" or "in preparation for Y"
This is the whole point of the command: ticket-blind research returns facts. Ticket-aware research returns opinions disguised as facts.
Subagent prompt template (use this verbatim, just substitute the questions):
You are answering factual questions about the current codebase. Return pure observations: what the code does today, which files/functions/lines are involved, what contracts and invariants exist. No opinions. No recommendations. No suggestions about what should change. If a question is ambiguous, make the ambiguity explicit. If a question cannot be answered from the codebase, say so plainly. Questions to answer: [insert this cluster's questions, numbered] For each question, report: - What the code does today - Which files / functions / lines are relevant (use file:line references) - Any noteworthy contracts, invariants, or gotchas - Short code snippets where they clarify the answer Length budget: aim for ~50–150 words per question. Quote sparingly. - The user's original
Phase 3 — Collate, save, commit
-
Merge subagent outputs into one markdown doc. One section per original (approved) question, in the order the user approved them. Use the question itself as the section heading.
-
Strip opinions. Read the merged doc once. If a subagent slipped in a recommendation ("you should...", "the cleanest approach would be...", "consider..."), either delete the sentence or reword to a fact. This is a hard rule.
-
Determine the slug — kebab-case derivation of
$ARGUMENTS, max ~5 words (e.g.database-sources,change-detection-watermarks). -
Get today's date and the current commit SHA:
date +%Y-%m-%d git rev-parse --short HEAD -
Create the research directory if needed:
mkdir -p docs/superpowers/research -
Write the doc to
docs/superpowers/research/<YYYY-MM-DD>-<slug>.mdwith this header:# <Human-readable title> — Research Notes **Date:** YYYY-MM-DD **Commit at time of research:** <short-sha> **Scope:** <one-sentence description of what was researched> --- ## <Question 1, verbatim> <Answer.> ## <Question 2, verbatim> <Answer.> ...The header's commit SHA exists so a future reader can tell whether the doc is still fresh.
-
Announce in chat — do NOT commit. The user reviews first, then commits themselves if they want the doc tracked:
Research saved to
docs/superpowers/research/<YYYY-MM-DD>-<slug>.md. Review it, then commit if you want it tracked:git add docs/superpowers/research/<YYYY-MM-DD>-<slug>.md git commit -m "docs(research): <slug>"Run
/brainstormingwhen you're ready to start design — pass this path as context.
What this command does NOT do
- Propose approaches, designs, or architecture
- Write or invoke a plan
- Invoke
brainstorming,writing-plans, or any implementation skill - Edit any file outside
docs/superpowers/research/ - Run tests or modify source code
- Commit the research doc, or any other file — review and commit are the user's job
The command ends after Phase 3.
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
