菜单
Conducts a comprehensive research across the codebase to answer user questions by spawning parallel sub-agents and synthesizing their findings.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Use when deciding how deeply to review a PR, which reviewers to involve, or whether to accept a large agentic PR
Plan test coverage and the TDD workflow for a GitHub issue through a grill-me interview, then produce an execution-ready plan doc (grounding, test locations, red/green steps, PR breakdown, review gates, task-list bookkeeping) at the repo root. Use when asked to plan a TDD implementation, plan which tests to add for an issue, or scope out test coverage before writing code.
Use ONLY when Nico explicitly invokes it (typically via /pdp-confidence-update) to write, prepare, or reconstruct the weekly PDP confidence update for a project channel. Do NOT trigger automatically from general Slack/status questions. Reconstructs the channel since the last `!pdp progress` post and drafts a ready-to-post update in Nico's house style.
Produces Nico's daily stand-up reply — "Yesterday", "Today", and "Blockers or risks" sections in his house style, organized by his current P1/P2/P3 epics with everything else under "Also" — then posts each as a separate Geekbot reply in his Slack DM once he agrees on the wording. Use when Nico asks to write his daily stand-up, stand-up message, Geekbot reply, or to summarize yesterday's work for stand-up.
Use when reviewing a PR in camunda/camunda that touches AGENTS.md, skills under .claude/skills/, the way the project uses ADRs, or the way the project uses architecture docs (not new ADRs or architecture docs themselves) — i.e., Agent-Ready initiative PRs
Use when user shares an error message, stack trace, or log snippet to investigate — or when investigating an incident that has a concrete error artifact (log line, exception, failed assertion). Skip when the incident has no error artifact, e.g. performance regressions, throughput/latency drops, capacity issues, or behavioral discrepancies without logs.
| name | 1_research_codebase |
| description | Conducts a comprehensive research across the codebase to answer user questions by spawning parallel sub-agents and synthesizing their findings. |
You are tasked with conducting comprehensive research across the codebase to answer user questions by spawning parallel sub-agents and synthesizing their findings.
When this command is invoked, respond with:
I'm ready to research the codebase. Please provide your research question or area of interest, and I'll analyze it thoroughly by exploring relevant components and connections.
Then wait for the user's research query.
Read any directly mentioned files first:
Analyze and decompose the research question:
Spawn parallel sub-agent tasks for comprehensive research:
Wait for all sub-agents to complete and synthesize findings:
Generate research document: Structure the document with YAML frontmatter followed by content:
---
title: "[Concise research title]"
type: research
date: [Current date in YYYY-MM-DD format]
tags: [relevant-component-names]
status: complete
---
# Research: [User's Question/Topic]
## Research Question
[Original user query]
## Summary
[High-level findings answering the user's question]
## Detailed Findings
### [Component/Area 1]
- Finding with reference (file.ext:line)
- Connection to other components
- Implementation details
### [Component/Area 2]
...
## Code References
- `path/to/file.py:123` - Description of what's there
- `another/file.ts:45-67` - Description of the code block
## Architecture Insights
[Patterns, conventions, and design decisions discovered]
## Open Questions
[Any areas that need further investigation]
Save and present findings:
thoughts/research/NNN_topic.md where NNN is a 3-digit sequential number (001, 002, etc.) unless told otherwise