research
Skill8 starsChanged 9 months agoMITView on GitHub
RPI Research Phase - Systematic codebase exploration with parallel agents and chunked output
189 other projects use a version of this.
Adopted by
Most recently taken up by
- quickchatai/quickreviewtheir own version, first seen Nov 15, 2026
- igoroliveirg/claude-copytheir own version, first seen Nov 15, 2026
- krishagel/geoffreytheir own version, first seen Nov 15, 2026
- HiteSit/real-scientist-skillstheir own version, first seen Nov 15, 2026
- Fractera/web4-node-starter-kittheir own version, first seen Nov 15, 2026
- alexj11324/Mikkatheir own version, first seen Nov 15, 2026
Used with
Projects with it also use daily-arxiv, exp-eval and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
LambenthanSkilldaily-arxiv
每日从 arXiv 拉取新论文,过滤相关性,auto-ingest 高优先级论文,检测 SOTA 更新
Also in 5 projects that use this
LambenthanSkillexp-eval
实验判决门:Review LLM 独立评判实验结果 → 4 种判决路径 → 自动更新 claims confidence、ideas status、graph edges
Also in 5 projects that use this
truongtv22SkillProblem-Solving Techniques
Apply systematic problem-solving techniques for complexity spirals (simplification cascades), innovation blocks (collision-zone thinking), recurring patterns (meta-pattern recognition), assumption constraints (inversion exercise), scale uncertainty (scale game), and dispatch when stuck. Techniques derived from Microsoft Amplifier project patterns adapted for immediate application.
Also in 5 projects that use this
liufeng1976Skillsetup-ts-deep-modules
Wire dependency-cruiser into a TypeScript repo so each package is a deep module — implementation hidden in subfolders, reachable only through its entry-point files. User-invoked.
Also in 43 projects that use this
skyllwtSkillexp-pilot-eval
Pilot result evaluation — read pilot results, apply success criteria, update idea page (pilot_result, failure_reason if failed), generate PILOT_VERDICT_REPORT. Called by /ideate Phase 5 after /exp-pilot-run.
Also in 4 projects that use this
skyllwtSkillexp-pilot-run
Pilot experiment execution — read Pilot Spec YAML, write pilot code, run experiment(Confirm with the user before operation and require the applicant to conduct manual inspection), return results. Called by /ideate Phase 5. Does NOT modify wiki pages or judge pass/fail.
Also in 4 projects that use this
- +5 more from Lambenthan
- +1 more from skyllwt
Other versions189 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 189
- mattpocock/skills★ 309ksame name
- Yeachan-Heo/oh-my-claude-sisyphus★ 41ksame name
- Yeachan-Heo/oh-my-claudecode★ 41ksame name
- QuivrHQ/quivr★ 40ksame name
- The-Vibe-Company/quivr★ 40ksame name
- udecode/plate★ 17ksame name
- openai/plugins★ 7.8ksame name
- ArcReel/ArcReel★ 6.3ksame name
- vinvcn/mattpocock-skills-zh-CN★ 5.5ksame name
- withcoral/coral★ 4.9ksame name
179 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository7 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- .github/copilot-instructions.mdCopilot instructions
- context-optimizeSkill
- implementSkill
- planSkill
- validateSkill
- verify-docsSkill
The file
We split this skill into the rules it gives an agent
5 rules. 0 are common, 3 are distinctive, and 2 are very specific to this project.
0
Common
Most of the 169 edited versions we read keep these.
3
Distinctive
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 7
When invoked, perform systematic codebase exploration using parallel agents:
- Very specific: line 21
Use template from .claude/research/RESEARCH_TEMPLATE.md
- Distinctive: line 59
Create chunk manifest table
- Distinctive: line 61
Verify all chunks are COMPLETE
- Very specific: line 101
After completion, run /context-eng:plan $ARGUMENTS
“Common” is measured: kept by at least 6 in 10 of the 169 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Context Engineering: Research Phase (Enhanced)
When invoked, perform systematic codebase exploration using parallel agents:
Key Innovation: Inter-Phase Awareness
This research phase KNOWS how RPI-Plan will consume its output:
- Output structured into research chunks (CHUNK-R1, CHUNK-R2, etc.)
- Each chunk is self-contained with files, dependencies, and status
- RPI-Plan will create a CHUNK-Pn todolist per CHUNK-Rn
- Chunk manifest enables sequential processing by RPI-Plan
Process
- Initialize Research Document
- Create
.claude/research/active/[feature]_research.md - Use template from
.claude/research/RESEARCH_TEMPLATE.md
- Create
Spawn Parallel Agents (3-5 agents)
Agent 1: API/Route Entry Points → CHUNK-R1 Agent 2: Business Logic & Models → CHUNK-R2 Agent 3: Database/Storage Layer → CHUNK-R3 Agent 4: External Integrations → CHUNK-R4 Agent 5: Test Coverage Analysis → CHUNK-R5Each agent receives:
- Feature name and objective
- Assigned domain
- Required output format (chunk structure)
- Line number requirement for all file references
- Per-Agent Chunk Output
Each agent produces a self-contained chunk:
## CHUNK-Rn: [Domain] **Status:** COMPLETE **Parallel Agent:** Agent N **Ready for Planning:** Yes ### Files Explored | File | Lines | Key Findings | ### Code Flow Analysis [call chain with file:line refs] ### Dependencies (This Chunk) - External: [APIs] - Internal: [services] - Aggregate Chunk Results
- Create chunk manifest table
- Combine all agent outputs
- Verify all chunks are COMPLETE
- Generate Inter-Phase Contract
EXPECTED_CONSUMER: rpi-plan CHUNK_PROCESSING_ORDER: sequential (R1 → R2 → R3 → R4 → R5) MARK_AS_PLANNED_WHEN: chunk todolist created REQUIRED_OUTPUT: CHUNK-Pn per CHUNK-Rn - Generate Summary
- 150-word summary for Plan phase
- Reference key files from each chunk
- Recommend approach
Chunk Manifest Format (Required)
| Chunk ID | Domain | Status | Files | Ready for Planning |
|----------|--------|--------|-------|-------------------|
| CHUNK-R1 | API/Routes | COMPLETE | 3 | ✅ |
| CHUNK-R2 | Business Logic | COMPLETE | 4 | ✅ |
| CHUNK-R3 | Database | COMPLETE | 2 | ✅ |
| CHUNK-R4 | External | COMPLETE | 1 | ✅ |
| CHUNK-R5 | Tests | COMPLETE | 3 | ✅ |Context Budget
- Target: 25% of 200k tokens (50k)
- Per-agent budget: ~10k tokens each
- Compaction: After each agent returns
- Final output: ~20k tokens (research doc only)
Output
Research document saved to .claude/research/active/ with:
- Chunk manifest
- Per-chunk details
- Inter-phase contract for RPI-Plan
Next Step
After completion, run /context-eng:plan $ARGUMENTS
RPI-Plan will read chunk manifest and create CHUNK-Pn todolist per CHUNK-Rn
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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- It worked
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Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

