Deep Research
Language: English | 简体中文 | Español | 日本語 | 한국어
Adaptive, auditable deep research for AI agents. This skill helps agents move from broad discovery to cited synthesis while keeping sources, claims, counterevidence, and uncertainty traceable.
Built for research memos, literature reviews, GitHub due diligence, source verification, current technical research, and decisions that need more than a quick lookup.
Why Use It
- Evidence ledger: track research hops, sources, claims, and evidence IDs.
- Adaptive protocol: broaden, deepen, verify, or stop based on what the evidence changes.
- Source-quality checks: separate primary sources, context, weak claims, counterevidence, and stale facts.
- Portable CLI: the ledger tool uses only the Python standard library.
- Marketplace ready: includes
SKILL.md, agent metadata, references, tests, and submission notes.
Install
Install with skills.sh:
npx skills add B143KC47/deep-research-skill
Install with the Codex skill installer:
python "$CODEX_HOME/skills/.system/skill-installer/scripts/install-skill-from-github.py" \
--repo B143KC47/deep-research-skill \
--path .
Clone directly:
git clone https://github.com/B143KC47/deep-research-skill.git
Quick Start
Create a research run:
python scripts/research_ledger.py init \
--question "Which open-source vector database should we evaluate?" \
--out-dir research_runs \
--effort deep \
--deliverable "evidence-backed recommendation"
Record a meaningful research hop:
python scripts/research_ledger.py add-hop \
--run-dir research_runs/<run-dir> \
--hop 1 \
--mode seed \
--tool-or-source web \
--query-or-action "search: official docs and benchmark pages" \
--result-summary "Identified primary docs and benchmark sources" \
--next-questions "Check implementation evidence and limitations"
Attach evidence to a claim:
python scripts/research_ledger.py add-evidence \
--run-dir research_runs/<run-dir> \
--hop 1 \
--source-id S001 \
--title "Project documentation" \
--url-or-path "https://example.com/docs" \
--publisher-or-owner "Example Project" \
--source-type official-doc \
--quality-score 5 \
--stance supports \
--claim "The project supports the required deployment mode" \
--quote-or-locator "Docs: deployment section"
Check readiness before writing the final report:
python scripts/research_ledger.py status --run-dir research_runs/<run-dir>
python scripts/research_ledger.py lint --run-dir research_runs/<run-dir>
Research Workflow
| Phase | What the agent does | Output |
|---|---|---|
| Frame | Restate the question, decision, scope, and freshness needs. | Research plan |
| Map | Split the topic into aspects, source classes, and unknowns. | Aspect map |
| Seed | Search several distinct routes before diving deep. | Initial source graph |
| Extract | Capture claims, locators, dates, versions, and source quality. | Evidence ledger |
| Verify | Look for contradictions, stale facts, and independent support. | Confidence labels |
| Synthesize | Answer with evidence IDs and explicit uncertainty. | Cited report |
Effort Levels
| Effort | Typical use | Target |
|---|---|---|
quick | Low-risk orientation or sanity check | 2-4 meaningful hops |
standard | Normal researched answer | 5-8 hops, 3+ source classes |
deep | Literature review, due diligence, broad synthesis | 9-14 hops, 4+ source classes |
exhaustive | High-stakes, contested, or user-budgeted work | 15+ hops, 5+ source classes |
Hop counts are planning targets, not quotas. Stop when high-impact claims are supported and remaining gaps are explicit.
Repository Layout
.
├── SKILL.md
├── agents/
│ └── openai.yaml
├── docs/
│ └── README.zh-CN.md
│ └── README.es.md
│ └── README.ja.md
│ └── README.ko.md
├── references/
│ ├── research-protocol.md
│ ├── source-quality.md
│ ├── query-playbook.md
│ └── report-template.md
├── scripts/
│ └── research_ledger.py
└── tests/
└── test_research_ledger.py
Development
The ledger script uses only the Python standard library.
Run tests:
python -m unittest discover -s tests
Run a syntax check:
python -m py_compile scripts/research_ledger.py
On Windows, if python opens the Microsoft Store or exits without output, use
py -m:
py -m unittest discover -s tests
py -m py_compile scripts\research_ledger.py
Marketplace
Useful links:
- GitHub repository: https://github.com/B143KC47/deep-research-skill
- Raw skill file: https://raw.githubusercontent.com/B143KC47/deep-research-skill/main/SKILL.md
- ClawHub slug:
b143kc47-deep-research
License
MIT. See LICENSE.