Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/cognitx-leyton/codegraph/researchgit clone --depth 1 https://github.com/cognitx-leyton/codegraphWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00010 | $0.00672 |
| Opus 5 | $0.00005 | $0.00336 |
| Sonnet 5 | $0.00002 | $0.00134 |
| Haiku 4.5 | $0.00001 | $0.00067 |
Grade A, and why
research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research (Step 2)
Input: $ARGUMENTS
Deep research before creating an implementation plan. Combines codegraph queries, codebase exploration, and external documentation research.
Phase 1: Issue Context
If input is a number, fetch the GitHub issue:
gh issue view $ARGUMENTS
Extract: title, description, labels, linked PRs, comments.
If input is text, use it directly as the research topic.
Phase 2: Codebase Impact Analysis
Use codegraph to understand what the change touches:
2.1 Find affected symbols
/graph "MATCH (c:Class) WHERE c.name CONTAINS '<keyword>' RETURN c.name, c.file"
/graph "MATCH (f:Function) WHERE f.name CONTAINS '<keyword>' RETURN f.name, f.file"
2.2 Blast radius
For each affected symbol, check who depends on it:
/blast-radius <symbol>
2.3 Related files
/graph "MATCH (f:File)-[r:IMPORTS_SYMBOL]->(g:File) WHERE g.path CONTAINS '<path>' RETURN f.path, r.symbol"
2.4 Architecture check
/arch-check
Note any existing violations that might interact with the change.
Phase 3: Codebase Exploration
Use the Explore agent to:
- Read the files identified in Phase 2
- Understand existing patterns and conventions
- Identify test files that cover the affected code
- Note any related TODO/FIXME/HACK comments
Phase 4: External Documentation (if needed)
If the issue involves external libraries, APIs, or frameworks:
4.1 Context7 (for library docs)
Use context7 MCP to fetch current documentation for the relevant library.
4.2 NotebookLM (for deep research)
If complex research is needed:
nlm notebook list # check existing notebooks
nlm notebook query <notebook-id> "<question>"
4.3 Web search (fallback)
Search for specific error messages, migration guides, or API references.
Phase 5: Research Summary
Output a structured summary:
## Research: <topic>
### Issue
<Issue title and key requirements>
### Affected Code
| File | Symbol | Impact |
|------|--------|--------|
| path | name | what changes |
### Blast Radius
- N files import the affected symbols
- N tests cover the affected code
- Key callers: <list>
### External Context
<Any library docs, API details, or migration notes>
### Risks
| Risk | Severity | Mitigation |
|------|----------|------------|
| ... | HIGH/MED/LOW | ... |
### Recommended Approach
<1-2 paragraphs on how to implement this>
### Ready for: /plan_local
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 109 lines · 10 tokens per session scan A 9a62d6fa1076
research is a command published in the GitHub repository cognitx-leyton/codegraph (11 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 10 tokens to every session and 672 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
feedback
Security Design Review — PRD/기획서 기반 보안 의견서·검토 의견서 생성.
compliance
이전 보안 진단 보고서의 Finding들이 패치되었는지 확인하고, 변경된 코드에서 신규 취약점을 탐색합니다.
va
Vulnerability Assessment — 8차원 아키텍처 진단 + Self-Verify + Evidence Verification.
pentest
Penetration Testing — 시나리오 기반 모의해킹 + POC + 라이브 검증 (State Delta 기반).
redteam
Red Team Operations — 인프라 설정 보안 리뷰 + MITRE ATT&CK + Detection Engineering.
verify
Adversarial Verification — 보안 진단 보고서 독립 검증 (Autonomous First).