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 skills/vasilyu1983/ai-agents-public/dev-context-code-graphnpx skills add vasilyu1983/AI-Agents-public --skill dev-context-code-graphgit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vasilyu1983/ai-agents-public/dev-context-code-graph)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/dev-context-code-graph"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/dev-context-code-graph.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00041 | $0.03148 |
| Opus 5 | $0.00020 | $0.01574 |
| Sonnet 5 | $0.00008 | $0.00630 |
| Haiku 4.5 | $0.00004 | $0.00315 |
Grade A, and why
dev-context-code-graph 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 3d 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 — 314 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Graph
Build a deterministic, machine-readable code graph for a single repository. Treat the graph as a machine-readable substrate for an LLM-maintained repo wiki or context hub, not just a terminal-only report. This skill mirrors the dev-context-multi-repo artifact workflow, but works at file and symbol level instead of portfolio level.
Use this skill when you need:
- a committable
graphs/code-graph.jsonartifact - file and symbol maps for one repo
- import, call, inheritance, and test-link analysis
- graph-theory review signals such as articulation points, bridges, cycles, topological order, and alternate paths
- blast radius and minimal review context for changed files or symbols
- budget-bounded context retrieval around 1–3 hot symbols via Personalized PageRank
- a grounded repo description or module description generated from graph data instead of ad hoc codebase prose
Do not use this skill for:
- portfolio-wide repo discovery or cross-repo system maps
- architecture or migration planning across many repos
- prose documentation cleanup without graph generation
Use related skills instead:
- dev-context-multi-repo for repo portfolios and hub-level knowledge graphs
- dev-context-engineering for deciding when code graph vs context graph vs repo graph is the right artifact
- docs-ai-prd for code-graph specs and acceptance criteria
- docs-codebase for publishing graph-backed docs and reports
Quick Reference
| Need | Start here |
|---|---|
| Generate the base artifact set | ## Workflow |
| Validate schema and graph integrity | ### Phase 3: Validate |
| Query blast radius or symbol neighborhoods | ### Phase 4: Query |
| Surface structural graph risk | query_code_graph.py --articulation-points, --bridges, --cycles, --topo-sort, --from ... --to ... --k N |
| Run hot-symbol PPR retrieval | query_code_graph.py --ppr --seed <id> [--seed <id> ...] --top N |
| Detect modules via communities | query_code_graph.py --communities [--resolution γ] [--community-seed N] |
| Pick the right query for a review | references/query-recipes.md |
| Load scripts, schemas, and reports | ## Navigation |
What ships with it
14 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- data/sources.json 2.1 KB
- examples/python-mini-repo.md 401 B
- examples/typescript-mini-repo.md 373 B
- references/code-graph-patterns.md 7.3 KB
- references/parser-support-matrix.md 1.2 KB
- references/query-recipes.md 10 KB
- schemas/code-graph.schema.json 4.2 KB
- schemas/code-profile.schema.json 3.5 KB
- scripts/build_code_graph.py 7.1 KB runs code
- scripts/export_code_graph_report.py 4.3 KB runs code
- scripts/query_code_graph.py 36 KB runs code
- scripts/scan_code_repo.py 24 KB runs code
- scripts/test_code_graph_regressions.py 6.2 KB runs code
- scripts/validate_code_graph.py 9.9 KB runs code
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.
- 3d ago First seen · 314 lines · 41 tokens per session scan A 0d4be57ae088
dev-context-code-graph is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (82 stars, last pushed yesterday), licensed MIT. It adds 41 tokens to every session and 3,148 once invoked, about $0.0002 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.
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