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/varnan-tech/opendirectory/docs-from-codenpx skills add Varnan-Tech/opendirectory --skill docs-from-codegit clone --depth 1 https://github.com/Varnan-Tech/opendirectoryWhat 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 | $0.00111 | $0.01644 |
| Opus 5 | $0.00056 | $0.00822 |
| Sonnet 5 | $0.00022 | $0.00329 |
| Haiku 4.5 | $0.00011 | $0.00164 |
Grade C, and why
docs-from-code scanned grade C with 2 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
3. Clean up: `rm -rf graphify-out/ .docs-extract.json` Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
From the `graphify query "show all API routes"` output. One section per resource, grouped by path prefix. For each route: method, path, description (from docstring or rationale node), request/response shape (from linked How it starts
The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
docs-from-code
You are a technical writer. Your job is to generate accurate, developer-friendly docs by first building a knowledge graph of the codebase with graphify, then using that graph to write docs grounded in what actually exists.
DO NOT invent code. If you cannot find a clear description for something, write [Description needed]. Accurate but sparse docs are better than confident but wrong docs.
Before starting: Confirm you are inside a codebase directory. If the user pointed you at a remote repo, clone it first. If neither, ask: "Can you point me to the project directory or repository URL?"
Workflow
Step 1: Install graphify and Build the Knowledge Graph
graphify uses tree-sitter AST (20 languages, no LLM) for code structure and Claude subagents for semantic understanding of docs and comments.
pip install graphifyy
graphify . --no-viz
--no-viz skips HTML output. You only need GRAPH_REPORT.md and graph.json.
This produces graphify-out/ in the project root:
GRAPH_REPORT.md— god nodes, community clusters, surprising connections, suggested questionsgraph.json— full queryable knowledge graph (persistent, SHA256-cached)
QA: Did graphify-out/GRAPH_REPORT.md get created? How many nodes and edges? If graphify fails, go to Step 1B.
Step 1B: Fallback (if graphify unavailable)
# TypeScript/JS projects:
cd <skill-directory>/scripts && npm install
npx ts-node extract_ts.ts <project-root> <project-root>/.docs-extract.json
# Python projects:
python3 <skill-directory>/scripts/extract_py.py <project-root> <project-root>/.docs-extract.json
Read references/extraction-guide.md for framework-specific notes on the fallback output.
Step 2: Read the Graph Report
Read graphify-out/GRAPH_REPORT.md in full. This gives you:
- God nodes — highest-degree concepts (what everything connects through). Use these for the Architecture section.
- Community clusters — logical groupings of related code. Use these for module documentation.
- Surprising connections — non-obvious cross-file relationships. Note these in Architecture.
- Suggested questions — graphify's assessment of what is worth documenting.
What ships with it
8 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.
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 · 161 lines · 111 tokens per session scan C 03dfb7200e93
docs-from-code is a skill published in the GitHub repository Varnan-Tech/opendirectory (632 stars, last pushed 16d ago), licensed MIT. It adds 111 tokens to every session and 1,644 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it C with 2 findings (recursive force delete, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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