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/colbymchenry/codegraph/add-langnpx skills add colbymchenry/codegraph --skill add-langgit clone --depth 1 https://github.com/colbymchenry/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 | $0.00077 | $0.03008 |
| Opus 5 | $0.00039 | $0.01504 |
| Sonnet 5 | $0.00015 | $0.00602 |
| Haiku 4.5 | $0.00008 | $0.00301 |
Grade A, and why
add-lang scanned grade A with 1 finding 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 yesterday.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
classes/structs, imports, enums; or `curl` a raw file from a known repo), then: How it starts
The opening of the file, as written. The whole thing — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Add a language to CodeGraph
Wire a new tree-sitter language into codegraph's extraction pipeline, prove it extracts real symbols on popular repos, and prove it beats no-codegraph for an agent. Runs fully autonomously — pick repos, benchmark, update docs, then report. Never commit, push, publish, or tag (house rule); leave all changes for the user to review.
The argument is the language token used throughout the Language union, e.g.
lua, elixir, zig. If none was given, ask which language. Use the lowercase
single-token form everywhere (csharp, not c#).
Prerequisites
- Run from the codegraph repo root.
node,git,gh, and a logged-inclaudeCLI (the benchmark spawns realclaude -pruns). - The benchmark uses the local dev build — Step 8 builds + links it on PATH.
Workflow
Copy this checklist and work through it in order:
- [ ] 1. Resolve language; bail early if already supported (just benchmark)
- [ ] 2. Find a grammar + health-check it (ABI / heap corruption)
- [ ] 3. Discover the grammar's AST node types (dump-ast.mjs)
- [ ] 4. Wire the language (4 files; sometimes a 5th core touch)
- [ ] 5. Build + verify-extraction loop until PASS
- [ ] 6. Add extraction tests; make them green
- [ ] 7. Auto-pick 3 popular repos by size tier; add to corpus.json
- [ ] 8. Benchmark all 3: extraction + with/without A/B
- [ ] 9. Update README + CHANGELOG
- [ ] 10. Report; do NOT commit
Step 1 — Resolve + short-circuit
Check whether the language is already wired: look for the token in the
LANGUAGES const (src/types.ts) and the EXTRACTORS map
(src/extraction/languages/index.ts). If it is already supported (e.g.
typescript, rust), skip Steps 2–6 and go straight to benchmarking
(Steps 7–8) to validate/measure it — note in the report that no code changed.
Step 2 — Find a grammar, then health-check it
ls node_modules/tree-sitter-wasms/out/ | grep -i <lang> # csharp -> c_sharp
- Present → likely off-the-shelf;
grammars.tsresolves it fromtree-sitter-wasmsautomatically. (Many languages: elixir, zig, ocaml, solidity, toml, yaml, …) - Absent → vendor a
.wasmintosrc/extraction/wasm/(likepascal/scala/lua) and add the token to the vendored branch in Step 4.
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.
- yesterday First seen · 220 lines · 77 tokens per session scan A ba81accf3243
add-lang is a skill published in the GitHub repository colbymchenry/codegraph (68,675 stars, last pushed 5d ago), licensed MIT. It adds 77 tokens to every session and 3,008 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…