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/onsager-ai/dev-skills/codegraphnpx skills add onsager-ai/dev-skills --skill codegraphgit clone --depth 1 https://github.com/onsager-ai/dev-skillsWhat 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 | $0.00199 | $0.01632 |
| Opus 5 | $0.00100 | $0.00816 |
| Sonnet 5 | $0.00040 | $0.00326 |
| Haiku 4.5 | $0.00020 | $0.00163 |
Grade A, and why
codegraph 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 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.
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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
codegraph
Local code graph exposed as 8 MCP tools. Accelerates exploration with O(1) SQLite lookups and FTS5 search — slashing the grep + Read loops needed to orient in a codebase.
It does not replace grep. Codegraph is a tree-sitter approximation of the call graph, not a compiler-grade one: it is fast and low-token but unsound (it has false negatives). Treat it as the accelerator for orientation and traversal, and keep grep as the source of truth for completeness. See Soundness & limits below — this distinction is load-bearing, not a footnote.
When to use
- Brownfield repo where structure is non-obvious
- Task involves: "where is X used", "what breaks if I change Y", "trace flow from A to B"
- Long-running session — cold-start cost amortizes over multiple queries
When NOT to use
- Single-file edits or trivial lookups
- Session likely shorter than cold-start cost (see
references/spike.mdfor per-repo numbers) - Repo not initialized AND no cached
.codegraph/AND short session
Soundness & limits — read before trusting a result
Codegraph is built on tree-sitter, not the compiler, so its call/reference edges are an approximation. It fails in three confirmed ways. The governing rule falls out of them:
Grep is the source of truth for completeness. Codegraph is the accelerator for orientation and traversal. Never invert these.
The asymmetry is the whole argument: for a refactor, a false negative is dangerous (you miss a caller, ship a break); a false positive is merely annoying (you glance and discard). Codegraph has false negatives; grep does not. So codegraph may propose and rank, but grep confirms whenever the answer must be exhaustive.
The three failure modes (measured on a ~580-file Rust+TS repo):
- Unsound edges → false negatives.
callers/callees/impactmiss real call sites when the receiver type can't be resolved from syntax alone (calls on locals, generics, trait objects). Measured: a method with 8 real callers returned 2 — the 5 dropped werelocal.method()and generic-dispatch sites, with no warning. Exact on unique free-functions + direct calls (verified 1/1, 2/2); unreliable on method dispatch. - Name resolution is fuzzy and overload-blind. Bare-name queries prefix-match (
useWorkspaceanswered foruseWorkspacesQuery— a different symbol) and conflate overloads (execute, with 19 definitions, returnedsqlx's.execute()DB calls, not the trait method). For any name with multiple definitions, drop tocodegraph_nodeon a specific symbol id; barecallers/calleesare unattributable. - The index lags the disk. CLI sees nothing until
codegraph sync(~0.3s); the MCP file-watcher closes the gap to ~1–2s. Right after an edit, the index is wrong — grep/Read is ground truth until it catches up.
What ships with it
3 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.
- yesterday First seen · 75 lines · 199 tokens per session scan A effb466edb67
codegraph is a skill published in the GitHub repository onsager-ai/dev-skills (5 stars, last pushed 19d ago), licensed MIT. It adds 199 tokens to every session and 1,632 once invoked, about $0.0010 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 2026-08-31.
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