Borrowing it
Nothing to install: this file belongs to andysom25/codegraph. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/andysom25/codegraph/main/.claude/skills/agent-eval/SKILL.mdgit clone --depth 1 https://github.com/andysom25/codegraphWrote 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/andysom25/codegraph/agent-eval)<a href="https://agentmods.dev/skills/andysom25/codegraph/agent-eval"><img src="https://agentmods.dev/badge/skills/andysom25/codegraph/agent-eval/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/andysom25/codegraph/agent-eval"><img src="https://agentmods.dev/badge/skills/andysom25/codegraph/agent-eval.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00065 | $0.00911 |
| Opus 5.5 | $0.00026 | $0.00364 |
| Sonnet 5.5 | $0.00013 | $0.00182 |
| Haiku 4.5 | $0.00006 | $0.00091 |
Grade A, and why
agent-eval 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 1mo 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.
This is a copy
92% identical to agent-eval — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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 Quality Audit
Measures how much CodeGraph helps an agent versus plain grep/read, for a chosen
codegraph version on a chosen real-world repo. Drives the harness in
scripts/agent-eval/.
Prerequisites
tmux3+, a logged-inclaudeCLI,node,git(macOS/Linux).- Run from the codegraph repo root.
Workflow
Copy this checklist:
- [ ] 1. Pick version (local or npm)
- [ ] 2. Pick language
- [ ] 3. Pick repo by size
- [ ] 4. Pick harness (headless / tmux / both)
- [ ] 5. Run audit.sh in the background
- [ ] 6. Report results
Step 1 — version. Ask with AskUserQuestion: which codegraph version to test.
Offer "Local dev build" and "Latest published"; the free-text "Other" lets the
user type a specific version (e.g. 0.7.10). Map the answer to a VERSION token:
- "Local dev build" →
local - "Latest published" →
latest - a typed version → that string (e.g.
0.7.10)
Step 2 — language. Read .claude/skills/agent-eval/corpus.json. Ask with
AskUserQuestion which language to test, listing the languages that have entries.
Step 3 — repo. From the chosen language's entries, ask which repo. Label each
option with its size and file count, e.g. excalidraw — Medium (~600 files).
Each entry carries the repo URL and a representative question.
Step 4 — harness. Ask with AskUserQuestion which harness to run, and map
the answer to a MODE token:
- "Headless" →
headless—claude -pwith stream-json: exact tokens/cost and a clean tool sequence (2 runs, fast, no TTY). - "Interactive (tmux)" →
tmux— drives the real Claude TUI in tmux: faithful Explore-subagent behavior, metrics from session logs (2 runs, slower). - "Both" →
all— headless + interactive (4 runs).
Step 5 — run. Launch in the background (sets the version, clones if missing, wipes + re-indexes, runs the chosen arms — several minutes):
scripts/agent-eval/audit.sh <VERSION> <repo-name> <repo-url> "<question>" <MODE>
Step 6 — report. When the job finishes, read the log and report per arm:
- Headless (
parse-run.mjs): total tool calls, fileReads, Grep/Bash, codegraph-tool calls, duration, total cost. - Interactive (
parse-session.mjs): theVERDICT: codegraph_explore used Nx | Read N | Grep/Bash NandTOKENS:lines.
What ships with it
1 file 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.
- 1mo ago First seen · 75 lines · 65 tokens per session scan A b83c65193e06
agent-eval is a skill published in the GitHub repository andysom25/codegraph (0 stars, last pushed 3mo ago), licensed MIT. It adds 65 tokens to every session and 911 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to agent-eval, differing in 6 lines, and is treated as a copy.
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