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Skills/colbymchenry/codegraph/agent-eval
agent-eval logo

agent-eval

colbymchenry/codegraph
741 installs49K stars
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Installation

npx skills add https://github.com/colbymchenry/codegraph --skill agent-eval

Summary

Benchmark CodeGraph retrieval quality on a real codebase by comparing agent behavior with vs without CodeGraph. Use when the user runs /agent-eval or asks to test, benchmark, audit, or validate a codegraph version (the local dev build or a published npm version) against a language's repo.

SKILL.md

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

  • tmux 3+, a logged-in claude CLI, 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 -p with 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, file Reads, Grep/Bash,

codegraph-tool calls, duration, total cost.

  • Interactive (parse-session.mjs): the `VERDICT: codegraph_explore used Nx |

Read N | Grep/Bash N and TOKENS:` lines.

Lead with cost + tool/Read counts — they are the reliable signals; raw token in/out are confounded by subagent delegation and prompt caching. State whether codegraph reduced effort and whether both arms reached a correct answer.

Notes

  • The index is rebuilt every run (audit.sh wipes .codegraph) — different

versions extract differently, so an index must be served by the same binary that built it.

  • audit.sh temporarily mutates the global codegraph install for the test,

then restores your dev link via local-install.sh.

  • Corpus repos are cloned to /tmp/codegraph-corpus (reused if already present).
  • Add or edit repos in corpus.json (fields: name, repo, size, files,

question).

Score

0–100
65/ 100

Grade

C

Popularity17/30

741 installs — growing adoption. Source repo has 49,116 GitHub stars.

Completeness27/30

Documented: full SKILL.md body, description, one-line install. Missing: category/license metadata.

Trust15/25

Community skill with a public GitHub source repository you can review.

Freshness6/15

No update timestamp is tracked for this skill in our catalog.

Scored automatically from popularity, completeness, trust, and freshness — computed only from data in our catalog, never fabricated.

Proud of your score? Add this badge to your README.

Paste a snippet into your GitHub README. The badge updates automatically and links back to this page.

Agent Eval skill score badge previewScore badge

Markdown

[![Agent Eval skill](https://www.claudemarket.ai/skills/colbymchenry/codegraph/agent-eval/badges/score.svg)](https://www.claudemarket.ai/skills/colbymchenry/codegraph/agent-eval)

HTML

<a href="https://www.claudemarket.ai/skills/colbymchenry/codegraph/agent-eval"><img src="https://www.claudemarket.ai/skills/colbymchenry/codegraph/agent-eval/badges/score.svg" alt="Agent Eval skill"/></a>

Agent Eval FAQ

How do I install the Agent Eval skill?

Run “npx skills add https://github.com/colbymchenry/codegraph --skill agent-eval” in your terminal. The skill is added to your agent's skills directory and picked up automatically on the next run — no restart or extra configuration needed.

What does the Agent Eval skill do?

Benchmark CodeGraph retrieval quality on a real codebase by comparing agent behavior with vs without CodeGraph. Use when the user runs /agent-eval or asks to test, benchmark, audit, or validate a codegraph version (the local dev build or a published npm version) against a language's repo. The full SKILL.md on this page shows the exact instructions the skill gives your agent.

Is the Agent Eval skill free?

Yes. Agent Eval is a free, open-source skill published from colbymchenry/codegraph. As with any third-party skill, review the source repository before installing it into an agent with sensitive access.

Does Agent Eval work with Claude Code and OpenClaw?

Yes. Skills use the portable SKILL.md format, so Agent Eval works with Claude Code, OpenClaw, Codex, Hermes, and any other agent that reads SKILL.md skills.

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