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CodeGraph Quality Audit

Free

Benchmark CodeGraph's impact on agent performance.

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Free · Opens the source repo

What CodeGraph Quality Audit does

The CodeGraph Quality Audit skill is designed to evaluate the effectiveness of CodeGraph in enhancing the performance of AI agents when interacting with codebases. By comparing the behavior of an agent using CodeGraph against one that relies solely on traditional methods like grep and manual reading, this skill provides insights into the advantages that CodeGraph offers. It is particularly useful for developers and teams who want to assess how different versions of CodeGraph perform in real-world scenarios.

To use this skill, you will need to follow a structured workflow that involves selecting the CodeGraph version, programming language, and repository size. The skill operates through a series of commands executed in a bash environment, requiring prerequisites such as tmux, a logged-in Claude CLI, Node.js, and Git. The audit process can be run in various modes, including headless and interactive, allowing users to choose how they want to visualize the results.

Once the audit is complete, the skill generates a detailed report that includes metrics such as tool call counts, execution duration, and overall cost. It also provides a side-by-side comparison of the agent's performance with and without CodeGraph, highlighting whether the use of CodeGraph reduced effort and improved accuracy. This makes it a valuable tool for teams looking to optimize their development processes and leverage AI capabilities effectively.

Whether you're a developer evaluating the integration of CodeGraph into your workflow or a team leader assessing the benefits of AI tools, the CodeGraph Quality Audit skill offers a clear methodology for benchmarking and validation. Its structured approach and comprehensive reporting make it an essential addition for anyone working with AI agents in code analysis.

When to use it

Use this skill when you want to benchmark CodeGraph's performance against traditional code analysis methods.

When not to use it

This skill may not be suitable for quick evaluations or when working with small codebases, as it is designed for thorough audits.

What you can build with it

Benchmarking New CodeGraph Versions

Use this skill to test the latest CodeGraph version against a popular codebase to see if it improves agent performance.

Evaluating Agent Performance

Run the audit to compare how well an AI agent performs with CodeGraph versus traditional methods in a real-world scenario.

Optimizing Development Processes

Leverage the insights from the audit to make informed decisions about integrating CodeGraph into your team's workflow.

How to install CodeGraph Quality Audit

View source

1. Install with the skills CLI

npx skills add colbymchenry/codegraph/agent-eval --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by colbymchenry

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" → headlessclaude -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.
  • Both paths also print the three feedback metrics — residual context occupancy, explore sufficiency, allocation efficiency — and a headless A/B ends with a side-by-side ARM COMPARISON table. Report that table, and check its contamination row first: CLI calls that RETURNED output > 0 means the arm reached codegraph through Bash and its numbers are void. How to read the rest: docs/benchmarks/agent-eval-feedback-metrics.md.

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).

Frequently asked questions about CodeGraph Quality Audit

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