A workflow for benchmarking how well OmniWeave helps a coding agent find information in a real codebase. It compares agent runs with and without OmniWeave across selected software languages and repositories.

It is for choosing an OmniWeave version, language, repository, and test harness, then running and reporting an audit.

It provides a structured way to test whether OmniWeave improves code retrieval rather than relying on impressions.

How SolvingLab/OmniWeave configures Claude Code and Codex

Reuse

Borrowing it

Nothing to install: this file belongs to SolvingLab/OmniWeave. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/SolvingLab/OmniWeave/main/.agents/skills/agent-eval/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/SolvingLab/OmniWeave

Made for: Claude Code, Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/solvinglab/omniweave/agent-eval/github.svg)](https://agentmods.dev/skills/solvinglab/omniweave/agent-eval)

Your own site

<a href="https://agentmods.dev/skills/solvinglab/omniweave/agent-eval"><img src="https://agentmods.dev/badge/skills/solvinglab/omniweave/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/solvinglab/omniweave/agent-eval"><img src="https://agentmods.dev/badge/skills/solvinglab/omniweave/agent-eval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 68 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 932 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin 86% copy Near-identical to another mod in the catalogue.

Token cost

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	$0.00068	$0.00932
Opus 5.5	$0.00027	$0.00373
Sonnet 5.5	$0.00014	$0.00186
Haiku 4.5	$0.00007	$0.00093

Measured 1mo ago against content hash 994c0dc16f25, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

Origin

This is a copy

86% identical to agent-eval — 38 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.

.agents/skills/agent-eval/SKILL.md · 75 lines

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.

OmniWeave Quality Audit

Measures how much OmniWeave helps an agent versus plain grep/read, for a chosen omniweave version on a chosen real-world repo. Drives the harness in scripts/agent-eval/.

Prerequisites

tmux 3+, a logged-in Codex CLI, node, git (macOS/Linux).
Run from the omniweave 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 omniweave 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 .Codex/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 — Codex -p with stream-json: exact tokens/cost and a clean tool sequence (2 runs, fast, no TTY).
"Interactive (tmux)" → tmux — drives the real Codex 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, omniweave-tool calls, duration, total cost.
Interactive (parse-session.mjs): the VERDICT: omniweave_explore used Nx | Read N | Grep/Bash N and TOKENS: lines.

Read the full file on GitHub · 75 lines

Files

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.

corpus.json 13 KB

Changes

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 · 68 tokens per session scan A 994c0dc16f25

Subscribe to this mod's changes

agent-eval is a skill published in the GitHub repository SolvingLab/OmniWeave (0 stars, last pushed 4mo ago), licensed MIT. It adds 68 tokens to every session and 932 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to agent-eval, differing in 38 lines, and is treated as a copy.

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