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/solvinglab/omniweave/agent-evalnpx skills add SolvingLab/OmniWeave --skill agent-evalgit clone --depth 1 https://github.com/SolvingLab/OmniWeaveWhat 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 | $0.00068 | $0.00932 |
| Opus 5 | $0.00034 | $0.00466 |
| Sonnet 5 | $0.00014 | $0.00186 |
| Haiku 4.5 | $0.00007 | $0.00093 |
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 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.
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.
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
tmux3+, a logged-inCodexCLI,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 -pwith 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, fileReads, Grep/Bash, omniweave-tool calls, duration, total cost. - Interactive (
parse-session.mjs): theVERDICT: omniweave_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.
- yesterday First seen · 75 lines · 68 tokens per session scan A 994c0dc16f25
agent-eval is a skill published in the GitHub repository SolvingLab/OmniWeave (0 stars, last pushed 2mo ago), licensed MIT. It adds 68 tokens to every session and 932 once invoked, about $0.0003 per session on Opus 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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