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/doidor/agentrig/harness-evalnpx skills add doidor/agentrig --skill harness-evalgit clone --depth 1 https://github.com/doidor/agentrigWhat 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 | $0.00048 | $0.01196 |
| Opus 5 | $0.00024 | $0.00598 |
| Sonnet 5 | $0.00010 | $0.00239 |
| Haiku 4.5 | $0.00005 | $0.00120 |
Grade A, and why
harness-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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
harness-eval (principle 6 — evaluate the harness itself)
A harness you cannot measure is a harness you cannot improve. This skill scores the harness on
three complementary layers and writes results to .agentrig/eval/results/ (validated on write
and on read; never hand-edit JSON).
Layer A1 — install completeness (deterministic, no model)
Every canonical artifact present at the path the manifest declares.
node .agentrig/eval/static-audit.mjs --json # Install Completeness %
Layer A2 — quality probes (deterministic, no model)
Cheap content sanity: YAML parseable, no unfilled {{PLACEHOLDER}} in AGENTS.md, every skill has
the required frontmatter, axes.json has an issue code per axis, developer/reviewer model
families differ (not just the model id strings).
A1 + A2 are what CI gates on. Both surface in the same --static report under "Layer A1" and
"Layer A2" sections.
Layer B — dynamic behavioral eval (agentic, independent judge, fixture-based)
For each scenario in .agentrig/eval/scenarios/*/:
- Seed a throwaway worktree from
scenarios/<id>/fixture/(orbaseline/+change/for review scenarios). - Producer model runs in that worktree against
scenarios/<id>/prompt.md. For--variant harness, the AgentRig harness is staged into the worktree first; for--variant baseline, the agent runs bare. - Oracle (
scenarios/<id>/oracle.yml) deterministically scores the hard axes (correctness, tests, scope, regression_risk, …) by running commands / inspecting the diff. No LLM. - Judge model — explicitly a different family from the producer — runs in a separate
provider.startConversation()call in its own cwd containing onlyprompt.md,diff.patch,transcript.md,oracle.json, andjudge_brief.md. It does NOT see the producer worktree or reasoning trace. It writes<artifactsDir>/<scenario>.trial<N>.judge.json; the orchestrator reads, validates, and persists viascore.mjs save.
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.
- 2d ago First seen · 93 lines · 48 tokens per session scan A 7c0df358b52f
harness-eval is a skill published in the GitHub repository doidor/agentrig (5 stars, last pushed 1mo ago), licensed MIT. It adds 48 tokens to every session and 1,196 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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