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 skills add spences10/my-pi --skill review-harnessgit clone --depth 1 https://github.com/spences10/my-piWrote 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.
[](https://agentmods.dev/skills/spences10/my-pi/review-harness)<a href="https://agentmods.dev/skills/spences10/my-pi/review-harness"><img src="https://agentmods.dev/badge/skills/spences10/my-pi/review-harness/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.
<a href="https://agentmods.dev/skills/spences10/my-pi/review-harness"><img src="https://agentmods.dev/badge/skills/spences10/my-pi/review-harness.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00033 | $0.00420 |
| Opus 5 | $0.00016 | $0.00210 |
| Sonnet 5 | $0.00007 | $0.00084 |
| Haiku 4.5 | $0.00003 | $0.00042 |
Grade A, and why
review-harness 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 12d 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.
What it actually says
Review Harness
Review completed or failed work against the immutable outer policy and the complete versioned scaffold/amendment history.
Workflow
- Resolve the harness directory and read
harness.json,TASK.md,status.json,OUTCOME.mdoroutcome.json, andlogs/events.jsonlif present. - Run or inspect
<harness_dir>/review.sh. - Inspect git diff and changed files.
- Compare changes against
policy.forbidden_paths, the activescaffold.allowed_paths, test policy, escalation rules, and all recorded amendments. - Verify validation evidence from
status.json, logs, and command output. - Decide: pass, needs executor fix, or escalate to planner/user.
Review Checks
- Did work stay inside the outer policy and active scaffold?
- Were scaffold changes authorized, versioned, and honestly recorded?
- Did any amendment attempt to weaken the outer trust boundary?
- Were tests changed when
allow_test_changesis false? - Were validators weakened, deleted, skipped, or bypassed?
- Were outputs faked or hardcoded to satisfy checks?
- Are status and evidence entries honest and complete?
- Does the outcome artifact include changed files, validation evidence, team status, and remaining risks?
- Did the executor make unapproved architecture decisions?
Output Shape
## Verdict
Pass | Needs executor fix | Escalate
## Findings
- P0/P1/P2: <issue> — `<path>` — <required action>
## Validation Evidence
- `<command>` — pass/fail/not run — <evidence>
## Required Next Step
- <one action>
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.
- 12d ago First seen · 60 lines · 33 tokens per session scan A 600943dc786f
review-harness is a skill published in the GitHub repository spences10/my-pi (125 stars, last pushed today), licensed MIT. It adds 33 tokens to every session and 420 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 2026-08-30.
Other skills, from other repositories
pi
Use when needing a minimal, extensible terminal AI coding agent harness in TypeScript with plugin architecture. Pi: minimalist terminal AI coding agent emphasizing simplicity and composability.
request-review
Prepare work for review so a reviewer can verify it fast and fairly. Triggers on get this reviewed, ready for review, request a review, hand off for review.
review
Review code for bugs, quality, and correctness. Triggers on review, check, audit, verify requests.
code-simplify
Reduce complexity and improve readability WITHOUT changing behavior. Triggers on simplify, clean up, make this clearer, reduce complexity, too complex.
gh-find-prs
Survey open Codewhale PRs and triage each for mergeability and disposition against the real landing branch.
tool-prompt-optimization
Optimize the description prompts an AI agent reads to learn its built-in tools (the .md files under prompts/tools/). Two halves: (1) measure how much of a prompt is already inferable from the tool's JSON parameter schema + name, to prune redundancy with evidence; (2) house authoring rules for what belongs in a tool…