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 manhua-man/harness-coding-protocol --skill pipeline-quality-auditgit clone --depth 1 https://github.com/manhua-man/harness-coding-protocolWrote 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/manhua-man/harness-coding-protocol/pipeline-quality-audit)<a href="https://agentmods.dev/skills/manhua-man/harness-coding-protocol/pipeline-quality-audit"><img src="https://agentmods.dev/badge/skills/manhua-man/harness-coding-protocol/pipeline-quality-audit/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/manhua-man/harness-coding-protocol/pipeline-quality-audit"><img src="https://agentmods.dev/badge/skills/manhua-man/harness-coding-protocol/pipeline-quality-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00056 | $0.01218 |
| Opus 5 | $0.00028 | $0.00609 |
| Sonnet 5 | $0.00011 | $0.00244 |
| Haiku 4.5 | $0.00006 | $0.00122 |
Grade A, and why
pipeline-quality-audit 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 9d 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pipeline Quality Audit
Judge the delivered media, not the command exit code. Prefer a recovery-first pipeline that returns a usable artifact and an observable account of repairs over one that rejects recoverable inputs and sends operational work back to the user.
Outcome contract
Define the minimum usable result from project evidence before changing the pipeline:
- media type, dimensions/aspect ratio, duration, frame rate, and required streams;
- content requirements such as a visible subject, intentional motion, continuity, or style anchors;
- consumer path that must be able to open or retrieve the artifact;
- acceptable automatic repairs, retries, fallbacks, and quality degradation.
Do not invent requirements that the caller or product does not have. A static shot, dark frame, missing audio track, or unusual aspect ratio is not a defect unless it violates the actual contract.
Recovery-first workflow
Use this order:
detect
-> normalize or repair locally
-> retry the failed stage with bounded changed inputs
-> use a declared fallback or lower-cost profile
-> quarantine the unusable attempt and retain diagnostics
-> terminal non-delivery only when no valid artifact can be produced
For every automatic action, record the attempt, observed defect, repair/fallback chosen, resulting artifact, and final consumer probe. Do not report a repaired failure as a clean first-attempt success.
1. Detect from actual output
- Probe the produced file with stable tools such as
ffprobe, decoder reads, or image statistics. - Black-frame detection may use ffmpeg
blackdetect:pix_this the darkness threshold andpic_this the proportion of dark pixels. Treat thresholds as project-calibrated signals, not universal truth. - Motion or duplicate-frame checks must exempt intentionally static content.
- Validate the final delivery contract, not only an intermediate render.
- Use numeric metrics to locate suspicious segments; use the product contract or visual review to decide whether the content is acceptable.
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.
- 9d ago First seen · 100 lines · 56 tokens per session scan A 04b919cae62d
pipeline-quality-audit is a skill published in the GitHub repository manhua-man/harness-coding-protocol (5 stars, last pushed yesterday), licensed MIT. It adds 56 tokens to every session and 1,218 once invoked, about $0.0003 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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