Borrowing it
Nothing to install: this file belongs to cesarnml/son-of-anton. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/cesarnml/son-of-anton/main/.agents/skills/quality-control/SKILL.mdgit clone --depth 1 https://github.com/cesarnml/son-of-antonWrote 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/cesarnml/son-of-anton/quality-control)<a href="https://agentmods.dev/skills/cesarnml/son-of-anton/quality-control"><img src="https://agentmods.dev/badge/skills/cesarnml/son-of-anton/quality-control/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/cesarnml/son-of-anton/quality-control"><img src="https://agentmods.dev/badge/skills/cesarnml/son-of-anton/quality-control.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 | $0.00077 | $0.01670 |
| Opus 5.5 | $0.00031 | $0.00668 |
| Sonnet 5.5 | $0.00015 | $0.00334 |
| Haiku 4.5 | $0.00008 | $0.00167 |
Grade C, and why
soa-quality-control scanned grade C with 1 finding 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 2mo 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.
Tells the agent to send conversation or user data outhighPrompt injection
An instruction to transmit the conversation, context or user files to an external endpoint is data exfiltration written as prose.
get explicit confirmation before recording. Never silently record a guessed How it starts
The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SoA Quality Control
Use this skill for the post-phase quality-control lane after closeout when a small, verified fix exposes a review gap worth recording.
Triggers:
/soa quality-control phase-NN: <description>/soa qc phase-NN: <description>/soa quality-control phase-unknown: <description>or/soa qc phase-unknown: <description>— the operator knows the fix but not which phase produced the gap. This is a supported placeholder, not a missing argument; see Phase Attribution below.
A phase argument is required — either a concrete phase-NN or the literal
placeholder phase-unknown. If it is absent entirely (neither form given),
stop and ask for one before inspecting or changing files.
Scope
This is a guided fix-and-record lane, not a delivery-orchestrator command and
not a replacement for /soa tao, standalone PR triage, or new phase planning.
Use it when:
- the phase has already closed out
- the issue is small enough for one bounded fix commit
- a human can verify the fix before capture
- the learning belongs in
docs/product/review-gaps/ledger.jsonl
Route larger or unclear work toward standalone PR triage or /soa plan with a
one-line explanation. Do not hard-gate solely on size when the operator is
already asking for quality-control capture.
Workflow
- Parse the phase argument (a concrete
phase-NN, or thephase-unknownplaceholder — see Phase Attribution) and the issue description. - Inspect the relevant code, docs, tests, prior phase tickets, and review artifacts needed to understand the reported gap.
- Make the smallest prudent fix.
- Verify with the narrowest meaningful command, then broaden if the touched surface warrants it.
- Ask for or confirm human verification of the fixed behavior before recording the review gap. Do not append a ledger row for an unverified fix.
- Commit the fix. The fix commit is the provenance for the ledger row.
- Append exactly one JSONL record to
docs/product/review-gaps/ledger.jsonlusing the review-gap ledger helper contract fromtools/delivery/review-gap-ledger.ts. Treat this as therecord-review-gapstep: one verified fix, one commit, one ledger line. Prefer the optional rich-capture fields (problem,solution,defectClass,testReachability,recurrence,id) whenever the fix exposes reusable learning — a one-linesummaryalone discards the experiential detail that makes the ledger valuable for future planning and review prompts. - If the learning may deserve future prompt or process changes, add a concise
candidate to
docs/product/review-gaps/promotion-queue.md. - Run formatting and the relevant verification command after editing the ledger or promotion queue.
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.
- 2mo ago First seen · 141 lines · 0 tokens per session scan C c1d6d9fcfe72
soa-quality-control is a skill published in the GitHub repository cesarnml/son-of-anton (2 stars, last pushed 3mo ago), licensed MIT. It adds 77 tokens to every session and 1,670 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it C with 1 finding (tells the agent to send conversation or user data out). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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