Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/MichelKerkmeester/skilled-agent-harness_spec-driven-loopsnpx agentmods add skills/michelkerkmeester/skilled-agent-harness_spec-driven-loops/sk-create-quality-controlWrote 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/michelkerkmeester/skilled-agent-harness_spec-driven-loops/sk-create-quality-control)<a href="https://agentmods.dev/skills/michelkerkmeester/skilled-agent-harness_spec-driven-loops/sk-create-quality-control"><img src="https://agentmods.dev/badge/skills/michelkerkmeester/skilled-agent-harness_spec-driven-loops/sk-create-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/michelkerkmeester/skilled-agent-harness_spec-driven-loops/sk-create-quality-control"><img src="https://agentmods.dev/badge/skills/michelkerkmeester/skilled-agent-harness_spec-driven-loops/sk-create-quality-control.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 | $0.00030 | $0.04557 |
| Opus 5 | $0.00015 | $0.02278 |
| Sonnet 5 | $0.00006 | $0.00911 |
| Haiku 4.5 | $0.00003 | $0.00456 |
Grade A, and why
sk-create-quality-control 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 8d 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 — 474 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Doc Quality (quality)
create-quality-control is the existing-document audit and optimization workflow packet of the sk-doc family. It evaluates markdown, extracts structure, computes Document Quality Index evidence, applies Human Voice Rules, and, only when explicitly requested, edits the same target document to improve structure, clarity and AI-friendliness.
Reach this packet through sk-doc hub routing on a doc-quality request. It carries no slash command: mode-registry.json declares "command": null, no .opencode/commands/doc/ file exists in any runtime, and /doc:quality survives only as historical vocabulary in this packet's trigger list and in the advisor's command-binding allowlist. Whichever way the packet is entered, the run is report-only unless the user explicitly asks for edits.
1. WHEN TO USE
Activation Triggers
Use this workflow when the request involves:
- Auditing an existing markdown document for structure, clarity, quality or publish readiness.
- Running
/doc:qualityon a README, SKILL.md, command doc, knowledge file, spec doc or generic markdown file. - Extracting document structure before deciding what to improve.
- Computing or interpreting a DQI score from
.opencode/skills/sk-doc/shared/scripts/extract_structure.py. - Applying HVR voice checks for AI-pattern cleanup, direct language and natural writing.
- Optimizing existing documentation for AI assistants, question-answering format or practical usage examples.
- Validating an edited markdown document before handoff.
- Auditing or validating an existing README or markdown flowchart when no new README or flowchart is being authored.
Keyword triggers: doc quality, /doc:quality, audit documentation quality, document audit, validate a document, validate markdown, validation rules, score this document, optimize this doc, DQI, HVR, human voice, AI-friendly documentation, extract structure, quality bar, flag, model's budget, trim.
What ships with it
18 files 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.
- changelog/v1.0.0.0.md 1.6 KB
- changelog/v1.0.1.0.md 1.1 KB
- changelog/v1.0.1.1.md 628 B
- changelog/v1.0.2.0.md 3.5 KB
- manual-testing-playbook/audit-and-validation/assess-batch-snapshot.md 6.5 KB
- manual-testing-playbook/audit-and-validation/leave-creation-requests-alone.md 4.8 KB
- manual-testing-playbook/audit-and-validation/run-a-report-only-audit.md 5.4 KB
- manual-testing-playbook/audit-and-validation/validate-structure-before-readiness.md 6.0 KB
- manual-testing-playbook/manual-testing-playbook.md 12 KB
- manual-testing-playbook/optimization-and-voice/optimize-only-when-asked.md 6.4 KB
- manual-testing-playbook/optimization-and-voice/require-evidence-for-dqi.md 5.4 KB
- README.md 9.6 KB
- references/optimization.md 8.5 KB
- references/README.md 3.8 KB
- references/transformation-patterns.md 6.5 KB
- references/validation-and-enforcement.md 7.5 KB
- references/workflow-examples.md 3.1 KB
- references/workflows.md 5.6 KB
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.
- 8d ago First seen · 474 lines · 30 tokens per session scan A 8e7cce8a51b7
sk-create-quality-control is a skill published in the GitHub repository MichelKerkmeester/skilled-agent-harness_spec-driven-loops (35 stars, last pushed yesterday), licensed MIT. It adds 30 tokens to every session and 4,557 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 2026-09-03.
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