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 synthesisengineering/synthesis-skills --skill synthesis-content-qualitygit clone --depth 1 https://github.com/synthesisengineering/synthesis-skillsWrote 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/synthesisengineering/synthesis-skills/synthesis-content-quality)<a href="https://agentmods.dev/skills/synthesisengineering/synthesis-skills/synthesis-content-quality"><img src="https://agentmods.dev/badge/skills/synthesisengineering/synthesis-skills/synthesis-content-quality/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/synthesisengineering/synthesis-skills/synthesis-content-quality"><img src="https://agentmods.dev/badge/skills/synthesisengineering/synthesis-skills/synthesis-content-quality.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 | $0.00139 | $0.08832 |
| Opus 5 | $0.00069 | $0.04416 |
| Sonnet 5 | $0.00028 | $0.01766 |
| Haiku 4.5 | $0.00014 | $0.00883 |
Grade A, and why
synthesis-content-quality 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 10d 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 — 444 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Quality
A systematic methodology for evaluating writing quality and identifying slop, with or without AI involvement. The framework targets bad content, not provenance. Ethically authored AI-collaborated content can be excellent; styled empty human content is slop. This skill detects slop.
The methodology is durable. The catalog refreshes as model behavior shifts and as new patterns emerge in production output. v4.0 adds model-family fingerprinting across eight families, a substance and depth section grounded in the Frankfurt-Pennycook-Hicks-Humphries-Slater framework, a cross-cutting causal-and-calibration layer, and zone-conditional detection. The compounding-archive principle means patterns are never deleted: when newer model versions train a pattern out, the catalog tags it Historical and retains it for forensic analysis of older published content.
Where this skill fits in the writing-quality family
This skill catches AI-generation patterns and substance failures specifically. Three sibling skills handle adjacent concerns:
synthesis-content-quality(this skill, v4.0): AI/LLM-generation patterns, substance and depth, calibration. Refreshes with new model releases.synthesis-writing-pitfalls: Universal human-source bad-writing patterns (cringe, throat-clearing, caveat overload, cliché reliance). Stable across decades.synthesis-writing-craft: Positive principles from the writing-craft tradition.
Use all three together for a comprehensive quality pass. Use this one alone when the focus is specifically slop in AI-collaborated or AI-generated content.
When to Use This Skill
- Reviewing AI-assisted drafts before publication.
- Editing content that may contain unrevised AI output.
- Building or calibrating AI content detection tools.
- Training writers or editors on content quality standards.
- Performing editorial review of submitted content.
- Forensic analysis of older published content for AI authorship signals (use Historical and Deprecated era patterns).
What ships with it
17 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.
- agents/openai.yaml 262 B
- references/bibliography.md 25 KB
- references/calibration-tables.md 62 KB
- references/combined-signal-fingerprints.md 92 KB
- references/current-model-candidates.md 4.6 KB
- references/detailed-criteria.md 208 KB
- references/historical-patterns.md 62 KB
- references/model-family-fingerprints.md 240 KB
- references/philosophy-and-application.md 8.5 KB
- references/substance-and-depth.md 54 KB
- scripts/corpus_repetition.py 17 KB runs code
- tests/fixtures/writing_quality_evidence_corrections.json 27 KB
- tests/fixtures/writing_quality_no_removals_baseline.json 526 KB
- tests/generate_no_removals_baseline.py 2.3 KB runs code
- tests/test_additive_upgrade_contract.py 2.6 KB runs code
- tests/test_corpus_repetition.py 13 KB runs code
- tests/test_no_removals.py 6.4 KB runs code
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.
- 10d ago First seen · 444 lines · 139 tokens per session scan A 6c0b9a898972
synthesis-content-quality is a skill published in the GitHub repository synthesisengineering/synthesis-skills (18 stars, last pushed today), licensed Apache-2.0. It adds 139 tokens to every session and 8,832 once invoked, about $0.0007 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.
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