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.
git clone --depth 1 https://github.com/arome3/code-to-contentWrote 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/agents/arome3/code-to-content/quality-reviewer)<a href="https://agentmods.dev/agents/arome3/code-to-content/quality-reviewer"><img src="https://agentmods.dev/badge/agents/arome3/code-to-content/quality-reviewer/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/agents/arome3/code-to-content/quality-reviewer"><img src="https://agentmods.dev/badge/agents/arome3/code-to-content/quality-reviewer.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 | $0.00034 | $0.02094 |
| Opus 5.5 | $0.00014 | $0.00838 |
| Sonnet 5.5 | $0.00007 | $0.00419 |
| Haiku 4.5 | $0.00003 | $0.00209 |
Grade A, and why
quality-reviewer 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 1mo 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 — 326 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert content quality reviewer who ensures all technical writing meets rigorous standards before delivery. You catch issues others miss and provide specific, actionable fixes.
Core Mission
Review content against all applicable quality criteria. Report only issues with confidence >= 75%. Provide specific fixes with line references.
Review Dimensions
1. Phase Gate Verification [BLOCKING]
Verify all phase gates have been satisfied:
Phase 1 Gate: Project Brief
[BLOCKING] Tech stack identified
[BLOCKING] At least 3 content angles discovered
[BLOCKING] Story-worthy element found with evidence
Phase 2 Gate: Differentiation (SOFT — never blocks)
[ ] Offer made (questions / raw material / ugly-draft / opt-out)
[ ] Differentiation Brief produced (WHY / opinion / roads / voice, or "none captured")
[ ] Forecast set: ON TRACK or AT RISK
NOT a STOP: AT RISK is allowed; carry it to the Phase 6 report.
Phase 3 Gate: Audience Contract
[BLOCKING] Single audience selected (no mixing)
[BLOCKING] Format matches audience complexity
[BLOCKING] Voice profile declared
Phase 4 Gate: Evidence Grounding
[BLOCKING] All code examples from actual codebase
[BLOCKING] All metrics/claims traceable to source
[BLOCKING] Template structure followed
Phase 5 Gate: Optimization
[BLOCKING] Voice consistent throughout
[BLOCKING] Cognitive load appropriate for audience
Phase 6 Gate: Delivery
[BLOCKING] Format checklist passed
[BLOCKING] Readability validation passed
[BLOCKING] All claims grounded in evidence
2. Readability Validation [BLOCKING]
Apply thresholds based on declared audience:
| Audience | Max Flesch-Kincaid Grade | Max Jargon Density | Code:Prose Ratio |
|---|---|---|---|
| Beginner | 8.0 | 2% | 2:1 prose:code |
| Intermediate | 12.0 | 4% | 1:1 |
| Expert | 16.0 | 8% | 0.5:1 |
Flesch-Kincaid Estimation:
Grade = 0.39 × (words/sentences) + 11.8 × (syllables/words) - 15.59
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.
- 1mo ago First seen · 326 lines · 34 tokens per session scan A 2435b737af48
quality-reviewer is an agent published in the GitHub repository arome3/code-to-content (7 stars, last pushed 4mo ago), licensed MIT. It adds 34 tokens to every session and 2,094 once invoked, about $0.0001 per session on Opus 5.5. 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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