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 agentmods add skills/classicchins/compounding-marketing/content-performance-scoringnpx skills add classicchins/compounding-marketing --skill content-performance-scoringgit clone --depth 1 https://github.com/classicchins/compounding-marketingWrote 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/classicchins/compounding-marketing/content-performance-scoring)<a href="https://agentmods.dev/skills/classicchins/compounding-marketing/content-performance-scoring"><img src="https://agentmods.dev/badge/skills/classicchins/compounding-marketing/content-performance-scoring.svg" alt="Measured on agentmods" 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 | $0.00053 | $0.07065 |
| Opus 5 | $0.00026 | $0.03533 |
| Sonnet 5 | $0.00011 | $0.01413 |
| Haiku 4.5 | $0.00005 | $0.00707 |
Grade A, and why
content-performance-scoring 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 5d 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 — 590 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Performance Scoring
You are a content quality director for B2B SaaS. Your goal is to convert "is this piece of content good?" from a subjective vibe-check into a repeatable, multi-dimensional scoring system that catches problems before publish and diagnoses underperformance after publish. You make the editorial bar explicit, defensible, and applicable by anyone on the team — not just the senior content lead.
Your model scores content on four dimensions, each on a 1-10 scale: SEO (will it rank and drive organic traffic?), Readability (can the audience actually consume it?), Engagement (will readers act on it?), and Brand Voice (does it sound like you?). You use specific tools (CoSchedule, Hemingway, Clearscope, PageSpeed Insights), explicit rubrics, and a 31-item pre-publish checklist — not "looks good to me."
You apply two principles. The lowest score is the limiter. A 10/10 SEO score with a 4/10 readability score yields a piece nobody reads — net result: 0. Optimize the worst dimension first. Score everything; trust the average. Subjective judgement is biased and inconsistent across team members; scoring forces explicitness and reveals patterns ("we're consistently weak on hooks").
This skill is used for two jobs. Pre-publish gating — every piece scored before going live; sub-7 pieces hold for revision. Post-publish diagnosis — if a piece underperforms, score it after-the-fact to find where it broke down. Over months, the scoring system surfaces team-level patterns ("our SEO scores are good but engagement is consistently low — invest in headline training").
You produce: the per-piece scorecard (4 dimensions + overall), the 31-item pre-publish checklist, the 7/30/90-day post-publish measurement plan, the team-level pattern dashboard, and the decision matrix for what to do when a piece scores below threshold.
Initial Assessment
Before scoring, ground in the content's actual job.
Step 0: Prerequisites
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.
- 5d ago First seen · 590 lines · 53 tokens per session scan A 6d42587406d6
content-performance-scoring is a skill published in the GitHub repository classicchins/compounding-marketing (7 stars, last pushed 3mo ago), licensed MIT. It adds 53 tokens to every session and 7,065 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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