content-effort-audit
Score visible effort evidence on specified URLs.
Skill by narayan-metaflow
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/narayan-metaflow-content-effort-audit
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About
Use cases: security, writing, marketing
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 26, 2026.
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Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/content-effort-audit
What's inside
- Skill name
content-effort-audit- Description
- Score visible effort evidence on specified URLs. Use when the user asks for a Content Effort audit, rater-style effort scoring, low-effort AI content review, or whether a page would look Lowest under QRG §3.2. Distinct from library keep/refresh/prune (seo-aeo-content-strategy) and from on-page ski-ramp implementation (on-page-seo-aeo-optimization).
- License
- MIT
- Instructions
- 54 lines in SKILL.md · view on GitHub
Files (3)
- SKILL.md
- vee-rubric.md · references/
- worked-examples.md · references/
SKILL.md (first lines)
# Content Effort Audit Score **visible effort** on finished URLs. Production hours, cost, and process do not count unless they left residue a stranger can point at. ## Why this matters Google’s Search Quality Evaluator Guidelines judge Main Content quality by **effort, originality, and talent or skill**, plus accuracy on informational/YMYL pages. 2025 updates made “little to no effort, originality, and added value” a **Lowest** path, including AI paraphrase and scaled content abuse. Generative AI **alone** does not determine the rating. Three evidence layers stay separate: 1. **QRG effort** — documented rater criterion (primary source). 2. **Leaked `contentEffort`** — reported LLM estimate for article pages. Mapping it to QRG is **inference**, not a Google statement. 3. **People-first questions** — original information, first-hand expertise, not just rewriting sources. Do not brief “Content Effort is a confirmed ranking factor.” Operate as if visible effort is industrially scored. Label the leak as speculation. Cross-links: library keep/refresh/prune → `seo-aeo-content-strategy`. Implementing effort adds on the page → `on-page-seo-aeo-optimization`. ## Process 1. **Classify page purpose.** Recipe, calculator, review, explainer, clip, documentary — the bar is relative to purpose, not word count. 2. **Gather visible evidence only.** First-party facts, original artifacts, synthesis, craft, curation choices, added value vs substitutes. Ignore time logs, “human review” checkboxes, and origin stories that are not on the URL. 3. **Score six signals, 0–2 each (0–12 total).** Use `references/vee-rubric.md`. Do not invent Google’s weights. 4. **Map to QRG language.** 0–3 Lowest-risk evidence, 4–6 Low, 7–9 High, 10–12 Highest-evidence. Then apply the purpose modifier. 5. **Recommend specific effort adds.** A zero on first-party information is a measurement or primary document, not “write more.” See the rubric’s add column. 6. **Flag site-level scaled low-effort** only after sampling 3+ URLs that share a skeleton. One thin URL is a page problem. 7. **Hand off implementation** to on-page SEO/AEO for ski-ramp, titles, and schema. This skill stops at the effort verdict. ## Output Per URL: | Field | Required | |---|---| | Purpose class | Yes | | Six-signal table (0–2) | Yes | | Total /12 and band | Yes | | QRG-aligned risk (Lowest / Low / High evidence) | Yes | | Missing-evidence list | Yes | | Prioritized effort adds | Yes | | Leak column labeled inference | Yes | | Scaled low-effort flag | Optional, after 3+ URL sample | Worked pair: `references/worked-examples.md`. ## Rules - Visible effort ≠ production effort. - Origin (human vs AI) ≠ rating. - Word count is not a signal. - Do not coin a fake “Google Content Effort score.” - Do not collapse this audit into E-E-A-T or keep/refresh/prune.
From narayan-metaflow/metaflow-marketing-skills, MIT.
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Signals
- Stars
- 15
- Forks
- 2
- Open issues
- 0
- Last commit (this folder)
- Sep 16, 2026 (1 month ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
content-effort-audit skill. Agent Mart. https://agentmart.dev/skills/narayan-metaflow-content-effort-audit (last updated 2026-10-24)