Content Decay Detector

What it does

When your agent uses it

• Refresh workflows
• : check for content decay
• Which content needs updating
• Content refresh

Example prompts

• “check for content decay”
• “which content needs updating”
• “content refresh”
• “/content-decay-detector”

Requirements

• Compatibility (from SKILL.md): Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent

Workflow steps

5 steps, taken from the step headings in SKILL.md.

1. 1Gather Content Data
2. 2Check for Decay Signals
3. 3Score Decay Priority
4. 4Generate Refresh Instructions
5. 5Self-Validation

What it can do on your machine

Read from SKILL.md and the folder at commit e43bfae. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are yaml).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

• Compatibility

  Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent

  From compatibility in the SKILL.md frontmatter.

Context cost

Content Decay Detector loads about 2k tokens when it runs. Until then it costs about 119 tokens; SKILL.md has 661 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~119

When it runs · the whole SKILL.md, loaded when a task matches

~2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Affitor/affiliate-skills at commit e43bfae, republished under its MIT licence (© Affitor). 661 words, ~2,039 tokens.

name

content-decay-detector

description

Monitor existing content for ranking drops and trigger refresh workflows. Triggers on: "check for content decay", "which content needs updating", "content refresh", "ranking drops", "traffic decline", "stale content", "content audit", "what to refresh", "outdated content", "content performance check", "update old articles", "declining rankings", "content maintenance", "refresh priority list", "which articles are losing traffic", "SEO content audit".

compatibility

Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent

license

MIT

version

1.0.0

tags

affiliate-marketing, blogging, seo, content-writing, content-audit, decay

metadata.author

affitor

metadata.version

1.0

metadata.stage

S3-Blog

site_url: string              # REQUIRED — the site to audit
                              # e.g., "myblog.com", "example.com/blog"

content_list: object[]        # OPTIONAL — specific pages to check
  - url: string               # Page URL
    title: string             # Page title
    publish_date: string      # Original publish date
    last_updated: string      # Last update date
    target_keyword: string    # Primary keyword

check_competitors: boolean    # OPTIONAL — whether to check if competitors published fresher content
                              # Default: true

timeframe: string             # OPTIONAL — "30d" | "90d" | "6m" | "1y"
                              # Default: "90d"

output_schema_version: "1.0.0"
content_decay:
  site: string
  pages_analyzed: number
  pages_decaying: number
  total_refresh_effort: string  # Estimated total hours

  decaying_pages:
    - url: string
      title: string
      priority: string          # "P0-critical" | "P1-high" | "P2-medium" | "P3-low"
      decay_signals: string[]
      refresh_actions: string[]
      estimated_effort: string
      revenue_impact: string    # "high" | "medium" | "low"

  healthy_pages: string[]       # Pages that don't need refresh

chain_metadata:
  skill_slug: "content-decay-detector"
  stage: "blog"
  timestamp: string
  suggested_next:
    - "affiliate-blog-builder"
    - "seo-audit"
    - "internal-linking-optimizer"
    - "keyword-cluster-architect"

## Content Decay Report: [Site]

### Summary
- **Pages analyzed:** XX
- **Pages decaying:** XX
- **Total refresh effort:** XX hours
- **Estimated traffic recovery:** XX%

### Priority Refresh Queue

#### P0 — Critical (do this week)

**[Page Title]** — [URL]
- Decay: [what's wrong]
- Action: [what to do]
- Effort: [time estimate]
- Impact: [expected result]

#### P1 — High (do this month)
[same structure]

#### P2 — Medium (schedule)
[same structure]

### Healthy Pages (no action needed)
- [Page] — still ranking, content fresh
- [Page] — recently updated

### Monthly Maintenance Schedule
- Week 1: Refresh P0 pages
- Week 2: Refresh P1 pages
- Week 3: Create new content for gaps found
- Week 4: Internal linking review

© Affitor, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/blog/content-decay-detector of Affitor/affiliate-skills.

Open the folder on GitHubat commit e43bfae