claude-blog is a Claude Code skill suite for planning, writing, optimizing, auditing, localizing, and refreshing blog content. It is for content and SEO workflows that produce articles and related publishing artifacts while checking drafts against defined delivery criteria. The catalogue entries provide the skills, agents, plugins, and instruction used by this workflow.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/AgriciDaniel/claude-blognpx agentmods add skills/agricidaniel/claude-blog/blog-decayWrote 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/agricidaniel/claude-blog/blog-decay)<a href="https://agentmods.dev/skills/agricidaniel/claude-blog/blog-decay"><img src="https://agentmods.dev/badge/skills/agricidaniel/claude-blog/blog-decay/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/agricidaniel/claude-blog/blog-decay"><img src="https://agentmods.dev/badge/skills/agricidaniel/claude-blog/blog-decay.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 | $0.00077 | $0.00854 |
| Opus 5 | $0.00039 | $0.00427 |
| Sonnet 5 | $0.00015 | $0.00171 |
| Haiku 4.5 | $0.00008 | $0.00085 |
Grade A, and why
blog-decay 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 11d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Blog Decay
Use /blog decay to find pages whose Google Search Console performance has
declined quarter-over-quarter. The command compares a current-period export
against a previous-period export, flags pages with a 20% or larger decline by
default, and recommends the next content action.
Default Offline Workflow
Run the local analyzer against two GSC page exports:
python3 scripts/content_decay.py current.json previous.json
Useful options:
python3 scripts/content_decay.py current.json previous.json --threshold 0.30
python3 scripts/content_decay.py current.json previous.json --metric impressions
python3 scripts/content_decay.py current.json previous.json --format markdown --output decay-report.md
The script accepts JSON lists of page rows with page or url, clicks, and
impressions. It also accepts the object shape returned by blog-google when
the rows are under a top-level rows key.
Optional Live GSC Export
For live data, use /blog google gsc or the underlying blog-google command
to create each period export, then run the offline analyzer:
python3 skills/blog-google/scripts/run.py gsc_query --property sc-domain:example.com --dimensions page --start-date YYYY-MM-DD --end-date YYYY-MM-DD --json > gsc-current.json
python3 skills/blog-google/scripts/run.py gsc_query --property sc-domain:example.com --dimensions page --start-date YYYY-MM-DD --end-date YYYY-MM-DD --json > gsc-previous.json
python3 scripts/content_decay.py gsc-current.json gsc-previous.json --format markdown
Use adjacent periods of similar length for short-term checks. For seasonality, also run a year-over-year comparison using the same date length, filters, search type, device, country, and property. When possible, inspect up to 16 months of GSC history before diagnosing a traffic drop.
Decay Model
The default metric is clicks. Also review impressions, CTR, average position,
query and page pairs, device, country, and search appearance deltas before
choosing an action.
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.
- 11d ago First seen · 89 lines · 77 tokens per session scan A 7371597aa5fe
blog-decay is a skill published in the GitHub repository AgriciDaniel/claude-blog (2,116 stars, last pushed 7d ago), licensed MIT. It adds 77 tokens to every session and 854 once invoked, about $0.0004 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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