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/zate/cc-plugins/blognpx skills add Zate/cc-plugins --skill bloggit clone --depth 1 https://github.com/Zate/cc-pluginsWhat 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 | $0.00016 | $0.02366 |
| Opus 5 | $0.00008 | $0.01183 |
| Sonnet 5 | $0.00003 | $0.00473 |
| Haiku 4.5 | $0.00002 | $0.00237 |
Grade A, and why
blog 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 2d 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 — 318 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Blog Writer
Create blog posts through a guided conversational workflow. Interviews you about your topic, captures your voice from how you answer, writes a draft matching your tone, removes AI patterns, and publishes as markdown or HTML.
Quick Start
If $ARGUMENTS is provided, use it as the initial topic. Otherwise, start by asking for a topic in Phase 1.
Phase 1: Interview
Goal: Understand the topic AND capture how the user naturally communicates.
CRITICAL: Pay close attention to HOW the user answers — not just WHAT they say. Their word choices, sentence length, formality, humor, directness, and vocabulary ARE the voice profile.
Ask questions one group at a time using AskUserQuestion, then follow up with open-ended questions to capture natural voice.
Step 1a: Topic & Audience
AskUserQuestion:
questions:
- question: "What's this blog post about? Give me the elevator pitch."
header: "Topic"
multiSelect: false
options:
- label: "Technical tutorial"
description: "How-to, walkthrough, or guide"
- label: "Opinion / hot take"
description: "Your perspective on something in your field"
- label: "Story / experience"
description: "Something that happened, lessons learned"
- label: "Announcement / update"
description: "Product launch, project update, news"
- question: "Who are you writing this for?"
header: "Audience"
multiSelect: false
options:
- label: "Developers / technical"
description: "People who write code"
- label: "Business / leadership"
description: "Decision makers, managers"
- label: "General / mixed"
description: "Broad audience, no assumed expertise"
- label: "Community / peers"
description: "People in your specific niche"
Step 1b: Voice Capture Questions
These are open-ended to capture natural writing voice. Ask via AskUserQuestion with options that encourage free-text responses.
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.
- 2d ago First seen · 318 lines · 16 tokens per session scan A 8e434e573fc5
blog is a skill published in the GitHub repository Zate/cc-plugins (10 stars, last pushed 1mo ago), licensed MIT. It adds 16 tokens to every session and 2,366 once invoked, about $0.0001 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.
Other skills, from other repositories
systematic-debugging
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brainstorming
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auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…