# anycap-blog-production

Turns raw notes, spreadsheets, benchmarks, or JSON into technical articles and guides in the AnyCap voice. Creates and validates visual proof blocks via CLI when needed.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 646 installs · ★ 45
- Author: anycap-ai
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/anycap-ai/anycap/tree/HEAD/skills/anycap-blog-production/SKILL.md
- Updated: 2026-10-21
- HTML: https://affpapa.org/en/nevibecode/skills/anycap-blog-production

## What it does

This skill turns raw inputs into technical content. It takes JSON payloads, tables, benchmark results, or bullet points and compiles them into a working brief. From there, the agent writes an article with an upfront answer block, a concrete H1 promise, and practical sections such as workflows or checklists.

When an argument needs visual proof, the skill calls AnyCap CLI commands. It checks model schemas, renders 16:9 assets into `web/public/content-evidence`, and validates the outcome using `anycap actions image-read`. If a section reads clearly without media, it skips visual assets entirely.

Without a functioning `anycap` CLI binary and internet access, asset generation commands will fail.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add anycap-ai/anycap --skill anycap-blog-production -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/anycap-blog-production/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/anycap-blog-production/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add anycap-ai/anycap --skill anycap-blog-production -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/anycap-blog-production/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add anycap-ai/anycap --skill anycap-blog-production -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/anycap-blog-production/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install anycap-ai/anycap anycap-blog-production
```

2. **Manually: put the files in place**
- File: `.github/skills/anycap-blog-production/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add anycap-ai/anycap --skill anycap-blog-production -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/anycap-ai/anycap --path skills/anycap-blog-production
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/anycap-blog-production/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add anycap-ai/anycap --skill anycap-blog-production -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/anycap-ai/anycap/tree/HEAD/skills/anycap-blog-production/SKILL.md

## When to use it
- turn raw JSON exports and benchmarks into structured technical posts
- draft tutorial and glossary pages from product notes
- generate and verify visual proof assets with the anycap CLI
- structure answer-first articles without generic marketing fluff

## Example prompts
- Convert this JSON benchmark file into an AnyCap-style technical post.
- Draft a tutorial from my feature notes and generate supporting diagram images via CLI.
- Write a glossary entry based on these product facts with a clear summary at the top.

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 0f0e08d64a25f9ea.

BEGIN THIRD-PARTY FILE 0f0e08d64a25f9ea
````text
---
name: anycap-blog-production
description: "Turn user-provided data, notes, research, product facts, or structured inputs into AnyCap-style blog posts and then enrich those posts with first-party evidence blocks when needed. Use when a user wants a blog, article, tutorial, learn page, or SEO content draft written in the tone of the AnyCap website. Best fit when the input is data-first rather than prose-first: spreadsheets, JSON, bullets, notes, source URLs, benchmarks, product facts, or rough research. Trigger on: write blog from data, turn notes into article, blog drafting, article production, learn page drafting, SEO article in AnyCap tone, or requests to convert structured inputs into publish-ready content."
metadata:
  version: 0.6.5
  website: https://anycap.ai
  compatibility: Requires anycap CLI binary and internet access. Best for AnyCap website content, especially blog, learn, guide, glossary, and landing-page style articles authored from structured inputs.
license: MIT
---

# AnyCap Blog Production

> **Read this entire file before starting.** This skill is for turning user-fed data into AnyCap-style articles, then adding evidence where the article needs proof.

Use this skill when the user provides facts, notes, examples, benchmarks, product capabilities, or other raw inputs and wants a finished blog post that sounds like the AnyCap website. The core job is:

1. normalize the input data into a working brief
2. draft a page in AnyCap's website tone
3. decide whether the article needs first-party evidence blocks
4. add AnyCap-generated visuals only when they materially improve the page

This skill is about **blog production workflow**. For raw CLI syntax, authentication, and command behavior, read the `anycap-cli` skill. For broader search-intent planning, read the `anycap-ai-tool-seo` skill.

Read these reference files before drafting:
- [voice.md](references/voice.md)
- [patterns.md](references/patterns.md)
- [input-brief.md](references/input-brief.md)

## Best Fit

- data-driven blog drafting
- learn pages and tutorials built from research notes
- glossary or concept pages built from product facts and supporting examples
- SEO articles where the user feeds benchmarks, examples, or feature data first
- content workflows where the input is bullets, tables, JSON, spreadsheets, links, or rough notes
- AnyCap website content that should sound operational, answer-first, and product-literate instead of generic content-marketing prose

## Before You Start

1. Verify AnyCap is available:

```bash
anycap status
```

2. Inspect the page structure before editing:
   - find the base page
   - find the localized wrapper
   - check whether localized or duplicated routes simply re-export the base page

3. Normalize the input into a brief:
   - what is the article topic?
   - who is it for?
   - what does the data actually prove?
````
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