harvest-adaptive
Adaptive content summarization - auto-detect content type and produce relevant summary
Best use case
harvest-adaptive is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Adaptive content summarization - auto-detect content type and produce relevant summary
Teams using harvest-adaptive should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/harvest-adaptive/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How harvest-adaptive Compares
| Feature / Agent | harvest-adaptive | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Adaptive content summarization - auto-detect content type and produce relevant summary
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Harvest Adaptive (Digest) Automatically detect content type and produce the most useful summary. A blog post gets key takeaways. A library gets feature evaluation. Documentation gets structure overview. API reference gets endpoint catalog. ## Usage ``` /digest <url> ``` ## Examples ```bash # Evaluate a library /digest https://github.com/owner/cool-library # Summarize an article /digest https://blog.example.com/microservices-patterns # Overview documentation /digest https://docs.example.com ``` ## Content Type Detection | Type | Signals | Output Style | |------|---------|-------------| | Library/Tool | GitHub repo, npm page, PyPI | Feature matrix, ecosystem health, recommendation | | Blog/Article | Single long-form content | Key takeaways, relevance assessment | | Documentation | Multi-page, navigation structure | Structure map, coverage assessment, quality | | API Reference | Endpoints, methods, params | Endpoint catalog, auth info, SDK availability | | Comparison | Tables, vs, alternatives | Feature matrix, winner per category | | Tutorial | Step-by-step, code blocks | Prerequisites, steps summary, outcome | ## Output Templates ### Library Evaluation ```markdown # Library: [name] > Source: [URL] | Stars: [X] | Last commit: [date] ## What It Does [1-2 sentence description] ## Key Features - Feature 1 - Feature 2 ## Ecosystem Health | Metric | Value | |--------|-------| | GitHub Stars | X | | Weekly Downloads | X | | Last Release | date | | Open Issues | X | | Contributors | X | ## API Surface [Key classes/functions/exports] ## Verdict [Use when... Don't use when... Alternatives: ...] ``` ### Article Summary ```markdown # Summary: [title] > Source: [URL] | Author: [name] | Date: [date] ## Key Takeaways 1. [Point 1] 2. [Point 2] 3. [Point 3] ## Relevance [How this applies to current project/context] ## Action Items - [What to do based on this] ``` ## Rules - Auto-detect, don't ask user for content type - Keep summaries under 500 words - Always include source URL and date - For libraries: check last commit date, flag if > 6 months - For articles: check publication date, flag if > 1 year - Confidence rating on every assessment
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