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 skills add h4vzz/awesome-ai-agent-skills --skill summarizationgit clone --depth 1 https://github.com/h4vzz/awesome-ai-agent-skillsWrote 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/h4vzz/awesome-ai-agent-skills/summarization)<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/summarization"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/summarization/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/h4vzz/awesome-ai-agent-skills/summarization"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/summarization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00030 | $0.02017 |
| Opus 5 | $0.00015 | $0.01009 |
| Sonnet 5 | $0.00006 | $0.00403 |
| Haiku 4.5 | $0.00003 | $0.00202 |
Grade A, and why
summarization 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 9d 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.
This is a copy
95% identical to summarization — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Summarization
This skill enables an AI agent to condense long-form text into clear, accurate summaries. The agent supports multiple summarization strategies — extractive (selecting key sentences verbatim), abstractive (rewriting in new words), hierarchical (layered summaries at different detail levels), and multi-document (synthesizing across several sources). The skill is designed for technical documents, meeting notes, research papers, articles, and any text where readers need the core information without reading the full content.
Workflow
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Analyze the Input: Determine the type, length, and structure of the source material. Identify whether it is a single document or multiple documents, whether it has clear sections (headings, chapters) or is unstructured prose, and what domain it belongs to. This determines which summarization strategy to apply.
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Select the Summarization Strategy: Choose the approach best suited to the input and the user's needs. Use extractive summarization for factual or legal texts where exact wording matters. Use abstractive summarization for general content where readability and brevity are priorities. Use hierarchical summarization when the user needs both a one-line TLDR and a detailed breakdown. Use multi-document summarization when synthesizing across several inputs.
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Identify Key Information: Regardless of strategy, identify the core claims, findings, decisions, action items, and supporting data in the source. Rank information by importance using signals like: position in the document (introductions and conclusions carry weight), frequency of mention, explicit markers ("importantly," "in conclusion"), and relevance to the user's stated purpose.
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Generate the Summary: Produce the summary at the requested length and detail level. Preserve factual accuracy — never introduce information not present in the source. Maintain the source's logical structure. For hierarchical summaries, produce three tiers: a one-sentence TLDR, a short paragraph (3-5 sentences), and a detailed section-by-section breakdown.
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.
- 9d ago First seen · 115 lines · 30 tokens per session scan A 8716b9b9d68e
summarization is a skill published in the GitHub repository h4vzz/awesome-ai-agent-skills (34 stars, last pushed today), licensed MIT. It adds 30 tokens to every session and 2,017 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to summarization, differing in 2 lines, and is treated as a copy.
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