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 curiositech/some_claude_skills --skill very-long-text-summarizationgit clone --depth 1 https://github.com/curiositech/some_claude_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/curiositech/some_claude_skills/very-long-text-summarization)<a href="https://agentmods.dev/skills/curiositech/some_claude_skills/very-long-text-summarization"><img src="https://agentmods.dev/badge/skills/curiositech/some_claude_skills/very-long-text-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/curiositech/some_claude_skills/very-long-text-summarization"><img src="https://agentmods.dev/badge/skills/curiositech/some_claude_skills/very-long-text-summarization.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 | $0.00129 | $0.02132 |
| Opus 5 | $0.00064 | $0.01066 |
| Sonnet 5 | $0.00026 | $0.00426 |
| Haiku 4.5 | $0.00013 | $0.00213 |
Grade A, and why
very-long-text-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 8d 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 — 229 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Very Long Text Summarization
Processes texts too large for a single context window using hierarchical multi-pass extraction with armies of cheap models. Produces structured knowledge maps, indexed summaries, and skill drafts — not just prose compression.
When to Use
✅ Use for:
- Professional handbooks and textbooks (100-1000+ pages)
- Career biographies and memoirs (extracting expertise patterns)
- Large codebases (architecture-level understanding)
- Research paper collections (synthesizing findings across papers)
- Any text exceeding a single context window (~100K tokens)
❌ NOT for:
- Short documents (<10 pages) — just read them directly
- Real-time conversation summarization (use auto-compact patterns)
- Code documentation generation (use
technical-writer) - Simple TL;DR requests (not worth the multi-pass overhead)
Architecture: Three-Pass Hierarchical Extraction
flowchart TD
D[Document] --> C[Chunk into segments]
C --> P1["Pass 1: Haiku army\n(parallel extraction)"]
P1 --> I[Intermediate summaries]
I --> P2["Pass 2: Sonnet synthesis\n(merge + structure)"]
P2 --> S[Structured knowledge map]
S --> P3["Pass 3: Opus refinement\n(optional, for skill drafts)"]
P3 --> O[Final output]
Pass 1: Chunked Extraction (Haiku Army)
Split the document into overlapping chunks (~4K tokens each, 500 token overlap). Deploy one Haiku call per chunk in parallel. Each extracts:
extraction_template:
summary: "2-3 sentence summary of this section"
key_claims: ["list of factual claims or assertions"]
processes: ["any step-by-step procedures described"]
decisions: ["any decision points or heuristics mentioned"]
failures: ["any failures, mistakes, or anti-patterns described"]
aha_moments: ["any insights, realizations, or conceptual breakthroughs"]
metaphors: ["any metaphors or mental models used"]
temporal: ["any 'things changed when...' or 'before X, after Y' patterns"]
quotes: ["notable direct quotes worth preserving"]
references: ["any citations, links, or cross-references"]
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 229 lines · 129 tokens per session scan A 8fd3de632092
very-long-text-summarization is a skill published in the GitHub repository curiositech/some_claude_skills (221 stars, last pushed 6d ago), licensed MIT. It adds 129 tokens to every session and 2,132 once invoked, about $0.0006 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 2026-09-03.
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