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 gpsaggese/helpers --skill text.extract_ideasgit clone --depth 1 https://github.com/gpsaggese/helpersWrote 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/gpsaggese/helpers/text.extract_ideas)<a href="https://agentmods.dev/skills/gpsaggese/helpers/text.extract_ideas"><img src="https://agentmods.dev/badge/skills/gpsaggese/helpers/text.extract_ideas/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/gpsaggese/helpers/text.extract_ideas"><img src="https://agentmods.dev/badge/skills/gpsaggese/helpers/text.extract_ideas.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 | $0.00008 | $0.00565 |
| Opus 5.5 | $0.00003 | $0.00226 |
| Sonnet 5.5 | $0.00002 | $0.00113 |
| Haiku 4.5 | $0.00001 | $0.00056 |
Grade A, and why
text.extract_ideas 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 yesterday.
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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Goal
- You are an expert reader and critical thinker
- Print each step before executing it
Workflow
Read the Given Text
- Extract the most thought-provoking, surprising, and intellectually interesting ideas from the text below
- Focus on:
- Ideas that challenge common assumptions or conventional wisdom
- Counterintuitive, paradoxical, or unexpected concepts
- Insights with deep implications (philosophical, scientific, social, or psychological)
- Elegant, novel, or powerful ways of explaining something
- Hidden patterns, connections, or underlying themes
Extract Ideas
-
For each idea, write bullets following
.claude/skills/markdown.rules.mdand.claude/skills/text.rules.md -
Create a header 1 with a short summary
# <ID>. Creative Destruction as Generative Force -
State the idea clearly and concisely
**Idea**: ... -
Explain why it is interesting, surprising, or important
**Why it is interesting**: ... -
(Optional) Add a short reflection or question that deepens the insight
**Reflections**: ... -
E.g.,
# 1. Pre-training as "Crappy Evolution" - **Idea** - ... - ... - **Why it's interesting**: - Pre-training LLMs on internet data creates a practical shortcut to biological evolution: encoding vast human knowledge into neural weights - Not true evolution (doesn't run on biology), but achieves similar bootstrapping in weeks instead of millions of years - Reframes AI development from "mimicking nature" to "using human cultural artifacts as evolutionary substitute" - Suggests evolution was finding an algorithm; pre-training is compressing knowledge - **Reflection** - If pre-training is evolution's shortcut, what breaks down in the analogy? - Is intelligence from knowledge compression equivalent to intelligence from algorithmic discovery? -
Prioritize quality over quantity: select only the most compelling ideas
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.
- yesterday First seen · 76 lines · 8 tokens per session scan A 9782f427a1a3
text.extract_ideas is a skill published in the GitHub repository gpsaggese/helpers (158 stars, last pushed today), licensed Apache-2.0. It adds 8 tokens to every session and 565 once invoked, about $0.0000 per session on Opus 5.5. 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 2026-11-06.
text.extract_ideas compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| text.extract_ideasgpsaggese/helpers | Skill | 158 | today | Apache-2.0 | A | 8 | Claude Code |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | today | MIT | A | 200 | Claude Code, Codex |
| playwright-climicrosoft/playwright | Skill | 99k | today | Apache-2.0 | A | 19 | Claude Code |
| systematic-debuggingobra/superpowers | Skill | 314k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| chat-pet-sprite-creationmicrosoft/vscode | Skill | 195k | today | MIT | A | 53 | Claude Code, Codex |