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 summarybotng/summarybot-ng --skill six-thinking-hatsgit clone --depth 1 https://github.com/summarybotng/summarybot-ngWrote 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/summarybotng/summarybot-ng/six-thinking-hats)<a href="https://agentmods.dev/skills/summarybotng/summarybot-ng/six-thinking-hats"><img src="https://agentmods.dev/badge/skills/summarybotng/summarybot-ng/six-thinking-hats.svg" alt="Measured on agentmods" 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 | $0.00079 | $0.01891 |
| Opus 5 | $0.00039 | $0.00945 |
| Sonnet 5 | $0.00016 | $0.00378 |
| Haiku 4.5 | $0.00008 | $0.00189 |
Grade A, and why
six-thinking-hats 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 4d 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 — 284 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Six Thinking Hats for Testing
<default_to_action> When analyzing testing decisions:
- DEFINE focus clearly (specific testing question)
- APPLY each hat sequentially (5 min each)
- DOCUMENT insights per hat
- SYNTHESIZE into action plan
Quick Hat Rotation (30 min):
🤍 WHITE (5 min) - Facts only: metrics, data, coverage
❤️ RED (3 min) - Gut feelings (no justification needed)
🖤 BLACK (7 min) - Risks, gaps, what could go wrong
💛 YELLOW (5 min) - Strengths, opportunities, what works
💚 GREEN (7 min) - Creative ideas, alternatives
🔵 BLUE (3 min) - Action plan, next steps
Example for "API Test Strategy":
- 🤍 47 endpoints, 30% coverage, 12 integration tests
- ❤️ Anxious about security, confident on happy paths
- 🖤 No auth tests, rate limiting untested, edge cases missing
- 💛 Good docs, CI/CD integrated, team experienced
- 💚 Contract testing with Pact, chaos testing, property-based
- 🔵 Security tests first, contract testing next sprint </default_to_action>
Quick Reference Card
The Six Hats
| Hat | Focus | Key Question |
|---|---|---|
| 🤍 White | Facts & Data | What do we KNOW? |
| ❤️ Red | Emotions | What do we FEEL? |
| 🖤 Black | Risks | What could go WRONG? |
| 💛 Yellow | Benefits | What's GOOD? |
| 💚 Green | Creativity | What ELSE could we try? |
| 🔵 Blue | Process | What should we DO? |
When to Use Each Hat
| Hat | Use For |
|---|---|
| 🤍 White | Baseline metrics, test data inventory |
| ❤️ Red | Team confidence check, quality gut feel |
| 🖤 Black | Risk assessment, gap analysis, pre-mortems |
| 💛 Yellow | Strengths audit, quick win identification |
| 💚 Green | Test innovation, new approaches, brainstorming |
| 🔵 Blue | Strategy planning, retrospectives, decision-making |
Hat Details
🤍 White Hat - Facts & Data
Output: Quantitative testing baseline
Questions:
- What test coverage do we have?
- What is our pass/fail rate?
- What environments exist?
- What is our defect history?
What ships with it
4 files 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.
- 4d ago First seen · 284 lines · 79 tokens per session scan A 5e800713191b
six-thinking-hats is a skill published in the GitHub repository summarybotng/summarybot-ng (2 stars, last pushed 3mo ago), licensed MIT. It adds 79 tokens to every session and 1,891 once invoked, about $0.0004 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.
Other skills, from other repositories
constructive-dissent
Structured disagreement protocols that expose weaknesses, test assumptions, and generate alternatives. Use when stress-testing proposals, playing devil's advocate, challenging architectural decisions, or auditing assumptions before finalizing plans.
deep-reasoning
Structured multi-step reasoning tool for complex problem solving with branching, hypothesis testing, and evidence tracking.
Home Security AI Benchmark
LLM & VLM evaluation suite for home security AI applications.
SmartHome Video Anomaly Benchmark
VLM evaluation suite for video anomaly detection in smart home camera footage.
HomeSafe-Bench
VLM indoor safety hazard detection benchmark inspired by HomeSafeBench (arXiv 2509.23690).
Cloud Provider Regression Test
Connectivity, chat, JSON & streaming regression tests for all enabled cloud LLM providers.