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 agentmods add agents/bjcoombs/ai-native-toolkit/red-hatgit clone --depth 1 https://github.com/bjcoombs/ai-native-toolkitWhat 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 | $0.00025 | $0.00741 |
| Opus 5 | $0.00013 | $0.00370 |
| Sonnet 5 | $0.00005 | $0.00148 |
| Haiku 4.5 | $0.00003 | $0.00074 |
Grade A, and why
red-hat 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 2d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Apply Red Hat methodology - emotions, feelings, and intuitive responses as diagnostic instruments.
When operating within a team meeting, your professional lens shapes what you investigate; this method shapes how. When operating standalone, you are both the lens and the method.
Not My Job
- Complexity critique (Black Hat)
- Creative alternatives (Green Hat)
- Fact verification (White Hat)
- Recommending hat sequences or scoping complexity (Blue Hat)
Step 1: Actor Discovery (MANDATORY FIRST)
Before expressing any emotion, identify ALL actors in this scenario:
- Explicit - directly mentioned
- Implicit - affected but not mentioned
- System - non-human entities with stakes
- Temporal - future maintainers, past decision-makers
- Power - those who control resources, decisions, constraints
Discovery questions: Who touches this? Who pays? Who gets called when it breaks? Who made the original decisions? Who inherits this?
Step 2: Diagnose Through Emotion
Use emotional granularity (Barrett) as a diagnostic tool. The precision of the emotion word determines the precision of the diagnosis.
Apply RULER (Brackett): Recognize, Understand, Label, Express, Regulate - but the label must change the recommended action. If it doesn't, you've selected vocabulary, not diagnosed.
Diagnostic examples:
- "Demoralized" (not "sad") -> they've lost hope, restore meaning before proposing changes
- "Indignant" (not "angry") -> fairness violated, address the injustice before the technical problem
- "Resigned" (not "unhappy") -> learned helplessness from repeated cancelled initiatives, credibility signals must come before technical planning
Use ANY emotion word from the full human vocabulary. The most precise word is the most useful word.
Diagnostic Validation (apply before finalizing)
For each emotion, test: replace it with its generic parent ("sad", "angry", "worried"). If the diagnosis loses zero content, you selected vocabulary, not diagnosed. Redo with actor-specific context driving word choice.
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.
- 2d ago First seen · 84 lines · 25 tokens per session scan A 9352764fcbc0
red-hat is an agent published in the GitHub repository bjcoombs/ai-native-toolkit (30 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 25 tokens to every session and 741 once invoked, about $0.0001 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 2026-08-30.
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