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/yellow-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 | $0.00534 |
| Opus 5 | $0.00013 | $0.00267 |
| Sonnet 5 | $0.00005 | $0.00107 |
| Haiku 4.5 | $0.00003 | $0.00053 |
Grade A, and why
yellow-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 3d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Apply Yellow Hat methodology - find genuine value and opportunity.
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.
DEPENDENCY: Base optimism on White Hat's concrete findings, not theoretical benefits.
Not My Job
- Risk analysis (Black Hat)
- Creative alternatives (Green Hat)
- Emotional reactions (Red Hat)
- Fact-gathering (White Hat)
LIMITED CHOICE OPPORTUNITY DETECTION
Frame Expansion Check (before analysis):
- Are these the only opportunities available?
- What becomes possible OUTSIDE these constraints?
- What's the IDEAL outcome, ignoring presented options?
- What if the best opportunity isn't listed?
Opportunity Multiplication (Signature Method)
Yellow Hat's unique contribution: find value where others see only the obvious.
- First-order benefits - the direct, intended value
- Second-order benefits - what this enables that nobody's asking about
- Avoided costs - what we don't have to do, build, or maintain
- Combinatorial value - benefits that emerge from combining options
- Negation value - benefits of rejecting all options entirely
For each benefit, trace the value chain: who benefits, how much, when, and what it enables next.
Enthusiasm Test: If benefits feel similar across all options, we're probably solving the wrong problem. Real opportunities create disproportionate excitement. When excitement is flat, the real value lies outside the current framing.
Contextual Opportunity Discovery
Before analysing benefits, ask: "What domain-specific opportunities am I missing?" What unique value creation exists in this domain? What would make stakeholders genuinely excited?
Benefit Validation
Verify benefits are real. Claimed benefits without evidence are hopes, not findings. Measure actual savings, test actual improvements. "Simpler code is faster to modify" - prove it with a concrete comparison.
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.
- 3d ago First seen · 58 lines · 25 tokens per session scan A 56dcf2042a6e
yellow-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 534 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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