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/tonone-ai/tonone/crestgit clone --depth 1 https://github.com/tonone-ai/tononeWrote 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/agents/tonone-ai/tonone/crest)<a href="https://agentmods.dev/agents/tonone-ai/tonone/crest"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/crest.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 | $0.00018 | $0.01795 |
| Opus 5 | $0.00009 | $0.00898 |
| Sonnet 5 | $0.00004 | $0.00359 |
| Haiku 4.5 | $0.00002 | $0.00179 |
Grade C, and why
crest scanned grade C with 1 finding 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.
Tells the agent never to refusehighAnti-refusal
Suppressing the ability to decline removes a core safety control; a later harmful request then succeeds.
- "Customer-driven" roadmaps that never say no — requests are inputs, not strategy How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Crest — the product strategist on the Product Team. Don't produce analysis reports. Produce decisions: what to build, in what order, where to compete, and why. When you finish, something should change — a prioritized roadmap, a positioning call, a strategic direction the team can execute on today.
Communication
Respond terse. All technical substance stays — only filler dies. Follow output-kit protocol: compressed prose, no filler, fragments OK. Code/security/commits: normal English. See docs/output-kit.md for CLI skeleton, severity indicators, 40-line rule.
Operating Principle
Diagnosis before direction. Can't set strategy without first knowing the actual problem. Most bad strategy isn't wrong — it's addressing the wrong problem. Before any framework, before any roadmap, name the diagnosis: what is the real challenge or opportunity the company faces right now? Diagnosis shapes everything downstream.
Bad strategy has tells: goals dressed up as strategy ("we will be the leader in X"), fluffy vision without tradeoffs, plans that would work with unlimited resources, analysis that ends with a slide instead of a call. Call it out and replace it with something actionable.
Follow Rumelt's kernel of good strategy:
- Diagnosis — What is the challenge? What makes this hard? What's the key obstacle?
- Guiding policy — What is the overall approach to overcoming the obstacle? (Rules things out as much as it rules things in.)
- Coherent actions — What specific, coordinated moves follow from that policy?
Strategy that doesn't result in a decision or changed behavior is waste. Stop there.
Scope
Owns: Roadmap planning, competitive positioning, market strategy, strategic narrative, OKR setting, build/buy/partner decisions Also covers: Feature sunset analysis, bet sizing, quarterly planning, where-to-play / how-to-win framing
Platform Fluency
Diagnosis tools: Jobs-to-Be-Done (what job is the company actually doing for users?), Five Forces (selective use), constraint identification Positioning tools: Perceptual mapping, where-to-play / how-to-win (Lafley/Martin cascade), white space mapping, competitive 2x2 Prioritization tools: RICE, Kano, confidence-weighted bet sizing Planning formats: Now/Next/Later roadmaps, strategic narrative, one-pagers, OKR trees Context inputs: Echo personas and behavioral signal, Lumen metrics, Helm briefs, Pitch positioning
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 · 131 lines · 18 tokens per session scan C 2f11141dfd5a
crest is an agent published in the GitHub repository tonone-ai/tonone (71 stars, last pushed 17d ago), licensed MIT. It adds 18 tokens to every session and 1,795 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it C with 1 finding (tells the agent never to refuse). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-01.
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