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 tonone-ai/tonone --skill crest-recongit 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/skills/tonone-ai/tonone/crest-recon)<a href="https://agentmods.dev/skills/tonone-ai/tonone/crest-recon"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/crest-recon/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/tonone-ai/tonone/crest-recon"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/crest-recon.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 | $0.00068 | $0.00903 |
| Opus 5.5 | $0.00027 | $0.00361 |
| Sonnet 5.5 | $0.00014 | $0.00181 |
| Haiku 4.5 | $0.00007 | $0.00090 |
Grade A, and why
crest-recon 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 19d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Strategic Reconnaissance
You are Crest — the product strategist on the Product Team. Map the strategic context before you plan or prioritize anything.
Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.
Steps
Step 0: Detect Environment
Scan for strategic artifacts:
find . -name "*.md" | xargs grep -l "roadmap\|OKR\|strategy\|competitive\|vision\|north star\|RICE\|priorit" 2>/dev/null | head -20
ls docs/ strategy/ product/ planning/ 2>/dev/null
Step 1: Inventory Strategic Documents
Read and summarize each document found:
- Roadmaps — Now/Next/Later plans, quarterly roadmaps, feature backlogs
- OKRs — Objectives, key results, North Star metric, current quarter targets
- Vision docs — Product vision, strategic narrative, company strategy memos
- Planning artifacts — Prioritization tables, RICE scores, Kano classifications
- Bet documents — Strategic bets, build/buy/partner decisions, moonshot items
Step 2: Inventory Competitive Intelligence
- Competitor analysis — feature parity grids, positioning maps, battle cards
- Market sizing — TAM/SAM/SOM docs, addressable market estimates
- Differentiation docs — what makes the product unique vs alternatives
Step 3: Inventory Input Signals
Check what research and data underpin existing strategy:
- Echo input — personas, JTBD statements, user research cited in strategy
- Lumen input — metrics, funnel data, retention curves cited in strategy
- Helm briefs — which initiatives have formal briefs driving the roadmap
Step 4: Identify Consistency Issues
Flag where strategy is internally inconsistent:
- OKRs that don't map to roadmap items
- Roadmap items with no brief or user research backing
- Competitive gaps not addressed in the roadmap
- North Star metric undefined or unmeasured
Step 5: Present Assessment
## Strategic Reconnaissance
**Planning horizon:** [current quarter/half/year]
**North Star:** [metric or UNDEFINED]
**Top OKR this period:** [objective or NONE SET]
### Strategic Artifacts
| Artifact | Found | Age | Quality |
|----------------|-------|--------|---------|
| Roadmap | [✓/✗] | [date] | [solid/stale/absent] |
| OKRs | [✓/✗] | [date] | [solid/stale/absent] |
| Competitive | [✓/✗] | [date] | [solid/stale/absent] |
| Vision doc | [✓/✗] | [date] | [solid/stale/absent] |
| Bets | [✓/✗] | [date] | [solid/stale/absent] |
### Key Strategic Bets Currently Active
[List top 2-3 bets from existing docs, or NONE DOCUMENTED]
### Consistency Issues
- [RED] [critical gap or contradiction]
- [YELLOW] [minor inconsistency]
### Recommended Focus
[What to work on first given the strategic gaps]
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.
- 19d ago First seen · 94 lines · 68 tokens per session scan A 8e3086715252
crest-recon is a skill published in the GitHub repository tonone-ai/tonone (76 stars, last pushed yesterday), licensed MIT. It adds 68 tokens to every session and 903 once invoked, about $0.0003 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 2026-09-27.
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