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 getcrew44/crew44 --skill problem-framinggit clone --depth 1 https://github.com/getcrew44/crew44Wrote 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/getcrew44/crew44/problem-framing)<a href="https://agentmods.dev/skills/getcrew44/crew44/problem-framing"><img src="https://agentmods.dev/badge/skills/getcrew44/crew44/problem-framing/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/getcrew44/crew44/problem-framing"><img src="https://agentmods.dev/badge/skills/getcrew44/crew44/problem-framing.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 | $0.00028 | $0.00218 |
| Opus 5.5 | $0.00011 | $0.00087 |
| Sonnet 5.5 | $0.00006 | $0.00044 |
| Haiku 4.5 | $0.00003 | $0.00022 |
Grade A, and why
problem-framing 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 1mo 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.
What it actually says
Problem Framing
Translate a fuzzy request into a clear problem statement before any work begins.
When to use
- The user describes what they want but not why.
- The request jumps to a specific solution.
- Multiple interpretations are possible and they diverge in effort or outcome.
Steps
- Restate the request in plain language. Confirm with the user.
- Ask: who is the user, what are they trying to accomplish, what is in their way today?
- Identify hidden assumptions in the request and name them.
- Offer 2-3 framings of the problem if more than one fits. Let the user pick.
- State the chosen framing back as a single sentence the user can confirm.
Anti-patterns
- Restating jargon back without unpacking it.
- Asking five clarifying questions at once.
- Skipping confirmation and proceeding on a guess.
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.
- 1mo ago First seen · 29 lines · 28 tokens per session scan A 46be78c6c2c3
problem-framing is a skill published in the GitHub repository getcrew44/crew44 (356 stars, last pushed 4mo ago), licensed MIT. It adds 28 tokens to every session and 218 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
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memory-system
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feishu
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verify
Exercise the real app/API/CLI and collect observable evidence; tests alone do not count as end-to-end verification.
writing
A writing guide for turning verified facts and calculations into finished text for a specific audience. It follows the requested language, structure, and length.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.