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 product-on-purpose/thinking-framework-skills --skill think-problem-restatementgit clone --depth 1 https://github.com/product-on-purpose/thinking-framework-skillsWrote 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/product-on-purpose/thinking-framework-skills/think-problem-restatement)<a href="https://agentmods.dev/skills/product-on-purpose/thinking-framework-skills/think-problem-restatement"><img src="https://agentmods.dev/badge/skills/product-on-purpose/thinking-framework-skills/think-problem-restatement/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/product-on-purpose/thinking-framework-skills/think-problem-restatement"><img src="https://agentmods.dev/badge/skills/product-on-purpose/thinking-framework-skills/think-problem-restatement.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 | $0.00080 | $0.01089 |
| Opus 5 | $0.00040 | $0.00544 |
| Sonnet 5 | $0.00016 | $0.00218 |
| Haiku 4.5 | $0.00008 | $0.00109 |
Grade A, and why
think-problem-restatement 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 11d 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Problem Restatement
The default failure is to solve a problem exactly as first stated, even though the first statement usually encodes a symptom, a presupposed solution, or one stakeholder's view. Problem restatement is a deliberate interrupt before solving: generate several genuinely different formulations of the problem, each by a distinct move (change altitude, separate goal from implementation, shift stakeholder, invert, bound with is/is-not), then choose the most useful one to work on. The output is a problem frame set ending in a single chosen working frame, not a longer list and not prose.
When to Use
- The problem is ambiguous, ill-defined, or stated as a symptom.
- The request names a solution ("build X") but the underlying goal is unstated.
- Solving the wrong problem would be costly; this is upstream of significant work.
- At the start of most reframing, discovery, or strategy workflows.
When NOT to Use
- The problem is already well-defined and validated; reframing a correct, clear problem wastes effort and manufactures doubt.
- For trivial or fully reversible tasks where a wrong frame costs little.
- To generate solutions (use an ideation skill) or to choose among them (use a decision skill); this tool only sharpens the problem.
- As endless reframing that avoids ever committing to solve. Restatement that never selects a working frame is the main failure mode.
Instructions
When asked to restate or reframe a problem, follow these steps:
- Capture the problem as given. Record it verbatim. Note who framed it and whether it names a symptom or a presupposed solution.
- Generate restatements with distinct moves, not rewordings. Produce 5 to 8 genuinely different frames using: altitude up ("what is this ultimately in service of?") and down ("what concretely is failing?"); goal versus implementation (separate the outcome wanted from the solution proposed); stakeholder shift (state it as each affected party would); inversion ("how would we cause this on purpose?"); and is / is not (what the problem explicitly is and is not).
- Justify each briefly. For every restatement, add one line: why this might be the real problem.
- Draw How Might We angles. From the most promising restatements, write 3 to 5 open "How might we ..." questions.
- Select one working frame. Choose the single restatement that best serves the user's actual goal, and say in one or two sentences why. Converge; do not leave it open.
- Emit the problem frame set. Produce the artifact in
references/TEMPLATE.md: the original, the tagged restatement table, the How Might We angles, and the chosen working frame with rationale.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 64 lines · 80 tokens per session scan A 23c7db30d5c8
think-problem-restatement is a skill published in the GitHub repository product-on-purpose/thinking-framework-skills (15 stars, last pushed 24d ago), licensed Apache-2.0. It adds 80 tokens to every session and 1,089 once invoked, about $0.0004 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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