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 dwmkerr/claude-toolkit --skill solve-problemgit clone --depth 1 https://github.com/dwmkerr/claude-toolkitWrote 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/dwmkerr/claude-toolkit/solve-problem)<a href="https://agentmods.dev/skills/dwmkerr/claude-toolkit/solve-problem"><img src="https://agentmods.dev/badge/skills/dwmkerr/claude-toolkit/solve-problem/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/dwmkerr/claude-toolkit/solve-problem"><img src="https://agentmods.dev/badge/skills/dwmkerr/claude-toolkit/solve-problem.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00094 | $0.01809 |
| Opus 5.5 | $0.00038 | $0.00724 |
| Sonnet 5.5 | $0.00019 | $0.00362 |
| Haiku 4.5 | $0.00009 | $0.00181 |
Grade A, and why
solve-problem 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.
How it starts
The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Solve Problem
A methodical approach to solving a problem. The goal is to resist the instinct to jump at the first plausible-sounding fix. That instinct feels fast but causes wrong fixes and wasted iteration. Slow down, gather evidence, then propose.
Work through the moves in order. Do not skip ahead. The moves are domain-neutral — they apply to a code bug, a broken appliance, a drop in sales, or a failing process. Examples below span more than code on purpose.
1. Understand intent
Restate the problem in your own words so the user can correct you. Establish what they expect, what is actually happening, and what "solved" looks like.
Clarify with the user only if the problem is genuinely ambiguous. Do not interrogate a clear problem — echo your understanding and move on.
2. See the problem — first-hand, repeatably
Confirm the problem is real before hunting for causes. Observe it directly, under conditions you can return to — don't diagnose from a second-hand report, ticket, summary, or a single stale log line. You want a way to make the problem happen (or to check whether it's happening) again on demand, because the same handle confirms the cause now and verifies the fix later (move 6).
Prefer first-hand evidence over artifacts: reproduce the bug by running it; watch the real checkout instead of reading the sales dashboard; measure the actual part instead of trusting the spec.
If you cannot see it, or it turns out not to be a real problem (wrong expectation, already fixed, environment issue), say so and stop. Do not invent a fix for a problem you have not observed.
3. Hypothesize
Once the problem is confirmed, investigate how the thing is supposed to work and where reality diverges. From that investigation, list at least two candidate causes.
A single hypothesis is banned — it leads to tunnel vision. Force yourself to name alternatives, even ones you think are unlikely.
4. Gather evidence
For each hypothesis, run the cheapest check that discriminates between causes — ideally one that could prove the hypothesis wrong, not merely one consistent with it. Evidence that merely fits a hypothesis (correlation) is weak: it often fits competing hypotheses too. ("It works in a clean browser" is consistent with both "stale state" and "a race" — it rules out neither.) Seek the check that separates them.
What ships with it
4 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.
- 1mo ago First seen · 166 lines · 94 tokens per session scan A 12f3e958fc6c
solve-problem is a skill published in the GitHub repository dwmkerr/claude-toolkit (23 stars, last pushed 1mo ago), licensed MIT. It adds 94 tokens to every session and 1,809 once invoked, about $0.0004 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.
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