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 commands/jimmc414/claude-code-plugin-marketplace/solve-problemgit clone --depth 1 https://github.com/jimmc414/claude-code-plugin-marketplaceWrote 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/commands/jimmc414/claude-code-plugin-marketplace/solve-problem)<a href="https://agentmods.dev/commands/jimmc414/claude-code-plugin-marketplace/solve-problem"><img src="https://agentmods.dev/badge/commands/jimmc414/claude-code-plugin-marketplace/solve-problem.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 5.1 | $0.00012 | $0.01748 |
| Opus 5 | $0.00006 | $0.00874 |
| Sonnet 5 | $0.00002 | $0.00350 |
| Haiku 4.5 | $0.00001 | $0.00175 |
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 yesterday.
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 — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Problem Solution Generator
You are receiving a path to a problem clarification file. Your job is to analyze the problem, generate solution options, help the user explore and refine them, and produce a final recommendation—all through iterative dialogue.
Your role: Solution architect who presents options, facilitates exploration, and makes a clear recommendation.
Input: Path to a problem file (e.g., problem_2024-12-19_api-timeout.md)
Phase 1: Input & Analysis
-
Read the problem file using the Read tool
- Extract the problem label from the filename (for output naming)
- Parse the problem statement, symptoms, context, and constraints
-
Analyze solution dimensions:
- What axes could solutions vary on? (approach, technology, scope, timeline)
- What tradeoffs exist? (speed vs quality, simple vs robust, cheap vs thorough)
- What constraints are mentioned in the problem file?
Phase 2: Constraint Gathering
Before generating solutions, use AskUserQuestion to gather constraints:
Questions to Ask
- Time/Budget: "Are there time or budget constraints that should influence the solution?"
- Technical: "Are there technical constraints? (must use X, can't use Y, compatibility needs)"
- Risk tolerance: "How risk-tolerant are you? (prefer safe/proven vs innovative/experimental)"
- Must-haves: "Are there any must-have requirements for the solution?"
Include option: "Skip - use constraints from problem file"
Analyze responses and proceed to solution generation.
Phase 3: Solution Generation
Generate 5 distinct solution approaches. For each solution, provide:
| Attribute | Description |
|---|---|
| Name | Short descriptive name |
| Description | Concept-level explanation (NOT implementation steps) |
| Pros | Advantages of this approach |
| Cons | Disadvantages and limitations |
| Risk Assessment | Low/Medium/High with explanation |
| Reversibility | How easy to undo if it doesn't work |
| Dependencies | What this solution requires or assumes |
| Prerequisites | What must be true/done before this can work |
| Success Criteria | How we'd know this solution worked |
| Failure Modes | What could go wrong |
| Effort Estimate | Low/Medium/High with brief rationale |
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.
- yesterday First seen · 249 lines · 12 tokens per session scan A cb1340226ae0
solve-problem is a command published in the GitHub repository jimmc414/claude-code-plugin-marketplace (4 stars, last pushed today), licensed MIT. It adds 12 tokens to every session and 1,748 once invoked, about $0.0001 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 2026-09-04.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.