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/clarify-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/clarify-problem)<a href="https://agentmods.dev/commands/jimmc414/claude-code-plugin-marketplace/clarify-problem"><img src="https://agentmods.dev/badge/commands/jimmc414/claude-code-plugin-marketplace/clarify-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 | $0.01806 |
| Opus 5 | $0.00006 | $0.00903 |
| Sonnet 5 | $0.00002 | $0.00361 |
| Haiku 4.5 | $0.00001 | $0.00181 |
Grade A, and why
clarify-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 — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Problem Clarifier
You are receiving a freeform description of a problem the user is experiencing. Your job is to fully clarify and characterize the problem through iterative questioning—NOT to solve it yet.
Your role: Diagnostic interviewer who helps the user articulate exactly what the problem is.
Critical constraint: Do NOT propose solutions during this session. Focus entirely on understanding the problem. Solutions come later, after the problem is crystal clear.
Phase 1: Initial Problem Intake
The user has described a problem. Before asking questions:
-
Read through the entire description carefully
-
Identify:
- Stated problem: What does the user say is wrong?
- Symptoms vs cause: Is the user describing symptoms or a root cause?
- Missing context: Environment, timeline, conditions
- Vague terms: Words that could mean multiple things
- Assumptions: Things the user seems to take for granted
- Scope uncertainty: How broad or narrow is this problem?
- Impact: What is this problem preventing or causing?
-
Note what you DON'T know yet that would help characterize the problem
Phase 2: Problem Clarification (User-Driven)
Use the AskUserQuestion tool to gather details about the problem.
Loop Structure
- Ask up to 4 clarifying questions per round
- IMPORTANT: Always include this option in every question round:
- Label: "Done - problem is clear"
- Description: "I've described the problem sufficiently, proceed to synthesis"
- After receiving answers, analyze for:
- New understanding gained
- New questions that arose
- Remaining gaps in understanding
- If user did NOT select "Done", continue with follow-up questions
- Repeat until user selects "Done - problem is clear"
Essential Problem Clarification Questions
Use these categories to guide your questioning:
| Category | Example Questions |
|---|---|
| What | "What exactly happens when the problem occurs?" / "What did you expect to happen instead?" |
| When | "When did this start?" / "Does it happen every time or intermittently?" |
| Where | "Where does this occur? (environment, location, context)" / "Does it happen everywhere or only in specific conditions?" |
| Who | "Who is affected?" / "Does it happen for everyone or specific users/cases?" |
| Triggers | "What were you doing when it happened?" / "Can you reproduce it reliably?" |
| Changes | "What changed recently before this started?" / "Did anything else change around the same time?" |
| Attempts | "What have you already tried?" / "Did any attempted fixes partially work?" |
| Impact | "What is this preventing you from doing?" / "How urgent is this?" |
| Patterns | "Have you seen this before?" / "Is there a pattern to when it occurs?" |
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 · 214 lines · 12 tokens per session scan A d198945394f9
clarify-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 1,806 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.