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/assafkip/huntkit/q-client-questionsgit clone --depth 1 https://github.com/assafkip/huntkitWrote 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/assafkip/huntkit/q-client-questions)<a href="https://agentmods.dev/commands/assafkip/huntkit/q-client-questions"><img src="https://agentmods.dev/badge/commands/assafkip/huntkit/q-client-questions.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 | $0.00000 | $0.00314 |
| Opus 5 | $0.00000 | $0.00157 |
| Sonnet 5 | $0.00000 | $0.00063 |
| Haiku 4.5 | $0.00000 | $0.00031 |
Grade A, and why
q-client-questions 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 5d 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
Draft targeted questions for the client based on current collection gaps.
The client often knows the answer to questions that would take 50+ tool calls to research. Ask early, ask smart.
Steps
- Read
canonical/scope.mdfor hypotheses and primary question - Read
canonical/collection-plan.mdfor outstanding gaps - Read
investigations/<case>/targets/for collection status on each target - Read
memory/investigation-state.mdfor what's been tried
Draft 3-5 questions that:
- Target the BIGGEST collection gaps (things we can't find online)
- Ask about RELATIONSHIPS between targets and the client
- Ask for IDENTIFICATION of people referenced but not yet identified
- Are answerable by the client (don't ask technical questions they wouldn't know)
- Are specific enough to get useful answers (not "do you know anything about X")
Output format
Subject: [Investigation name] -- Quick questions for [client name]
[Professional greeting]
[2-sentence context: where we are in the investigation]
Questions:
1. [Specific question] -- this helps us [what it unlocks]
2. [Specific question] -- this helps us [what it unlocks]
3. [Specific question] -- this helps us [what it unlocks]
[Professional sign-off]
Present the draft to the founder for review before sending. Never send directly.
Argument: $ARGUMENTS (optional: specific gap to focus questions on)
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.
- 5d ago First seen · 40 lines · 0 tokens per session scan A 41c5c57c7492
q-client-questions is a command published in the GitHub repository assafkip/huntkit (51 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 314 tokens. 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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checklist
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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.
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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.