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-challengegit clone --depth 1 https://github.com/assafkip/huntkitWhat 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 | $0.00398 |
| Opus 5 | $0.00000 | $0.00199 |
| Sonnet 5 | $0.00000 | $0.00080 |
| Haiku 4.5 | $0.00000 | $0.00040 |
Grade A, and why
q-challenge 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 3d 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
Force a structured challenge of all investigation assumptions and hypotheses.
This is a mid-investigation health check. Do NOT skip steps.
Steps
- Read
canonical/scope.mdfor hypotheses - Read ALL files in
investigations/<case>/findings/ - Read
investigations/<case>/evidence/index (list files, read key ones) - Read
memory/investigation-state.mdfor current assessment
For each hypothesis, answer:
| Question | Answer |
|---|---|
| What evidence SUPPORTS this? | (cite specific files) |
| What evidence CONTRADICTS this? | (cite specific files) |
| What would DISPROVE this? | (describe the test) |
| Have we actually looked for disproving evidence? | Yes/No |
| Is this based on fact, inference, or assumption? | (classify) |
| Could the same evidence support a different explanation? | (describe) |
Then check for:
- Circular reasoning: Are we using conclusion A to support conclusion B, and B to support A?
- Name-matching traps: Are we assuming two accounts/profiles are the same person just because the name matches?
- Confirmation bias: Have we only collected evidence that supports our theory?
- Source independence: Do multiple "sources" actually trace back to the same original data point?
- Community inference vs. identification: Did witnesses IDENTIFY someone, or INFER from context?
Output
Write to investigations/<case>/findings/CHALLENGE-YYYY-MM-DD.md with:
- Each hypothesis: current confidence, revised confidence (if changed), and why
- Top 3 weakest assumptions that need testing
- Recommended collection to test (not confirm) the weakest assumptions
- Tag each recommended action with Energy + Time Est
Present the challenge to the founder as choices, not commands.
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.
- 3d ago First seen · 40 lines · 0 tokens per session scan A 8e648cf0d181
q-challenge is a command published in the GitHub repository assafkip/huntkit (51 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 398 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
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.