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-reality-checkgit 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-reality-check)<a href="https://agentmods.dev/commands/assafkip/huntkit/q-reality-check"><img src="https://agentmods.dev/badge/commands/assafkip/huntkit/q-reality-check.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 | $0.00296 |
| Opus 5 | $0.00000 | $0.00148 |
| Sonnet 5 | $0.00000 | $0.00059 |
| Haiku 4.5 | $0.00000 | $0.00030 |
Grade A, and why
q-reality-check 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
Challenger mode. Argue AGAINST current hypotheses and findings to find weak spots.
Different from /q-challenge (which audits evidence quality). This command adopts the opposing perspective and stress-tests conclusions.
Steps
- Read the active case's scope, findings, and investigation state
- For each major hypothesis or finding, temporarily argue the OPPOSITE:
- "What if this person is NOT the harasser?"
- "What if the accounts are NOT connected?"
- "What if the evidence supports a completely different explanation?"
- Rate each hypothesis:
- STRONG: Would survive cross-examination. Multiple independent evidence sources.
- MODERATE: Plausible but relies on a single evidence chain or inference.
- WEAK: Could be coincidence, name matching, or confirmation bias.
- For each MODERATE or WEAK finding:
- What specific evidence would upgrade it to STRONG?
- What's the simplest alternative explanation?
- What collection could test it? (Energy tag + Time Est)
Output
Write to the active case's investigation/findings/REALITY-CHECK-YYYY-MM-DD.md
Tone
Not hostile. Socratic. The goal is to find weak spots before a client, attorney, or opposing party does. Frame as: "How would someone challenge this in court/deposition?"
Run before finalizing any brief or report.
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 · 30 lines · 0 tokens per session scan A fcc7895b4b3e
q-reality-check 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 296 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.
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.