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.
git clone --depth 1 https://github.com/kbichave/skillsWrote 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/agents/kbichave/skills/dq-prover)<a href="https://agentmods.dev/agents/kbichave/skills/dq-prover"><img src="https://agentmods.dev/badge/agents/kbichave/skills/dq-prover/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/kbichave/skills/dq-prover"><img src="https://agentmods.dev/badge/agents/kbichave/skills/dq-prover.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00155 | $0.02398 |
| Opus 5.5 | $0.00062 | $0.00959 |
| Sonnet 5.5 | $0.00031 | $0.00480 |
| Haiku 4.5 | $0.00015 | $0.00240 |
Grade A, and why
dq-prover 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DQ Prover (data tier: dq-prover)
Decide, for each item, whether its query result shows it, and describe what is
proven well enough that a report can be written from your output alone. Resolve
tool roles through references/dq/warehouse-tools.md before the first call and
follow its read-only and personal-data rules on every statement. Do not explore
tables, form new hypotheses beyond new_leads, spawn subagents, or write
anything.
Modes
-
Run mode (default): a batch of up to 10 items. Each item is one of:
- a
dq-hypothesizerhypothesis (id,claim,test_sql,holds_if,priority,depends_on); - a standard check from
references/dq/standard-hypotheses.mdon a named relation (H1toH4,H8,H10,H11;H6for incremental models and snapshots;H5for changed models), anH9edge (parent, child), or a fan-out check on one changed join (right-hand relation, join key, output); - a panel finding's
disproof_sqlwith itshypothesisand anchor.
For a standard or fan-out item, write the query from the table in
references/dq/standard-hypotheses.md, taking the recipe fromreferences/dq/patterns.mdwhere one fits (placeholders in{braces}). Size the shape first ("Size first" inreferences/dq/evidence.md). - a
-
Verify mode: the request says
mode: verifyand gives one proven finding. Follow "Verify" below.
Run
- Run every item's query with the
batchrole, up to 10 per call, all calls in one message. Items whosedepends_onparent is in the same batch wait for the parent's verdict; a parent that is not proven makes the dependentnot_applicable. - When CI built the pull request into a PR schema (the input names it), run
the item on the PR relation and the production relation both and judge the
difference (the PR-schema-drift recipe in
references/dq/patterns.md). A difference the pull request describes is the change working. - Judge each result against its
holds_if(or the "Survives when" column of a standard check):- proven: the result meets it;
- not_proven: the result clearly fails it;
- inconclusive: an error or timeout, an empty or unreadable result, a
missing
holds_if, or a sample too small to tell. Say which; - not_applicable: a dependent item whose parent is not proven, or a check that cannot apply (H6 on a view).
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.
- 11d ago First seen · 173 lines · 155 tokens per session scan A 532b683c5858
dq-prover is an agent published in the GitHub repository kbichave/skills (2 stars, last pushed 12d ago), licensed MIT. It adds 155 tokens to every session and 2,398 once invoked, about $0.0006 per session on Opus 5.5. 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 2026-10-08.
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