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 skills add x3allamerican/skills --skill dataq-evidence-standardsgit clone --depth 1 https://github.com/x3allamerican/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/skills/x3allamerican/skills/dataq-evidence-standards)<a href="https://agentmods.dev/skills/x3allamerican/skills/dataq-evidence-standards"><img src="https://agentmods.dev/badge/skills/x3allamerican/skills/dataq-evidence-standards/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/skills/x3allamerican/skills/dataq-evidence-standards"><img src="https://agentmods.dev/badge/skills/x3allamerican/skills/dataq-evidence-standards.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 | $0.00038 | $0.00834 |
| Opus 5.5 | $0.00015 | $0.00334 |
| Sonnet 5.5 | $0.00008 | $0.00167 |
| Haiku 4.5 | $0.00004 | $0.00083 |
Grade A, and why
dataq-evidence-standards 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 1mo 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DataQ Evidence Standards — What Actually Wins
Not all DataQ challenges succeed. The difference is evidence quality.
High-success patterns (>70% reversal rate)
- Identity error — wrong driver named (95% success)
- Officer documentation error — inspection time conflicts with ELD (78% success)
- CFR provision mismatch — citation references wrong rule (65% success)
- Crash classification — § 390.5 trigger absent (70% success)
- Equipment misidentification — wrong unit number (75% success)
- Driver was hired by another carrier at time of inspection (90% success)
- Cab card vs registration mismatch — paperwork issue clarified (60% success)
- State law conflict with FMCSA regulation (case-by-case)
Low-success patterns (<30% reversal rate)
- Officer made a legal interpretation error — challenging legal judgment (15%)
- Officer was over-zealous — subjective claim (10%)
- First-time offense argument — not grounds for reversal (0%)
- Driver was tired — driver state isn't a defense (10%)
- Driver was new — training isn't a defense (5%)
- Equipment failure — should have been caught in pre-trip (15%)
Evidence ranked by power
- Document with officer signature (highest)
- ELD/GPS data with timestamp (very high)
- Vehicle photo with date/timestamp (high)
- Dispatch records (high)
- Driver statement (medium — corroboration needed)
- Witness statement (medium)
- Pre-trip inspection log (medium)
- Maintenance records (medium)
- Photos from after-the-fact (low — no timestamp)
- Verbal account from driver (lowest)
How to structure the challenge
- Issue: State exactly what's wrong (1 sentence)
- CFR citation: Reference the specific section being challenged
- Evidence: Numbered list of documents attached
- Timeline: What happened, in chronological order
- Conclusion: Why the inspection record should be reversed
When NOT to file
If you have no documentary evidence and only the driver's word — the challenge is unlikely to succeed. Save the effort.
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.
- 1mo ago First seen · 77 lines · 38 tokens per session scan A 8e3a95915ad4
dataq-evidence-standards is a skill published in the GitHub repository x3allamerican/skills (3 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 38 tokens to every session and 834 once invoked, about $0.0002 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 2026-09-03.
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