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/Peaky8linders/eu-ai-act-scannerWrote 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/peaky8linders/eu-ai-act-scanner/ai-act-scan-fix)<a href="https://agentmods.dev/commands/peaky8linders/eu-ai-act-scanner/ai-act-scan-fix"><img src="https://agentmods.dev/badge/commands/peaky8linders/eu-ai-act-scanner/ai-act-scan-fix/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/commands/peaky8linders/eu-ai-act-scanner/ai-act-scan-fix"><img src="https://agentmods.dev/badge/commands/peaky8linders/eu-ai-act-scanner/ai-act-scan-fix.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 | $0.00043 | $0.01366 |
| Opus 5 | $0.00022 | $0.00683 |
| Sonnet 5 | $0.00009 | $0.00273 |
| Haiku 4.5 | $0.00004 | $0.00137 |
Grade A, and why
ai-act-scan-fix 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/ai-act-scan-fix
Run /ai-act-scan, then build a remediation plan for the top compliance gaps.
Arguments
$1(optional) — path to scan. Defaults to the current directory.--top N(default 3) — number of gaps to remediate.
Behaviour
- Invoke the scanner as in
/ai-act-scan. If the result hasis_ai_system: false, stop: the project is not an AI system (EU AI Act Art. 3(1)), so there is nothing to remediate. Surfacescope_noteand do not propose any fixes — writing compliance "evidence" into a non-AI project fabricates a story the Regulation does not ask for. (Theeu-ai-act-fixCLI enforces this automatically: it short-circuits and writes nothing.) - Select the top
Ngap findings by (low score × article weight). Prioritisation order when scores are tied:risk_mgmt(Art. 9) — foundational, blocks conformity assessmentdata_gov(Art. 10) — training-data provenance affects all downstreamhuman_oversight(Art. 14) — hard legal requirement for high-risklogging(Art. 12) — auditability- Everything else
- For each selected gap, produce a remediation proposal:
- What: one-sentence description of the missing control
- Article: the obligation driving the fix (e.g.
Art. 14(1)) - Where: specific file path(s) to create or edit
- What to add: concrete code / YAML / markdown snippet
- Verification: how the scanner would recognise the fix on a re-scan
- Show the plan. Do NOT apply edits yet. Ask the user which proposals to apply. Then:
- Apply approved proposals one at a time
- Re-run the scanner after each fix
- Report the delta in compliance score per dimension
- If the user says "apply all", still apply serially and re-scan between fixes so regressions are caught early.
Remediation pattern library
When proposing fixes, lean on these canonical patterns — they are what the scanner's analyzers are looking for:
| Gap dimension | Minimal evidence the scanner will accept |
|---|---|
logging |
structlog or logging.getLogger() with correlation IDs, OR MLflow run tracking |
human_oversight |
A function/decorator named human_review, approval_gate, confidence_threshold, or human_in_the_loop |
tech_docs |
A README.md with ≥5 sections covering purpose, data, risks, monitoring, and contacts; or a MODEL_CARD.md |
test_suite |
A tests/ directory with pytest files covering model behaviour (not just unit tests on utils) |
data_gov |
A DATA_CARD.md or docs/data/ describing sources, collection, bias assessment |
security_controls |
Auth middleware, rate limiting, input validation on any AI-serving endpoint |
fairness_testing |
Tests that import aif360, fairlearn, or compute disparate-impact metrics |
transparency / content_transparency (Art. 50) |
An AI-interaction disclosure string ("you are chatting with an AI"), a C2PA / watermark / SynthID marking call on generated output, an emotion/biometric exposure notice, or a visible "AI-generated" deep-fake label |
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 · 91 lines · 43 tokens per session scan A da1c9f572df0
ai-act-scan-fix is a command published in the GitHub repository Peaky8linders/eu-ai-act-scanner (5 stars, last pushed 4d ago), licensed Apache-2.0. It adds 43 tokens to every session and 1,366 once invoked, about $0.0002 per session on Opus 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 2026-08-31.
Other commands, from other repositories
evidence
Export a signed compliance evidence package as JSON or PDF for regulatory handoff.
replay
Replay recorded AI traces to detect behavioral drift between model versions.
comply
Run a full EU AI Act compliance scan on your Python AI project.
testing-plan
Digital operational resilience testing requirements and planning under DORA.
audit
Generate a structured audit report for the current session or a specified time range. The report summarises every governed action, lists any blocked requests, and provides a compliance-ready summary you can download or share.
service-auditor-prep
Generate complete auditor-ready evidence package for SOC 2.