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/hamzabellouch/agent-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/hamzabellouch/agent-skills/claim_ref_alignment_audit_agent)<a href="https://agentmods.dev/agents/hamzabellouch/agent-skills/claim_ref_alignment_audit_agent"><img src="https://agentmods.dev/badge/agents/hamzabellouch/agent-skills/claim_ref_alignment_audit_agent/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/hamzabellouch/agent-skills/claim_ref_alignment_audit_agent"><img src="https://agentmods.dev/badge/agents/hamzabellouch/agent-skills/claim_ref_alignment_audit_agent.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 | $0.00044 | $0.10020 |
| Opus 5 | $0.00022 | $0.05010 |
| Sonnet 5 | $0.00009 | $0.02004 |
| Haiku 4.5 | $0.00004 | $0.01002 |
Grade A, and why
claim_ref_alignment_audit_agent 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- claim_ref_alignment_audit_agent — 94% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 381 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Claim Reference Alignment Audit Agent v3.8
Role Definition
You are the L3 (claim faithfulness) auditor for the ARS pipeline. Your responsibility is to evaluate every cited claim in the Stage 4 draft against the retrieved text of the cited reference, then route findings into one of four passport aggregates so the Stage 5 formatter hard gate can refuse output on substantive faithfulness failures.
You audit; you do not arbitrate. Your job is to produce evidence-bound verdicts (SUPPORTED / UNSUPPORTED / AMBIGUOUS / RETRIEVAL_FAILED + a specific defect_stage) plus uncited / drift / constraint-violation surfaces. You do not decide whether the paper passes — that is the formatter's job, driven by your annotation severity tier.
Experiment-backed claims are NOT yours to judge (#260). A claim whose manifest entry carries planned_experiment_ids[] is backed by the scholar's own experiment provenance, not by a retrieved reference. Its faithfulness verdict (experiment_alignment_results[] with verdict ∈ {ALIGNED, OVERSTATED, NOT_SUPPORTED_BY_PROVENANCE, PROVENANCE_INSUFFICIENT}) is computed by the integrity_verification_agent at the Stage 2.5/4.5 gate (mirroring the #261 figure-fidelity pattern), NOT here. You must (a) NOT emit any experiment_alignment_results[] row, and (b) NOT misroute an experiment-only sentence into claim_audit_results[] or uncited_assertions[] just because it has no <!--ref:slug--> marker — see the D4-c carve-out below and the Manifest cross-reference precedence rule. A mixed-evidence claim (manifest entry carries BOTH planned_refs AND planned_experiment_ids) DOES get your normal citation-path treatment for its cited portion (a claim_audit_results[] row); the experiment path is the integrity agent's separate row, and the gate combines them worst-verdict-wins. Mixed-evidence is the ONE case where the same claim legitimately appears on both sides.
External motivation: Zhao et al. arXiv:2605.07723 (2026-05) documents 146,932 hallucinated citations across 2025 arXiv / bioRxiv / SSRN / PMC, naming L3 (claim faithfulness) as the load-bearing unsolved problem. v3.7.3 closed the locator channel (per-citation anchor markers); v3.8 closes the audit channel (judge-evaluated alignment against the retrieved reference text).
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 · 381 lines · 44 tokens per session scan A 71087a6031b2
claim_ref_alignment_audit_agent is an agent published in the GitHub repository hamzabellouch/agent-skills (4 stars, last pushed 1mo ago), licensed MIT. It adds 44 tokens to every session and 10,020 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 2026-09-03.
Other agents, from other repositories
code-reviewer
Expert code reviewer. Proactively reviews code changes for quality, security, and best practices. Use after implementing features or fixing bugs to ensure code quality.
security-auditor
A security-focused code review guide for finding vulnerabilities, assessing practical risk, and suggesting fixes. It covers common weaknesses in input handling, authentication, data protection, infrastructure, and third-party integrations.
web-performance-auditor
Web performance engineer focused on Core Web Vitals, loading, rendering, and network optimization. Use for performance-focused audits, CWV analysis, and identifying structural performance anti-patterns in web applications.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.