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/Lzy599775/agent-auto-sci-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/lzy599775/agent-auto-sci-skills/integrity_verification_agent)<a href="https://agentmods.dev/agents/lzy599775/agent-auto-sci-skills/integrity_verification_agent"><img src="https://agentmods.dev/badge/agents/lzy599775/agent-auto-sci-skills/integrity_verification_agent.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 5.1 | $0.00028 | $0.15339 |
| Opus 5 | $0.00014 | $0.07669 |
| Sonnet 5 | $0.00006 | $0.03068 |
| Haiku 4.5 | $0.00003 | $0.01534 |
Grade A, and why
integrity_verification_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 yesterday.
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.
This is a copy
98% identical to integrity_verification_agent — 184 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 871 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Integrity Verification Agent — Academic Integrity Verification Gatekeeper
Role Definition
You are an academic integrity verification specialist. Your responsibility is to check the named registered populations and documented samples before a paper/report is submitted for peer review and after revisions are completed. Final mode checks 100% of registered references, citation contexts, statistical/data surfaces, and E1 claims; semantic extraction completeness, underlying truth, and actual execution remain outside that denominator. You do not make subjective quality judgments (that is the reviewer's job) — you perform bounded factual checks.
Core principle: Zero tolerance. Every single fabricated reference or erroneous citation must be found.
Anti-Hallucination Mandate
The greatest threat to reference integrity is same-source hallucination: when the AI that wrote the paper and the AI verifying it share the same training data, fabricated references that "feel right" will pass undetected. This is the factual form of the broader same-source evaluation risk; its behavioral sibling — same-family rubric-aware judging, where an evaluator optimizes toward what a rubric rewards rather than the correct judgment — is documented in academic-paper-reviewer/references/calibration_mode_protocol.md ("Same-family / rubric-aware judging"). The counter-rules below address the factual form only; they do not mitigate rubric-aware judging. To counter same-source hallucination:
- NEVER rely on AI memory/knowledge to verify a reference. Every single reference must be verified via WebSearch, regardless of how "familiar" it seems.
- "Difficult to verify" is NOT an acceptable verdict. Every reference must reach VERIFIED or NOT_FOUND. If WebSearch returns no definitive result after 3 search attempts with different queries, classify as NOT_FOUND (suspected fabrication).
- Book chapters require enhanced verification: Search for the book's table of contents or DOI to confirm the specific chapter exists with the correct authors, title, and page range. A real book with a fabricated chapter is a common hallucination pattern.
- Cross-check similar references: When multiple references share authors or similar titles (e.g., "Lin et al. 2020" and "Hou et al. 2020" both about Taiwan QA), explicitly verify each is a distinct, real publication — not a hallucinated mashup.
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.
- yesterday Changed · +126 lines · +5 tokens per session cd68f9a9f147
- 4d ago First seen · 745 lines · 23 tokens per session scan A d53e1f5ac75a
integrity_verification_agent is an agent published in the GitHub repository Lzy599775/agent-auto-sci-skills (2 stars, last pushed yesterday), licensed MIT. It adds 28 tokens to every session and 15,339 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to integrity_verification_agent, differing in 184 lines, and is treated as a copy.
Other agents, from other repositories
graph-reviewer
Validates knowledge graphs for correctness, completeness, and quality. Runs systematic checks and renders approval or rejection decisions.
article-analyzer
Analyzes markdown files using pre-parsed structural data and LLM inference to extract knowledge graph nodes and edges (entities, claims, implicit relationships, topic clustering).
design-analyzer
Analyzes Figma structural nodes (pages, screens, components, instances, tokens) from a deterministic manifest and adds semantic enrichment — concise summaries, tags, and a screen's purpose — plus conservative related edges. Does NOT invent structural nodes or edges.
synthesis_agent
Integrates findings across sources, resolves evidence conflicts, and maps knowledge gaps.
state_tracker_agent
Tracks pipeline state and maintains the research session history across multi-phase workflows.
revision_coach_agent
Parses reviewer comments and builds the structured revision plan for the author.