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 raja21068/AutoResearch --skill paper-claim-auditgit clone --depth 1 https://github.com/raja21068/AutoResearchWrote 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/raja21068/autoresearch/paper-claim-audit)<a href="https://agentmods.dev/skills/raja21068/autoresearch/paper-claim-audit"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/paper-claim-audit/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/raja21068/autoresearch/paper-claim-audit"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/paper-claim-audit.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 | $0.00077 | $0.03170 |
| Opus 5 | $0.00039 | $0.01585 |
| Sonnet 5 | $0.00015 | $0.00634 |
| Haiku 4.5 | $0.00008 | $0.00317 |
Grade A, and why
paper-claim-audit 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 8d 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.
This is a copy
89% identical to paper-claim-audit — 46 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 — 327 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Claim Audit: Zero-Context Evidence Verification
Verify that every claim in the paper matches raw evidence for: $ARGUMENTS
Why This Exists
The executor writes experiments AND writes the paper. It "knows" what the results should be. This creates confirmation bias:
- Rounding 84.7% up to 85.3%
- Reporting best seed instead of average
- Citing metrics from a different experiment config
- Claiming "improves by 15%" when the delta is actually 12.8%
A fresh reviewer with zero prior context catches these because it has no expectations — it just compares paper text vs raw files.
How This Differs From Other Audit Skills
| Skill | Question it answers |
|---|---|
/experiment-audit |
Is the experiment code honest? (fake GT, normalization fraud) |
/result-to-claim |
Does the data scientifically support this claim? |
/paper-claim-audit |
Does the paper report the data truthfully and precisely? |
Core Principle
Zero-context, fresh reviewer. The auditor receives ONLY:
- Paper .tex files (the claims)
- Raw result files (the evidence)
It does NOT receive:
- ❌ EXPERIMENT_LOG.md
- ❌ EXPERIMENT_TRACKER.md
- ❌ AUTO_REVIEW.md
- ❌ NARRATIVE_REPORT.md
- ❌ Any executor summary or interpretation
- ❌ Any prior audit results
- ❌ Any conversation history
This is stricter than reviewer-independence — it's zero-context evidence audit.
Workflow
Step 1: Collect Files (Executor — Claude)
Locate paper and result files WITHOUT reading or interpreting them.
Paper files (claims) — paths shown relative to the shell's working
directory so you can find them with ls; when writing them into
audited_input_hashes, use paths relative to the paper dir (no paper/
prefix) per the "Submission Artifact Emission" section below:
paper/main.tex # → hash key: main.tex
paper/sections/*.tex # → hash key: sections/*.tex
paper/tables/*.tex (if separate) # → hash key: tables/*.tex
Result files (evidence):
results/*.json, results/*.jsonl, results/*.csv, results/*.tsv
outputs/*.json, outputs/*.csv
wandb-summary.json (if exists)
**/metrics.json, **/eval_results.json
**/config.yaml, **/args.json (experiment configs)
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.
- 8d ago First seen · 327 lines · 77 tokens per session scan A 834e017fb1f2
paper-claim-audit is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 77 tokens to every session and 3,170 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to paper-claim-audit, differing in 46 lines, and is treated as a copy.
Other skills, from other repositories
literature-review-agent
Step 3 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the literature search strategy from outline.json — discover candidate papers via web search, verify them through Semantic Scholar (Levenshtein > 70 fuzzy title match, temporal cutoff, dedup by paperId), cross-corroborate against Crossref + OpenAlex to…
agent-research-aggregator
Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimentallog.md). TRIGGER when the user says…
content-refinement-agent
Step 5 of the PaperOrchestra pipeline (arXiv:2604.05018). Iteratively refine drafts/paper.tex by simulating peer review and applying targeted revisions, with strict accept/revert halt rules, deterministic 0-100 decision bands (Accept/Minor/Major/Reject) that drive a target-met early stop, and a Devil's Advocate…
paper-orchestra
Orchestrate the full PaperOrchestra (Song et al., 2026, arXiv:2604.05018) five-agent pipeline to turn unstructured research materials (idea, experimental log, LaTeX template, conference guidelines, optional figures) into a submission-ready LaTeX manuscript and compiled PDF. TRIGGER when the user asks to "write a paper…
section-writing-agent
Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimentallog.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges…
plotting-agent
Step 2 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the visualization plan from outline.json — render plots and conceptual diagrams from experimentallog.md and idea.md, optionally refine via VLM critique loop, and produce context-aware captions. Runs in parallel with the literature-review-agent. TRIGGER…