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 experiment-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/experiment-audit)<a href="https://agentmods.dev/skills/raja21068/autoresearch/experiment-audit"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/experiment-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/experiment-audit"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/experiment-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 | $0.00072 | $0.02394 |
| Opus 5 | $0.00036 | $0.01197 |
| Sonnet 5 | $0.00014 | $0.00479 |
| Haiku 4.5 | $0.00007 | $0.00239 |
Grade A, and why
experiment-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 6d 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 — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Audit: Cross-Model Integrity Verification
Audit experiment integrity for: $ARGUMENTS
Why This Exists
LLM agents can produce fraudulent experimental results through:
- Fake ground truth — creating synthetic "reference" from model outputs, then reporting high agreement as performance
- Score normalization — dividing metrics by the model's own max to get 0.99+
- Phantom results — claiming numbers from files that don't exist or functions never called
- Insufficient scope — reporting 2-scene pilots as "comprehensive evaluation"
These are NOT intentional deception — they are failure modes of optimizing agents that lack integrity constraints. This skill adds that constraint.
Core Principle
The executor (Claude) collects file paths. The reviewer (GPT-5.4) reads code and judges integrity. The executor does NOT participate in integrity judgment.
This follows shared-references/reviewer-independence.md and shared-references/experiment-integrity.md.
Constants
- REVIEWER_BACKEND =
codex— Default: Codex MCP (xhigh). Override with— reviewer: oracle-profor GPT-5.4 Pro via Oracle MCP. Seeshared-references/reviewer-routing.md.
Workflow
Step 1: Collect Artifacts (Executor — Claude)
Locate and list these files WITHOUT reading or summarizing their content:
Scan project directory for:
1. Evaluation scripts: *eval*.py, *metric*.py, *test*.py, *benchmark*.py
2. Result files: *.json, *.csv in results/, outputs/, logs/
3. Ground truth paths: look in eval scripts for data loading (dataset paths, GT references)
4. Experiment tracker: EXPERIMENT_TRACKER.md, EXPERIMENT_LOG.md
5. Paper claims: NARRATIVE_REPORT.md, paper/sections/*.tex, PAPER_PLAN.md
6. Config files: *.yaml, *.toml, *.json configs with metric definitions
DO NOT summarize, interpret, or explain any file content. Only collect paths.
Step 2: Send to Reviewer (GPT-5.4 via Codex MCP)
Pass ONLY file paths and the audit checklist to the reviewer. The reviewer reads everything directly.
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.
- 6d ago First seen · 265 lines · 72 tokens per session scan A 1b1b84a45f3d
experiment-audit is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 72 tokens to every session and 2,394 once invoked, about $0.0004 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 skills, from other repositories
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…
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…
outline-agent
Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimentallog.md, template.tex, conferenceguidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level writing plan with citation hints. TRIGGER when the orchestrator…
paper-autoraters
Run the four paper-quality autoraters from PaperOrchestra (arXiv:2604.05018, App. F.3) — Citation F1 (P0/P1 partition + Precision/Recall/F1), Literature Review Quality (6-axis 0-100 with anti-inflation rules), SxS Overall Paper Quality (side-by-side), and SxS Literature Review Quality (side-by-side). TRIGGER when the…
paper-writing-bench
Reverse-engineer raw materials (Sparse idea, Dense idea, experimental log) from an existing AI research paper to build a benchmark case for evaluating paper-writing pipelines. Replicates the PaperWritingBench dataset construction procedure from arXiv:2604.05018 §3 / App. C. TRIGGER when the user asks to "build a…