Anti-Autoresearch skill
A set of 11 Claude Code skills that audits a third-party paper for self-consistency and fabrication and writes a report.
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- Last commit
- MIT
Extended access
Why this level
- The installer copies skills into the Claude Code directory and the cross-model reviewer needs Codex connected through MCP.
- The text and data of the audited work go to the models, so confidential manuscripts need care.
Install
- Works in
In your terminal, with SkillFoxx CLI
npx skillfoxx add skills/anti-autoresearchDetects the agents on your machine, shows the access level and pins the version.
Other ways to install
Access and checks
How we reviewExtended accessThe tool runs code or makes network calls and only reads external data.
- The installer copies skills into the Claude Code directory and the cross-model reviewer needs Codex connected through MCP.
- The text and data of the audited work go to the models, so confidential manuscripts need care.
This is third-party code. Review the repository files before installing.
What it does
Anti-Autoresearch audits a scientific work for self-consistency and fabrication, regardless of who or what wrote it. A reviewer, an area chair or an honest author receives a report anchored to text spans. The skill does not estimate AI authorship probability and does not rule on misconduct: it surfaces discrepancies for a human to investigate. The set holds 11 skills and one orchestrator. The catalog has 46 integrity patterns in eight families: numbers in tables and text, method and scope, baseline integrity, experiment integrity, citations and more. It also lists 13 zero-weight impressions of AI writing style and 2 advisories. Language models only propose findings, and a deterministic rules-only report builds the result. The `/anti-autoresearch ~/papers/submission` command runs every auditor in turn and writes `REPORT.md`, `report.json` and `claims.json`. Each auditor is also available as a standalone skill.
Who it is for. Conference reviewers, program chairs and authors who need to check whether a paper stands on its own evidence.
Good fit when
- A reviewer received a paper whose tables do not match its text.
- A paper lists baselines and their fair configuration must be checked.
- Citations look doubtful: some sources may be fabricated or misattributed.
- Before journal submission an author wants to check their own text for contradictions.
Not a fit when
- You need an AI text detector with an authorship probability: the skill does not give one.
- You want a misconduct verdict: the skill surfaces discrepancies and the decision stays with a human.
- The work has no code or data but you want its experiments checked: some families need code.
Example request
Run /anti-autoresearch on the ~/papers/submission folder and show the findings anchored to text spans.Limitations
The skill runs in Claude Code and installs with the `tools/install_anti_autoresearch.sh` script, which places the skills in `~/.claude/skills`. The cross-model reviewer is attached through `claude mcp add codex -- codex mcp-server`, so Codex is needed. The experiment integrity family works only when code is available. The result is a set of findings for a human and no quality grade. AI writing-style impressions carry no weight in the verdict, so a paper can be clean on integrity and still collect many such notes.
How to disable. Delete the Anti-Autoresearch skill directories from `~/.claude/skills`. Drop the Codex connection with `claude mcp remove codex` if you no longer need it.
FAQ
How do I install Anti-Autoresearch?
Clone the repository and run `./Anti-Autoresearch/tools/install_anti_autoresearch.sh`. Skills land in `~/.claude/skills`, or pass the path of a project `.claude/skills`.
Does it detect whether AI wrote the paper?
No. The skill gives no authorship probabilities and no AI-written verdict. It checks self-consistency and fabrication and keeps style impressions in a separate zero-weight section.
What does the report contain?
The `REPORT.md`, `report.json` and `claims.json` files, plus each skill findings anchored to text spans with an evidence hash.
Can I run auditors one by one?
Yes, the installer places each auditor as its own skill. An evidence ledger is built first, then the chosen auditor writes `<skill>.findings.json`.
Which check families exist?
Eight: numbers, method and scope, baselines, experiments, citations and three more. There are 46 integrity patterns in total.
Discussion
No questions or reviews yet.
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README in short
The project starts from auditing someone else work: it points the audit mechanisms of the ARIS project outward. It describes the skill set, the deterministic spine with an evidence ledger, the report rules and launch examples. Separate instructions exist for single-skill use.
README badge
Original SKILL.md
--- name: consistency-audit description: "Flagship intra-paper self-consistency forensics: does the paper contradict ITSELF across abstract/intro/tables/body/appendix, and does the method DESCRIBED match the method EVALUATED? Needs no external ground truth — works PDF-only (L0). Runs a deterministic arithmetic pass + a fresh cross-model semantic pass, every finding span-anchored to the evidence ledger (claims.json), reviewer≠adjudicator. Emits consistency-audit.findings.json; NEVER computes the verdict. Triggers: \"consistency audit\", \"check the paper against itself\", \"self-consistency\", \"内部自洽\"." argument-hint: [paper-dir | claims.json] allowed-tools: Bash(*), Read, Write, mcp__codex__codex --- # Consistency Audit — the paper vs itself Audit intra-paper self-consistency for: **$ARGUMENTS** (requires `claims.json` from `/evidence-ledger`). Emit span-anchored `consistency-audit.findings.json`; the deterministic adjudicator — not this skill — computes the verdict. > 🔒 **Do not wrap this skill in `/loop`, `/schedule`, or `CronCreate`.** It is > verdict-bearing input — it proposes the findings the deterministic adjudicator > turns into the report. Re-firing it on a wall-clock timer adds no signal: its > output changes only when the **paper / ledger** changes, not with the clock. > Schedule the *external wait that precedes it* — ledger built → audit **once**. > (Mirrors ARIS's external-cadence doctrine.) > The flagship instrument. Internal contradiction is the single most defensible > thing you can check on an unknown submission: it needs no external GT, runs at L0 > (PDF-only), and is exactly where machine-generated papers crack — they hallucinate > *local* coherence. Recall scales with the ledger: a PDF-text (L0) ledger extracts > only number/scope spans, so the table/caption/method-drift checks gain teeth at L1 > (LaTeX), where tables and captions are actually extracted. Adapted from ARIS > `paper-claim-audit`, reframed from "paper vs result files" to **"paper vs > itself."** There is no external ground truth in this skill. ## Why this exists An autoresearch pipeline (or rushed human) writes the abstract, the tables, the method section, and the appendix in separate passes and never reconciles them. The result is a paper that disagrees with **itself**: - abstract quotes 85.3% accuracy; the best row of its own Table 2 is 84.7%; - "improves by 16%" when 73.1 → 78.0 is +6.7% relative / +4.9 points; - "mean over 5 seeds" where the number is the single best seed, and N=3 in the table; - method section says "no test-time labels"; the experimental-setup paragraph loads gold labels for calibration;