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false-positive-reviewer

Use when a user asks what AI-writing flags mean, whether detector output proves AI authorship, or wants a careful interpretation of possible false positives…

Skill by conorbronsdon

IndexedMIT★ 5,223
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/conorbronsdon-false-positive-reviewer

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About

Use when a user asks what AI-writing flags mean, whether detector output proves AI authorship, or wants a careful interpretation of possible false positives, especially for academic, hiring, publication, disciplinary, or other consequential decisions.

Use cases: writing, research, writing-editing

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
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Secrets
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Install scripts
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Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/false-positive-reviewer

What's inside

Skill name
false-positive-reviewer
Description
Use when a user asks what AI-writing flags mean, whether detector output proves AI authorship, or wants a careful interpretation of possible false positives, especially for academic, hiring, publication, disciplinary, or other consequential decisions.
License
MIT
Instructions
76 lines in SKILL.md · view on GitHub

Files (2)

  • SKILL.md
  • openai.yaml · agents/

SKILL.md (first lines)

# False-Positive Reviewer

Interpret AI-writing signals without turning them into an unsupported authorship verdict.

## Authority

Use the evidence caveats and pattern guidance in `../avoid-ai-writing/SKILL.md`. The original Skill explicitly treats flags as writing-quality signals, not proof of who or what wrote the text.

For cross-Skill work, follow `../avoid-ai-writing-router/references/handoff-contract.md` and `../avoid-ai-writing-router/references/skill-graph.json`.

## Connection contract

### Incoming

Accept interpretation work from:

- `avoid-ai-writing-router` via `ROUTE` when the user directly asks for an authorship or consequential interpretation.
- `ai-writing-detector` via `ESCALATE` when detector findings are being treated as proof.
- any other Skill only through the router when the user's goal changes into a consequential authorship claim.

Preserve the distinction between:

- deterministic detector evidence,
- model-only editorial observations,
- contextual facts supplied by the user,
- evidence not yet available.

### Produce

Update the handoff envelope only with interpretation-relevant state:

- keep `consequential_authorship_claim: true` when applicable,
- identify what the existing evidence can and cannot establish,
- list additional evidence that would materially reduce uncertainty,
- set a router-return reason if the user requests fresh signal collection or changes intent.

Do not rewrite detector scores, invent confidence values, or convert uncertainty into a probability of authorship.

### Terminal behavior

This Skill has no direct outgoing Skill edge.

If fresh signal collection is genuinely needed, return control to `avoid-ai-writing-router` with `fresh_signal_collection_needed`. The router may run `ai-writing-detector` and then route the updated evidence back for interpretation if the user's request still requires it.

If the user separately asks to rewrite or edit the text, return control to the router with the new intent. Do not jump directly into rewrite or mutation from this Skill.

This keeps interpretation terminal in the Skill graph and prevents reviewer-detector cycles.

## AI-engineering evidence lens

Apply the `agency-ai-engineer` lens encoded in `../avoid-ai-writing-router/references/agency-role-lenses.md`:

- treat detector output as noisy evidence rather than ground truth,
- account for context mode, genre, second-language writing, technical register, editing software, and baseline writing style,
- separate model behavior from human attribution,
- avoid false precision,
- prefer process evidence when the decision has consequences.

## Workflow

1. Identify which observations are deterministic detector hits, model-only editorial observations, or contextual facts supplied by the user.
2. Explain the strongest signals and plausible human reasons they can appear.
3. Consider genre, second-language writing, technical register, deadline pressure, editing tools, typography software, and the writer's known baseline when those facts are available.
4. If an adequate audit is missing and the user wants one, return control to the router with a fresh-signal request. Do not call the detector directly.
5. For consequential decisions, do not turn a score or pattern list into a definitive claim of AI use, cheating, fraud, dishonesty, or suitability.
6. Suggest evidence that is more probative for the legitimate decision, such as source history, drafts, revision logs, direct discussion with the writer, or task-specific process evidence.

## Stop conditions

Stop when the interpretation question is answered. If more signal collection or a different action is requested, return control to the router rather than opening a direct Skill loop.

## Output

Distinguish what the text actually shows, what it may suggest, what it cannot establish, which evidence came from executed tooling versus model-only review, what additional evidence would reduce uncertainty, and whether control should return to the router for a newly requested stage.

From conorbronsdon/avoid-ai-writing, MIT.

More from conorbronsdon/avoid-ai-writing

Signals

Stars
5,223
Forks
463
Open issues
16
Last commit (this folder)
Oct 15, 2026 (13 days ago)
Last release
Oct 15, 2026 (13 days ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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Cite this page

false-positive-reviewer skill. Agent Mart. https://agentmart.dev/skills/conorbronsdon-false-positive-reviewer (last updated 2026-10-24)