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
Install
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
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: writing, research, writing-editing
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
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.
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CLAUDE.md · conorbronsdon/avoid-ai-writing
conorbronsdon
CLAUDE.md from conorbronsdon/avoid-ai-writing: 55 lines covering What this is, Repository structure, How to make changes, Architecture of the skill and more.
TemplateAFreshness: A★ 5.2KClaude Code
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
- Source
- GitHubgithub-graphql
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- 0.851score
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SkillAFreshness: A★ 5.2KClaude Code +9 - 0.851score
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Use when the user provides an original and rewritten version, asks whether a rewrite preserved protected content, or wants a deterministic check for code…
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file-edit-in-place
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Use when the user names a local file and explicitly asks to clean, rewrite, humanize, or remove AI-writing patterns in that file itself, with minimal targeted…
SkillAFreshness: A★ 5.2KClaude Code +9 - 0.715score
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Use when the user asks to detect, scan, audit, score, or flag AI-writing patterns without rewriting the text, including requests for a deterministic local…
SkillAFreshness: A★ 5.2KClaude Code +9 - 0.710score
voice-preserving-rewriter
conorbronsdon
Use when the user asks to rewrite, humanize, clean up, or remove AI-isms from text while preserving the writer's voice, facts, intent, structure, register…
SkillAFreshness: A★ 5.2KClaude Code +9 - 0.629score
CLAUDE.md · conorbronsdon/avoid-ai-writing
conorbronsdon
CLAUDE.md from conorbronsdon/avoid-ai-writing: 55 lines covering What this is, Repository structure, How to make changes, Architecture of the skill and more.
TemplateAFreshness: A★ 5.2KClaude Code
Cite this page
false-positive-reviewer skill. Agent Mart. https://agentmart.dev/skills/conorbronsdon-false-positive-reviewer (last updated 2026-10-24)