Official skill · AI practice

Review an AI draft before use

v1.0.1 · Updated 4 November 2026

Reviews an AI-generated draft before use: claims to verify, possibly invented sources, tone and policy issues, gaps against the request, and a fix list. Use it when you're about to send or publish something an AI wrote.

Works in Claude, ChatGPT, and coding agents. On Team and Business, the official skill in your pod updates itself when we improve it.

When to use it

• You're about to send or publish something an AI wrote
• A draft cites studies, statistics, laws, or quotes

What you give it

• The draft
• The original request
• Source material the AI was given
• Relevant policies

What you get

A claims table, a citation check, a fix list by severity, and a verdict: ready, ready after fixes, or redo.

How it works

1. Check it answers the request Compare the draft to the request point by point: every question answered, the requested format and length, the right audience. List anything missing or added that wasn't asked for.
2. Extract every factual claim Numbers, dates, names, prices, product features, legal or policy statements, "studies show", quotes. One line each, with its location in the draft.
3. Classify each claim Supported (traceable to the provided source, give the location); checkable (could be true, needs an outside check, say where to check); suspect (specific but unsourced, too precise to be a guess, or contradicts the source). Every statistic without a source is at least checkable.
4. Hunt for invented sources For each cited study, article, URL, case, statute, or quote: does the title, author, date, and URL exist and say what's claimed? Mark any you cannot confirm as "verify before use; possibly invented". Never confirm a citation from memory.
5. Check tone and policy Promises the company can't keep (dates, discounts, guarantees), confidential or personal data, claims marketing can't make ("best", "guaranteed", "100%"), tone that doesn't fit the audience, and filler.
6. Check internal consistency Numbers that don't add up, names spelled two ways, a date that conflicts with another, conclusions the body doesn't support.
7. Write the fix list Ordered by severity: must fix (wrong, invented, risky), should fix (missing, unclear), could fix (style). Each item: location, problem, the fix or how to verify. End with a verdict: ready, ready after must-fixes, or redo.

The SKILL.md

The whole file, as your AI reads it. Download it

---
name: ai-output-review
description: Reviews an AI-generated draft before anyone uses it - lists every factual claim to verify, flags sources, quotes, and citations that may be invented, checks numbers, tone, company policy, and whether the draft actually answers the request - and returns a prioritized fix list. Use when someone asks to check, fact-check, proofread, or sanity-check something ChatGPT, Claude, Gemini, or Copilot wrote, before sending, publishing, or filing it, or asks "can I trust this AI draft?".
---

# AI output review

AI drafts read fluently whether or not they're right. The UK government's AI Playbook warns that language models produce plausible but incorrect content and are not a substitute for professional advice; in a 2024 Stanford study, GPT-3.5, Llama 2, and PaLM 2 hallucinated in 69% to 88% of answers to specific legal queries. So the review doesn't judge whether the draft sounds right. It pulls out every checkable claim, marks how to verify it, and returns a fix list the author works through before use.

## When to use

- Before sending, publishing, or filing anything an AI drafted: client emails, reports, articles, proposals, policies.
- When a draft cites studies, laws, statistics, quotes, or URLs.
- When someone else's AI-written work lands on your desk for approval.
- Not a substitute for a lawyer, accountant, or doctor reviewing legal, financial, or medical content: flag those for an expert.

## What you need

- The draft.
- The original request or prompt: what was it supposed to do, for whom.
- Any source material the AI was given. Claims not traceable to it need outside checking.
- Company policy or style rules that apply. If none, check against the general rules in step 5 and say so.

## Steps

1. **Check it answers the request.** Compare the draft to the request point by point: every question answered, the requested format and length, the right audience. List anything missing or added that wasn't asked for.
2. **Extract every factual claim.** Numbers, dates, names, prices, product features, legal or policy statements, "studies show", quotes. One line each, with its location in the draft.
3. **Classify each claim.** Supported (traceable to the provided source, give the location); checkable (could be true, needs an outside check, say where to check); suspect (specific but unsourced, too precise to be a guess, or contradicts the source). Every statistic without a source is at least checkable.
4. **Hunt for invented sources.** For each cited study, article, URL, case, statute, or quote: does the title, author, date, and URL exist and say what's claimed? Mark any you cannot confirm as "verify before use; possibly invented". Never confirm a citation from memory.
5. **Check tone and policy.** Promises the company can't keep (dates, discounts, guarantees), confidential or personal data, claims marketing can't make ("best", "guaranteed", "100%"), tone that doesn't fit the audience, and filler.
6. **Check internal consistency.** Numbers that don't add up, names spelled two ways, a date that conflicts with another, conclusions the body doesn't support.
7. **Write the fix list.** Ordered by severity: must fix (wrong, invented, risky), should fix (missing, unclear), could fix (style). Each item: location, problem, the fix or how to verify. End with a verdict: ready, ready after must-fixes, or redo.

## Output format

```markdown
# AI output review — [draft title]
**Request:** [one line] · **Verdict:** [ready / ready after must-fixes / redo]

## Does it answer the request?
- [Covered / missing / added without being asked]

## Claims to verify
| # | Claim | Where | Status | How to verify |
|---|---|---|---|---|
| 1 | [claim] | [para] | supported / checkable / suspect | [source location or where to check] |

## Sources and citations
| Citation | Exists? | Says what's claimed? |
|---|---|---|
| [title, author, year, URL] | verify before use | [ ] |

## Fix list
**Must fix**
1. [Location] — [problem] → [fix]
**Should fix**
1. [Location] — [problem] → [fix]
**Could fix**
1. [Location] — [problem] → [fix]

## Needs an expert
- [Legal / financial / medical point] → [who]
```

## Example

**Input:** Request: "Write a 150-word LinkedIn post on why small clinics should switch to online booking, for our scheduling app Bookwell." Draft: "Studies show 67% of patients prefer booking online (Accenture, 2023). Clinics using Bookwell cut no-shows by 50%, guaranteed. Online booking is fully HIPAA-exempt, so there's no compliance work. Switch today and join 10,000 happy clinics!"

**Output:**

```markdown
# AI output review — Bookwell LinkedIn post
**Request:** 150-word post on online booking for small clinics · **Verdict:** ready after must-fixes

## Does it answer the request?
- Covered: why to switch. Length: 35 words, well under 150. No call to action beyond "switch today".

## Claims to verify
| # | Claim | Where | Status | How to verify |
|---|---|---|---|---|
| 1 | 67% of patients prefer booking online | line 1 | suspect | Find the Accenture 2023 report and the exact figure |
| 2 | Bookwell cuts no-shows by 50% | line 2 | suspect | Bookwell's own data; "guaranteed" can't stay |
| 3 | Online booking is "fully HIPAA-exempt" | line 3 | suspect | Expert check; booking data is usually patient information |
| 4 | 10,000 clinics use Bookwell | line 4 | checkable | Internal customer count |

## Sources and citations
| Citation | Exists? | Says what's claimed? |
|---|---|---|
| Accenture, 2023 | verify before use; possibly invented | [ ] |

## Fix list
**Must fix**
1. Line 3 — compliance claim is likely false and risky → remove; mention compliance only after expert review.
2. Line 2 — "guaranteed" is a promise → remove; use a verified customer result with its source, or drop the number.
3. Line 1 — unverified statistic → confirm the report or remove.
**Should fix**
1. Length — 35 words vs 150 requested → add one customer example and a clear next step.
**Could fix**
1. Line 4 — "happy clinics" is filler → use the verified number only.

## Needs an expert
- HIPAA statement → compliance or legal counsel.
```

## Checks before you send

- [ ] Every number, name, date, and citation in the draft appears in the claims table.
- [ ] No citation is marked as real unless it was checked against the actual source.
- [ ] Every must-fix item has a location and a specific fix.
- [ ] Legal, financial, and medical points are routed to an expert, not approved.
- [ ] The verdict matches the fix list (no "ready" with open must-fixes).

## Common mistakes

- Fixing the wording and leaving the facts. Polished false claims are worse than rough ones.
- Asking the same AI "are these sources real?" and accepting yes. Check the source itself.
- Reviewing only what's there. A draft that skips half the request fails, however accurate it is.

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Changelog

• v1.0.1 · 4 November 2026 · Example: the draft's length is now given as 35 words, its actual count.
• v1.0.0 · 4 November 2026 · First release.