Add multi-platform export generator (single source of truth)

Make the library multi-platform without duplicating content. Each skills/<name>/SKILL.md body remains the single source of truth; a new generator renders platform-ready exports from it. - scripts/build-exports.mjs — dependency-free Node generator with a PLATFORMS registry so new platforms (Gemini, Cursor, …) are a few lines. Ships ChatGPT exports at exports/chatgpt/<bundle>/<skill>/SYSTEM_PROMPT.md (172 skills), plus generated index READMEs. Supports --platform and --check. - exports/ — generated ChatGPT system prompts, ready to paste into a Custom GPT. - .github/workflows/check-generated.yml — fails a PR if exports or web/skills.json drift from the source skills. - README "Works With" now documents the ready-to-use exports and regen command. - CHANGELOG + SKILL-AUTHORING-STANDARD note the generated artifacts. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_016JWn5jRD5tcEFKrubjQ6Px
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+# AI Product Canvas Skill
+
+Define AI products with the same rigour as any product decision — but with additional layers for data, model, evaluation, and responsible AI. This canvas prevents the most common AI product failure: building a technically impressive feature that doesn't solve a real problem.
+
+## AI Product Anti-Patterns to Check First
+
+Before building, flag if any of these apply:
+- ❌ "We should add AI to [existing feature]" — with no user problem defined
+- ❌ Accuracy target undefined before build begins
+- ❌ No plan for what happens when the model is wrong
+- ❌ User-facing AI output with no human review or fallback
+- ❌ Training data not audited for bias or quality
+- ❌ No evaluation metric — "we'll know it when we see it"
+
+---
+
+## AI Product Canvas Output Format
+
+### AI Product Canvas — [Feature Name] — [Date]
+
+**PM Owner:** [Name]
+**ML/AI Lead:** [Name]
+**Status:** Discovery / Design / Build / Evaluation / Live
+
+---
+
+#### 1. Problem Definition
+**User problem being solved:**
+> [What specific situation is the user in? What job are they trying to get done?]
+
+**Why AI?**
+> [What makes this problem require AI vs a deterministic solution? If the answer is "because we can," stop here.]
+
+**Success for the user looks like:**
+> [What outcome does the user experience when the AI feature is working well?]
+
+---
+
+#### 2. AI Approach
+
+**Task type:**
+- [ ] Classification
+- [ ] Generation (text, image, code)
+- [ ] Summarisation / extraction
+- [ ] Recommendation
+- [ ] Search / retrieval
+- [ ] Prediction / forecasting
+- [ ] Conversation / agent
+
+**Model approach:**
+- [ ] LLM API (GPT-4, Claude, Gemini, etc.) — specify: [Model name + version]
+- [ ] Fine-tuned model on own data
+- [ ] Custom model trained from scratch
+- [ ] RAG (retrieval-augmented generation)
+- [ ] Embedding + vector search
+
+**Rationale for chosen approach:** [Why this, not alternatives]
+
+---
+
+#### 3. Data Requirements
+
+| Data Type | Source | Volume | Quality Status | Bias Risk |
+|---|---|---|---|---|
+| [Training data] | [Where it comes from] | [Volume] | [Audit status] | H/M/L |
+| [Evaluation data] | [Where it comes from] | [Volume] | [Audit status] | H/M/L |
+
+**Data gaps:** [What's missing and plan to get it]
+**Privacy considerations:** [Any PII in training or inference data]
+**Data ownership:** [Do we own this data? Can we use it for training?]
+
+---
+
+#### 4. Evaluation Framework
+
+**Primary metric:** [The number that defines success — accuracy, F1, BLEU, user rating, task completion rate]
+**Minimum acceptable threshold:** [Below X, the feature does not ship]
+**Human evaluation plan:** [How will humans review model outputs? Sampling rate? Review panel?]
+
+| Evaluation Type | Method | Cadence | Owner |
+|---|---|---|---|
+| Offline (pre-launch) | [Test set, benchmark] | Pre-launch | ML Lead |
+| Online (post-launch) | [A/B test, user feedback] | Weekly | PM + ML |
+| Adversarial | [Red-team, edge cases] | Pre-launch | Safety reviewer |
+
+---
+
+#### 5. User Experience Design
+
+**How is AI output presented?**
+- [ ] Direct output shown to user (high trust required)
+- [ ] AI-assisted with user confirmation
+- [ ] Suggestion user can accept/reject
+- [ ] Background action with audit log
+
+**Confidence and uncertainty handling:**
+- What happens when confidence is low? [Show alternative, ask for clarification, fallback to manual]
+- How is uncertainty communicated to the user? [UI pattern]
+
+**Fallback plan:**
+- If the model fails or returns an error: [Specific fallback behaviour]
+- If accuracy degrades below threshold: [Kill switch or graceful degradation plan]
+
+---
+
+#### 6. Responsible AI Checklist
+
+- [ ] Bias audit completed on training data
+- [ ] Demographic fairness evaluated (does performance differ by user group?)
+- [ ] Hallucination / confabulation risk assessed and mitigated
+- [ ] User can see and correct AI output
+- [ ] Opt-out mechanism exists (can user disable the AI feature?)
+- [ ] Output provenance visible when relevant (does user know AI generated this?)
+- [ ] PII not used in ways user didn't consent to
+- [ ] Regulatory review completed (GDPR, AI Act, sector-specific)
+- [ ] Model cards / documentation completed
+
+---
+
+#### 7. Launch & Monitoring Plan
+
+**Rollout:** [% of users, with staged expansion criteria]
+**Monitoring metrics:**
+- Model performance: [Metric + alert threshold]
+- User engagement with AI output: [Acceptance rate, override rate, feedback score]
+- Error rate: [% of failed inferences]
+- Latency: [P95 target]
+
+**Model refresh cadence:** [How often is the model retrained or updated?]
+**Drift detection:** [How will you know when model performance degrades in production?]
+
+---
+
+## Guidelines
+
+- Never skip the "Why AI?" section — it's the most important question in AI product development
+- The fallback UX is not optional — what happens when AI fails defines your product's trustworthiness
+- Responsible AI checklist must be completed before launch, not after
+- Include latency in success metrics — a 5-second AI response is often worse than no AI at all
+- Recommend starting with a human-in-the-loop design and automating only when accuracy is proven
+
+## Required Inputs
+
+Ask the user for these if not provided:
+- **Feature or product description** (what the AI is intended to do)
+- **User problem** (what problem the AI is solving for users)
+- **Available data** (what training/inference data exists)
+- **ML/AI lead** (who owns the technical implementation)
+
+## Anti-Patterns
+
+- [ ] Do not skip the "Why AI?" question — if the answer is "we want to use AI," stop and reframe around the user problem first
+- [ ] Do not launch with an undefined accuracy threshold — "good enough" is not a threshold; set a number before build begins
+- [ ] Do not design the UX to hide AI-generated output as if it were system truth — users need to know when AI is involved so they can override it
+- [ ] Do not defer the Responsible AI checklist to post-launch — bias and privacy issues are far harder to fix in production than in design
+- [ ] Do not treat model latency as a post-launch optimisation — a 6-second AI response that replaces a 1-second rule-based response is a regression, not a feature
+
+## Quality Checks
+
+- [ ] "Why AI?" is answered clearly (not "because we can")
+- [ ] Minimum acceptable accuracy threshold is defined before build begins
+- [ ] Fallback UX is specified for model failures or low-confidence outputs
+- [ ] Responsible AI checklist is completed (not deferred to post-launch)
+- [ ] Monitoring plan includes both model performance and user engagement metrics