Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add cnfeat/top-pm-skills --skill ai-product-canvasgit clone --depth 1 https://github.com/cnfeat/top-pm-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cnfeat/top-pm-skills/ai-product-canvas)<a href="https://agentmods.dev/skills/cnfeat/top-pm-skills/ai-product-canvas"><img src="https://agentmods.dev/badge/skills/cnfeat/top-pm-skills/ai-product-canvas/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cnfeat/top-pm-skills/ai-product-canvas"><img src="https://agentmods.dev/badge/skills/cnfeat/top-pm-skills/ai-product-canvas.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00073 | $0.01607 |
| Opus 5 | $0.00036 | $0.00804 |
| Sonnet 5 | $0.00015 | $0.00321 |
| Haiku 4.5 | $0.00007 | $0.00161 |
Grade A, and why
ai-product-canvas scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 170 lines · 73 tokens per session scan A f8164316197a
ai-product-canvas is a skill published in the GitHub repository cnfeat/top-pm-skills (48 stars, last pushed 3mo ago), licensed MIT. It adds 73 tokens to every session and 1,607 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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