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 5.5 | $0.00029 | $0.00643 |
| Sonnet 5.5 | $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 1mo 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.
This is a copy
91% identical to ai-product-canvas — 9 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
- 1mo 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 (55 stars, last pushed 4mo ago), licensed MIT. It adds 73 tokens to every session and 1,607 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 91% identical to ai-product-canvas, differing in 9 lines, and is treated as a copy.
Other skills, from other repositories
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and designs an end-to-end governed agentic analytics solution using Knowledge Catalog and Managed Service for Apache Spark (Lightning Engine). Use when designing data science and analytics workflows across structured and unstructured distributed data (including in S3, Azure Blob, AlloyDB, and…
training-check
Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.
finetuning
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file…