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Recommendation Canvas · getcrew44 | DecimalAI

productivityby getcrew44

Recommendation Canvas

getcrew44/recommendation-canvas

Source getcrew44/crew44 ↗License MIT

Evaluate an AI product idea across outcomes, hypotheses, risks, and positioning.

SkillScore86Maintained

Automatic

Product Management pack

Last verified 8/23/2026

What it changes

Passed 19 of 22 test cases with the skill, and 13 of 22 without it.

Without skill59%

With skill86%

Checks it passed that the model alone missed

Writes the customer product outcome from the user's first-person perspective.
Uses the epic hypothesis format containing 'If we', 'for', and 'Then we will'.
Includes a dedicated assumptions and unknowns section alongside solution risks.

and 4 more in the full report

7 fixed12 passed either way2 still failing1 broke

Pass-rate lift: +27 pp
Tokens: +94%
Agent turns: 0%
Response time: -22%

Model gemini-3.6-flashMeasured 8/23/2026v119 of 22 cases passed with skill

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/getcrew44-recommendation-canvas/SKILL.md https://app.decimal.ai/s/getcrew44-recommendation-canvas/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add getcrew44-recommendation-canvas, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/getcrew44-recommendation-canvas/ and pins it in .decimal/skills.lock

decimalai skills add getcrew44-recommendation-canvas

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/getcrew44-recommendation-canvas/SKILL.md https://app.decimal.ai/s/getcrew44-recommendation-canvas/SKILL.md

Raw file: /s/getcrew44-recommendation-canvas/SKILL.md ↗ · machine-readable summary at /s/getcrew44-recommendation-canvas.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 7

Prompt

We are framing the customer outcome for an AI copy assistant in an email marketing tool. Should the product outcome be written as 'Our platform automatically writes email subject lines' or from the user's vantage point?

What was checked, without and with the skill
Checked	Without skill	With skill
Writes the customer product outcome from the user's first-person perspective.	Missed	Passed

case-08 · gemini-3.6-flash · 8/23/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md366 linessingle file

Purpose

Evaluate and propose AI product solutions using a structured canvas that assesses business outcomes, customer outcomes, problem framing, solution hypotheses, positioning, risks, and value justification. Use this to build a comprehensive, defensible recommendation for stakeholders and decision-makers—especially when proposing AI-powered features or products that carry higher uncertainty and risk.

This is not a feature spec—it's a strategic proposal that articulates why this AI solution is worth building, what assumptions need validating, and how you'll measure success.

Key Concepts

The Recommendation Canvas Framework

Created for Dean Peters' Productside "AI Innovation for Product Managers" class, the canvas synthesizes multiple PM frameworks into one strategic view:

Core Components:

Business Outcome: What's in it for the business?
Product Outcome: What's in it for the customer?
Problem Statement: Persona-centric problem framing
Solution Hypothesis: If/then hypothesis with experiments
Positioning Statement: Value prop and differentiation
Assumptions & Unknowns: What could invalidate this?
PESTEL Risks: Political, Economic, Social, Technological, Environmental, Legal
Value Justification: Why this is worth doing
Success Metrics: SMART metrics to measure impact
What's Next: Strategic next steps

Why This Works

Outcome-driven: Forces clarity on business AND customer value
Hypothesis-centric: Treats solution as a bet to validate, not a commitment
Risk-explicit: Makes assumptions and risks visible upfront
Executive-friendly: Comprehensive but structured for C-level review
AI-appropriate: Especially useful for AI features with high uncertainty

Anti-Patterns (What This Is NOT)

Not a PRD: This is strategic framing, not detailed requirements
Not a business case (yet): It informs the business case but needs validation first
Not a feature list: Focus on outcomes, not capabilities

When to Use This

Proposing a new AI-powered product or feature
Pitching to execs or securing budget/sponsorship
Evaluating whether an AI solution is worth pursuing
Aligning cross-functional stakeholders (product, engineering, data science, business)
After completing initial discovery (you need context to fill this out)

Track record

Benchmark

19 / 22

test cases passed, vs 13 / 22 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

getcrew44 / crew44

daemon/internal/presets/defaultcrew/skills/product/recommendation-canvas/SKILL.md

View on GitHub ↗

License MITImported 8/3/2026Version v1

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deanpeters/recommendation-canvasEvaluate an AI product idea across outcomes, hypotheses, risks, and positioning. Use when deciding whether an AI solution deserves investment or recommendation.Close match+50 pp pass-rate lift

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