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Get Started Free →Lean Canvas (Ash Maurya) — startup adaptation of the Business Model Canvas for capturing assumptions and unfair advantages in early-stage products. Use at the idea, pre-PMF, or pivot stage, or when you need a 1-page model fast.
.claude/skills/borghei-lean-canvas/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 98% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 102% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 160% | 0% |
| case-20 | ✗→✓ | ▲ Improved | 13% | 0% |
| case-06 | ✓→✓ | = Same ✓ | 115% | 0% |
A 9-block startup canvas adapted from BMC. Replaces 4 BMC blocks (Key Resources, Activities, Partnerships, Customer Relationships) with startup-focused ones (Problem, Solution, Key Metrics, Unfair Advantage).
| Lean Canvas | BMC | |-------------|-----| | Problem | Key Partnerships | | Solution | Key Activities | | Key Metrics | Key Resources | | Unfair Advantage | Customer Relationships |
Same outer shape; different inner emphasis. Use Lean when problem + unfair advantage matter more than ops detail.
Before building the canvas, confirm these inputs. If any is unknown or vague, ASK — do not assume:
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
Start with Problem and Customer Segments. If you can't list the top 3 problems for a specific segment, you don't have a startup — you have an idea.
Within Problem block, list how customers solve this today. If the answer is "they don't / they live with it," that's important signal.
A single sentence that:
Template: "Outcome] for segment] that differentiator]."
Limit to 3. Founders always want to list 10. Resist.
The hardest block. What do you have that no one can easily copy?
If empty: most startups have nothing yet. Mark explicitly + plan how to build one.
Fill remaining blocks. Be specific.
lean_canvas_validator.pyAudit for: missing blocks, generic content, no early adopter named, no existing alternatives, no Key Metrics, vague Unfair Advantage.
bashpython3 project-management/strategy-frameworks/lean-canvas/scripts/lean_canvas_validator.py \ --input canvas.json --format markdown
| Use Lean Canvas | Use BMC | |-----------------|---------| | < $1M ARR or pre-revenue | Mature company / division | | Single segment, single product | Multi-product or multi-segment | | Problem-solution fit hunting | Operating + scaling | | Pivot conversations | Strategic planning | | 20-minute sketch | Half-day planning workshop |
Early adopters are not "future mainstream users." They are:
If you can't name 5 specific early adopters by name + workaround, you're not at problem-solution fit yet.
Counts:
Doesn't count:
If your only unfair advantage is "speed" or "execution," that's a weakness as a moat.
references/lean-canvas-framework.md — the 9 blocks deep, comparison to BMCreferences/lean-startup-anti-patterns.md — common mistakes + worked fixesproject-management/strategy-frameworks/business-model-canvas — operating-scale variantproject-management/discovery/value-proposition-canvas — deeper on UVPproject-management/discovery/identify-assumptions — assumption registerproject-management/discovery/pre-mortem — risk discoveryproject-management/execution/north-star-metric — Key Metric definitionc-level-advisor/ceo-advisor — strategic context| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | fail→pass | 14,936 | 21,290 | +43% | 1 | 1 | 0% | 2,389 | 4,724 | +98% | 0 | 0 | — |
case-01 | fail→fail | 22,429 | 28,820 | +28% | 1 | 1 | 0% | 3,478 | 6,105 | +76% | 0 | 0 | — |
case-02 | fail→fail | 19,679 | 22,831 | +16% | 1 | 1 | 0% | 3,033 | 5,166 | +70% | 0 | 0 | — |
case-03 | fail→fail | 18,868 | 21,433 | +14% | 1 | 1 | 0% | 2,961 | 4,813 | +63% | 0 | 0 | — |
case-05 | fail→pass | 14,129 | 16,953 | +20% | 1 | 1 | 0% | 2,071 | 4,174 | +102% | 0 | 0 | — |
case-06 | pass→pass | 17,283 | 26,325 | +52% | 1 | 1 | 0% | 2,543 | 5,456 | +115% | 0 | 0 | — |
case-07 | pass→pass | 22,680 | 20,493 | -10% | 1 | 1 | 0% | 3,565 | 4,756 | +33% | 0 | 0 | — |
case-08 | pass→pass | 18,237 | 26,739 | +47% | 1 | 1 | 0% | 2,600 | 5,609 | +116% | 0 | 0 | — |
case-09 | pass→pass | 31,358 | 31,299 | -0% | 1 | 1 | 0% | 6,188 | 7,874 | +27% | 0 | 0 | — |
case-10 | pass→pass | 17,409 | 25,898 | +49% | 1 | 1 | 0% | 2,583 | 5,587 | +116% | 0 | 0 | — |
case-11 | pass→pass | 16,819 | 19,216 | +14% | 1 | 1 | 0% | 2,474 | 4,571 | +85% | 0 | 0 | — |
case-12 | pass→pass | 10,639 | 19,904 | +87% | 1 | 1 | 0% | 1,644 | 4,809 | +193% | 0 | 0 | — |
case-13 | fail→pass | 13,316 | 24,201 | +82% | 1 | 1 | 0% | 2,136 | 5,544 | +160% | 0 | 0 | — |
case-14 | pass→pass | 16,261 | 26,436 | +63% | 1 | 1 | 0% | 2,524 | 5,749 | +128% | 0 | 0 | — |
case-15 | pass→pass | 16,066 | 18,649 | +16% | 1 | 1 | 0% | 2,535 | 4,665 | +84% | 0 | 0 | — |
case-16 | pass→pass | 14,775 | 23,246 | +57% | 1 | 1 | 0% | 2,361 | 5,230 | +122% | 0 | 0 | — |
case-17 | pass→pass | 19,706 | 30,336 | +54% | 1 | 1 | 0% | 3,055 | 6,466 | +112% | 0 | 0 | — |
case-18 | pass→pass | 18,734 | 28,914 | +54% | 1 | 1 | 0% | 2,870 | 6,109 | +113% | 0 | 0 | — |
case-19 | pass→pass | 13,637 | 27,885 | +104% | 1 | 1 | 0% | 2,245 | 6,319 | +181% | 0 | 0 | — |
case-20 | fail→pass | 16,109 | 7,726 | -52% | 1 | 1 | 0% | 2,604 | 2,945 | +13% | 0 | 0 | — |
case-21 | fail→fail | 15,413 | 23,114 | +50% | 1 | 1 | 0% | 2,286 | 5,342 | +134% | 0 | 0 | — |
case-22 | pass→pass | 14,176 | 18,953 | +34% | 1 | 1 | 0% | 2,161 | 4,634 | +114% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +18 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.