Use when making high-stakes decisions under uncertainty that require stakeholder buy-in. Invoke when evaluating strategic options (build vs buy, market entry, resource allocation), quantifying tradeoffs with uncertain outcomes, justifying investments with expected value analysis, pitching recommendations to decision-makers, or creating business cases with cost-benefit estimates. Use when user mentions "should we", "ROI analysis", "make a case for", "evaluate options", "expected value", "justify decision", or needs to combine estimation, decision analysis, and persuasive communication.
Quantify uncertain choices with expected value analysis, then package recommendations into persuasive stakeholder narratives. Use when evaluating strategic options, creating business cases, or justifying investments triggered by phrases like "should we," "ROI analysis," or "make a case for.
/plugin marketplace add lyndonkl/claude/plugin install lyndonkl-thinking-frameworks-skills@lyndonkl/claudeThis skill inherits all available tools. When active, it can use any tool Claude has access to.
resources/evaluators/rubric_chain_estimation_decision_storytelling.jsonresources/examples/build-vs-buy-analytics-platform.mdresources/methodology.mdresources/template.mdSystematically quantify uncertain choices, make defensible decisions using expected value analysis, and communicate recommendations through persuasive narratives. This meta-skill chains estimation → decision → storytelling to transform ambiguous options into clear, stakeholder-ready recommendations.
Trigger phrases: "should we", "evaluate options", "make a case for", "ROI analysis", "expected value", "justify decision", "quantify tradeoffs", "pitch to", "business case", "cost-benefit", "probability-weighted"
A three-phase meta-skill that combines:
Quick Example:
# Should we build custom analytics or buy a SaaS tool?
## Estimation
Build custom: $200k-$400k dev cost (60% likely $300k), $50k/year maintenance
Buy SaaS: $120k/year subscription, $20k implementation
## Decision
Expected 3-year cost:
- Build: $300k + (3 × $50k) = $450k
- Buy: $20k + (3 × $120k) = $380k
- Difference: $70k savings with Buy
Expected value with risk adjustment:
- Build: 30% chance of 2x cost overrun → $510k expected
- Buy: 95% confidence in pricing → $380k expected
- Recommendation: Buy (lower cost, lower risk)
## Story
"We evaluated building custom analytics vs. buying a SaaS solution. While building seems cheaper initially ($300k vs. $380k over 3 years), custom development carries significant risk—30% of similar projects experience 2x cost overruns, bringing expected cost to $510k. The SaaS solution offers predictable pricing, faster time-to-value (2 months vs. 8 months), and proven reliability. Recommendation: Buy the SaaS tool, saving $130k in expected costs and delivering value 6 months earlier."
Copy this checklist and track your progress:
Chain Estimation → Decision → Storytelling Progress:
- [ ] Step 1: Clarify decision and gather inputs
- [ ] Step 2: Estimate uncertain variables
- [ ] Step 3: Analyze decision with expected value
- [ ] Step 4: Craft persuasive narrative
- [ ] Step 5: Validate and deliver
Step 1: Clarify decision and gather inputs
Define the choice (what decision needs to be made?), identify alternatives (2-5 options to compare), list uncertainties (what variables are unknown or probabilistic?), determine audience (who needs to be convinced?), and clarify constraints (budget, timeline, requirements). Ensure the decision is actionable and the options are mutually exclusive.
Step 2: Estimate uncertain variables
For each alternative, quantify costs (fixed, variable, opportunity), estimate benefits (revenue, savings, productivity), assign probabilities to scenarios (best case, base case, worst case), and perform sensitivity analysis (which inputs matter most?). Use ranges rather than point estimates. For simple cases → Use resources/template.md for structured estimation. For complex cases → Study resources/methodology.md for advanced techniques (Monte Carlo, decision trees, real options).
Step 3: Analyze decision with expected value
Calculate expected outcomes for each alternative (probability-weighted averages), compare using decision criteria (NPV, payback period, IRR, utility), identify dominant option (best expected value or risk-adjusted return), and test robustness (does conclusion hold across reasonable input ranges?). Document assumptions explicitly. See Common Patterns for decision-type specific approaches.
Step 4: Craft persuasive narrative
Structure story with: problem statement (why this decision matters), alternatives considered (show you did the work), analysis summary (key numbers and logic), recommendation (clear choice with reasoning), next steps (what happens if approved). Tailor to audience: executives want bottom line and risks, technical teams want methodology and assumptions, finance wants numbers and sensitivity.
Step 5: Validate and deliver
Self-check using resources/evaluators/rubric_chain_estimation_decision_storytelling.json. Verify: estimates are justified with sources/logic, probabilities are calibrated (not overconfident), expected value calculation is correct, sensitivity analysis identifies key drivers, narrative is clear and persuasive, assumptions are stated explicitly, risks and limitations are acknowledged. Minimum standard: Score ≥ 3.5. Create chain-estimation-decision-storytelling.md output file with full analysis and recommendation.
For build vs buy decisions:
For market entry decisions:
For resource allocation:
For technology decisions:
For hiring/staffing decisions:
Do:
Don't:
Common Pitfalls:
resources/template.md - Structured estimation → decision → story frameworkresources/methodology.md - Advanced techniques (Monte Carlo, decision trees, real options)resources/examples/ - Worked examples (build vs buy, market entry, hiring decision)resources/evaluators/rubric_chain_estimation_decision_storytelling.jsonchain-estimation-decision-storytelling.mdCreating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.
Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.