Refactor high-complexity React components in Dify frontend. Use when `pnpm analyze-component...
npx skills add liqiongyu/lenny_skills_plus --skill "evaluating-trade-offs"
Install specific skill from multi-skill repository
# Description
Evaluate trade-offs and produce a Trade-off Evaluation Pack (trade-off brief, options+criteria matrix, all-in cost/opportunity cost table, impact ranges, recommendation, stop/continue triggers). Use for tradeoff/trade-off, pros and cons, cost-benefit, opportunity cost, build vs buy, ship fast vs ship better, continue vs stop (sunk costs). Category: Leadership.
# SKILL.md
name: "evaluating-trade-offs"
description: "Evaluate trade-offs and produce a Trade-off Evaluation Pack (trade-off brief, options+criteria matrix, all-in cost/opportunity cost table, impact ranges, recommendation, stop/continue triggers). Use for tradeoff/trade-off, pros and cons, cost-benefit, opportunity cost, build vs buy, ship fast vs ship better, continue vs stop (sunk costs). Category: Leadership."
Evaluating Trade-offs
Scope
Covers
- Turning an ambiguous “pros/cons” debate into a decision-ready trade-off evaluation
- Comparing options using all-in cost (not just dollars) and explicit opportunity cost
- Using order-of-magnitude estimates (ranges + confidence) instead of false precision
- Stress-testing decisions with thought experiments (pre-mortems, reversibility, “worse first” dips)
- Avoiding sunk-cost traps with a clean stop/continue decision rule
When to use
- “Help me evaluate this trade-off and recommend a path.”
- “Create a pros/cons that actually leads to a decision.”
- “Compare options with cost/impact ranges and key assumptions.”
- “We’re debating speed vs quality—what’s the right trade and how do we manage the dip?”
- “Should we keep investing in this project, or stop? (Sunk cost question.)”
When NOT to use
- You need to clarify what problem you’re solving (use problem-definition).
- You need a full cross-functional decision process (use running-decision-processes).
- You’re prioritizing across many initiatives (use prioritizing-roadmap).
- You’re cutting scope to hit a date/timebox (use scoping-cutting).
- The decision is personal/legal/HR/financial advice (escalate to qualified humans).
Inputs
Minimum required
- The trade-off / decision statement (one sentence) and a decision date (or “by EOW”)
- 2–4 options you’re choosing between (include “do nothing” if plausible)
- Constraints + non-negotiables (budget, headcount, policy, deadlines, customer commitments)
- What “good” means (success metrics + guardrails) and the time horizon you care about
- What you already know (evidence) + biggest unknowns (assumptions that drive the choice)
Missing-info strategy
- Ask up to 5 questions from references/INTAKE.md (3–5 at a time).
- If inputs are unavailable, proceed with explicit assumptions and label unknowns that would change the recommendation.
Outputs (deliverables)
Produce a Trade-off Evaluation Pack in Markdown (in-chat; or as files if requested) in this order:
1) Trade-off brief (decision, why now, options, constraints, horizon, stakeholders)
2) Options + criteria matrix (criteria + weights/guardrails; option notes)
3) All-in cost + opportunity cost table (money, people/time, eng effort, complexity, displacement)
4) Impact ranges (order-of-magnitude) (upside/downside ranges, confidence, key assumptions)
5) Worse-first + mitigation plan (expected dip, leading indicators, mitigations, comms)
6) Recommendation + stop/continue triggers (decision, rationale, review date, kill/continue criteria)
7) Risks / Open questions / Next steps (always included)
Templates: references/TEMPLATES.md
Expanded guidance: references/WORKFLOW.md
Workflow (7 steps)
1) Frame the trade-off (make it decidable)
- Inputs: User request; references/INTAKE.md.
- Actions: Write the decision in one sentence (“We are choosing X vs Y by DATE to achieve GOAL”). List constraints/non-negotiables. Confirm the decision owner and who must live with the outcome.
- Outputs: Trade-off brief (decision, why now, constraints, stakeholders).
- Checks: You can answer: “What exactly are we deciding, by when, and for what outcome?”
2) Define what you’re optimizing (criteria + horizon)
- Inputs: Goals, metrics, guardrails; time horizon.
- Actions: Pick 4–8 criteria (include at least one guardrail like trust/reliability/cost). Decide weights only if it changes the decision. Explicitly name what you’re not optimizing for.
- Outputs: Options + criteria matrix (criteria definitions + weights/guardrails).
- Checks: Criteria reflect real trade-offs (not “everything is important”); horizon is explicit (e.g., 90 days vs 2 years).
3) Build the all-in cost + opportunity cost view
- Inputs: Team capacity, budget, dependencies, timelines.
- Actions: Estimate all-in cost (cash, headcount time, eng effort, maintenance, coordination). List the opportunity cost: what won’t be done if you choose each option.
- Outputs: All-in cost + opportunity cost table.
- Checks: Costs include “hidden” items (maintenance/on-call, tooling, cross-team coordination, switching costs).
4) Estimate impact with ranges (avoid false precision)
- Inputs: Any baseline numbers; evidence; assumptions.
- Actions: For each option, estimate upside/downside as ranges and note confidence. Prefer order-of-magnitude comparisons (10× vs 1.1×). Identify the 2–3 assumptions that drive the model.
- Outputs: Impact ranges table (range, confidence, key assumptions).
- Checks: No fake decimals; uncertainty is explicit; the decision is driven by a few key drivers you can name.
5) Run “thought experiments” (think more, build less)
- Inputs: Options, assumptions, risks.
- Actions: Do a pre-mortem for the top 1–2 options (“It failed—why?”). Identify the cheapest evidence to de-risk the biggest assumption (data pull, customer calls, small prototype, timeboxed spike). Decide if this should be a thought experiment only (no build) vs a real experiment.
- Outputs: Assumption list + minimal validation plan (if needed).
- Checks: Proposed tests are the smallest that could change your mind; you’re not shipping an “obvious loser” experiment.
6) Account for “worse first” + sunk costs
- Inputs: Expected short-term impacts; current investment/sunk costs.
- Actions: Name any “worse-first” dip (short-term pain) and plan mitigations/leading indicators. Apply a sunk-cost reset: “If we weren’t already doing this, would we start today?” Define stop/continue triggers and a review date.
- Outputs: Worse-first plan + stop/continue triggers.
- Checks: The plan anticipates the dip; continuation logic ignores sunk costs and focuses on future ROI and strategic fit.
7) Recommend, commit, and quality-gate
- Inputs: All artifacts above.
- Actions: Write the recommendation with rationale and explicit trade-offs (what you will stop doing). Add risks, open questions, and next steps with owners/dates. Run references/CHECKLISTS.md and score with references/RUBRIC.md.
- Outputs: Final Trade-off Evaluation Pack.
- Checks: A stakeholder can read this async and make (or support) the decision without re-litigating the debate.
Quality gate (required)
- Use references/CHECKLISTS.md and references/RUBRIC.md.
- Always include: Risks, Open questions, Next steps.
Examples
Example 1 (resource allocation): “Should we invest in SEO or paid acquisition for the next 2 quarters? Build a trade-off pack with all-in cost, ROI speed, and assumptions.”
Expected: all-in cost vs alternatives, order-of-magnitude impact ranges, and a clear recommendation + review date.
Example 2 (speed vs quality): “We can ship v1 next week with rough edges or delay 3 weeks to ship ‘noteworthy’. Evaluate the trade-off and propose a worse-first mitigation plan if we ship now.”
Expected: explicit criteria/guardrails (trust/support load), dip plan, and stop/continue triggers if metrics degrade.
Boundary example: “Help me decide if I should leave my job.”
Response: this skill is for organizational/product leadership trade-offs; suggest a personal decision framework or coach instead.
# Supported AI Coding Agents
This skill is compatible with the SKILL.md standard and works with all major AI coding agents:
Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.