Meta Decision Analysis Agent Skill
Apply structured decision analysis using decision matrices, decision trees, expected value, and multi-criteria decision analysis (MCDA). Use this skill when the user faces a complex decision with multiple options and criteria, needs to compare alternatives objectively, quantify risk vs reward, or facilitate group decisions — even if they say 'which option should we choose', 'help me decide', 'how do we compare these options', or 'what's the expected outcome'.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/asgard-ai-platform/skills --skill meta-decision-analysisWhat comes with it
16 601 bytes besides the instructionThe instruction itself
8 sections, as written by the authorDecision Analysis
Framework
IRON LAW: Make Criteria and Weights Explicit BEFORE Evaluating Options
Choosing criteria after seeing the options lets bias sneak in — you
unconsciously weight criteria that favor your preferred option.
Define criteria, assign weights, THEN score options.
Decision Matrix (Weighted Scoring)
- List alternatives (3-6 options including "do nothing")
- Define criteria (4-8 factors that matter)
- Weight criteria (must sum to 100%)
- Score each option per criterion (1-5 or 1-10)
- Calculate weighted total = Σ(score × weight)
- Sensitivity check: Does the winner change if you adjust the top-weighted criterion?
Decision Tree (Sequential Decisions Under Uncertainty)
For decisions with uncertainty and sequential steps:
- Map decision nodes (squares) and chance nodes (circles)
- Assign probabilities to chance outcomes (must sum to 1.0)
- Assign payoffs to terminal nodes
- Calculate Expected Value = Σ(probability × payoff)
- Choose the branch with highest EV (or best risk-adjusted outcome)
Multi-Criteria Decision Analysis (MCDA)
For complex decisions with competing stakeholder priorities:
- Each stakeholder defines their criteria and weights independently
- Aggregate into a combined weighted matrix
- Identify where stakeholders agree (easy decisions) and disagree (requires negotiation)
Output Format
# Decision Analysis: {Decision}
## Alternatives
1. {Option A}
2. {Option B}
3. {Option C}
## Decision Matrix
| Criterion | Weight | Option A | Option B | Option C |
|-----------|--------|----------|----------|----------|
| {criterion 1} | {X%} | {1-5} | {1-5} | {1-5} |
| **Weighted Total** | 100% | **{total}** | **{total}** | **{total}** |
## Sensitivity Analysis
- If {criterion} weight changes from X% to Y%, winner changes from {A} to {B}
## Recommendation
{Winner with rationale and key trade-offs acknowledged}
Gotchas
- "Do nothing" is always an option: Include it as a baseline. Sometimes the best decision is to wait.
- Scores are subjective: A score of "4" from one person ≠ "4" from another. Calibrate by defining what each score means before scoring.
- Expected value ignores risk preference: EV of $50 (certain) vs EV of $50 (50% chance of $0, 50% chance of $100) are equal by EV but feel very different. For high-stakes decisions, use risk-adjusted metrics.
- Analysis paralysis: Decision analysis should accelerate decisions, not delay them. Set a time limit for the analysis.
References
- For decision tree software tools, see
references/decision-tools.md
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How to use it
Take asgard-ai-platform/meta-decision-analysis from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.