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weight-classification

weight-classificationSafety 100Repository

Classify decision weight (Light/Medium/Complete) and adjust gate depth accordingly

1 stars
1.2k downloads
Updated 2/2/2026

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SKILL.md

Weight Classification

Purpose: Match decision rigor to decision stakes. All 7 gates run at appropriate depth.

The Three Weights

WeightTimeWhen to Use
Light<10 minReversible decisions, domain expert, lower stakes
Medium30-40 minModerate stakes, some uncertainty, standard rigor
Complete60-90 minHigh stakes, irreversible, significant uncertainty

Critical: Weight affects depth, not gates. All 7 gates always run.

Weight Selection

When to Suggest Weight

Weight is suggested at the end of Thesis Gate, after the AI understands:

  • Financial/strategic impact
  • Reversibility
  • Time horizon
  • User's domain expertise
  • Complexity revealed

Suggestion Format

Based on what you've described:
- [Key factor 1]
- [Key factor 2]
- [Key factor 3]

I recommend **[Weight]** depth (~[time] minutes).

[Light: Quick pass through all gates - good for reversible decisions where you have expertise]
[Medium: Standard rigor - balances thoroughness with efficiency]
[Complete: Full analysis - for high-stakes, hard-to-reverse decisions]

Does that feel right, or would you prefer [alternative]?

Weight Selection Criteria

FactorLightMediumComplete
Financial impact<$10K$10K-$100K>$100K
ReversibilityEasy (days)Moderate (weeks/months)Hard (years/permanent)
Domain expertiseExpertFamiliarNew territory
Stakeholders affectedJust meTeam/departmentOrganization/customers
Time to know outcomeDays/weeksMonthsYears

If any factor suggests Complete, recommend Complete.

Depth by Gate

Gate 1: Thesis

AspectLightMediumComplete
Questions3-4 key questions5-6 questionsFull exploration
StakeholdersList onlyIdentify conflictsMap relationships
ConstraintsNon-negotiables onlyAll constraintsConstraint flexibility analysis
Success criteria1-2 metrics3-4 metricsComprehensive metrics + timeline

Gate 2: Landscape

AspectLightMediumComplete
Alternatives2-3 + do nothing4-5 + do nothing5-6+ + creative options
Decision PointsSurface level (3-5)Thorough (6-10)Comprehensive (10+)
IntersectionsNote obvious onesMap dependenciesFull intersection analysis
Knowns/UnknownsQuick categorizationStandard matrixDetailed with confidence

Gate 3: Research

AspectLightMediumComplete
Web researchNone - user knowledge1-2 targeted searchesThorough multi-source
Reference classAsk user if knownFind basic referenceDeep reference class analysis
Document reviewSkip unless providedReview key docsComprehensive review
Evidence qualityTrust userBasic assessmentFull quality evaluation

Gate 4: Calibration

AspectLightMediumComplete
Fact/Assumption splitQuick passFull categorizationDetailed with evidence
Confidence levelsHigh/Low onlyHigh/Medium/LowCalibrated percentages
Assumption inventoryCritical onlyImportant + criticalComprehensive inventory
Blind spotsQuick checkIdentify gapsDeep blind spot analysis

Gate 5: Contrarian

AspectLightMediumComplete
Pre-mortem scenariosTop 3 risks5 scenarios5+ with probability/severity
Steel-manSkipTop rejected alternativeAll rejected alternatives
Bias auditQuick self-checkStandard auditFull bias analysis
Second-order effectsNote obviousOne level deepTwo levels deep

Gate 6: Synthesis

AspectLightMediumComplete
Must-Be-TrueTop 3 conditions5-6 conditionsComprehensive conditions
Exit criteria1-2 basic tripwires3-4 specific tripwiresDetailed thresholds + dates
Risk assessmentHigh-level summaryConsolidated risksFull risk matrix
RecommendationBriefStandard with rationaleComprehensive with confidence

Gate 7: Decision

AspectLightMediumComplete
SummaryOne paragraphStandard summaryFull decision summary
DocumentationMinimal artifactStandard artifactComplete artifact
Rationale captureBriefKey factorsVerbatim + deciding factors

Upgrading Mid-Process

User-Initiated Upgrade

User can say "let's go deeper" at any point. AI responds:

Upgrading to [Weight]. Remaining gates will run at [Weight] depth.
[If past gates were Light, note what was skipped that won't be revisited]

Already-completed gates stay as-is. No going back.

AI-Suggested Upgrade

AI should suggest upgrading when it detects:

SignalExampleSuggestion
Emerging complexityMore alternatives than expected"This is more complex than it first appeared. Want to upgrade to Medium/Complete?"
Low confidence on critical assumptionsKey assumption rated Low"A critical assumption has low confidence. Recommend upgrading to get more rigor here."
Surprising research findingsReference class shows high failure rate"The reference class suggests higher risk than expected. Want to go deeper?"
Stakes seem higherFinancial impact larger than initially framed"The stakes seem higher than initially framed. Recommend Complete depth."
User uncertaintyUser expresses doubt repeatedly"You seem uncertain about some key points. Want to spend more time here?"

Upgrade Suggestion Format

⚠️ **Upgrade Suggestion**

I'm noticing [signal]. This suggests [weight] depth might be more appropriate.

Current: [Weight] (~X min remaining)
Suggested: [Weight] (~Y min remaining)

[Brief explanation of what deeper analysis would add]

Continue at current depth, or upgrade?

Weight in Artifact

Document the weight in the decision artifact header:

# Decision: [Statement]

**Weight:** [Light/Medium/Complete]
**Estimated time:** [X minutes]
**Upgraded:** [Yes - from Light at Gate 3 / No]

Commands

Weight can be specified when starting:

/decide              → AI suggests weight after Thesis
/decide --light      → Start at Light (can upgrade)
/decide --medium     → Start at Medium (can upgrade)
/decide --complete   → Start at Complete

If weight is pre-specified, skip the weight suggestion step in Thesis but still allow upgrades.

Install

Download ZIP
Requires askill CLI v1.0+

AI Quality Score

95/100Analyzed 2/11/2026

An exceptionally well-structured skill that defines a tiered decision-making framework. It provides clear criteria, specific depth adjustments for every stage of the process, and templates for AI-user interaction.

100
98
75
100
95

Metadata

Licenseunknown
Version-
Updated2/2/2026
Publisherwme3

Tags

observability