decision-journal

name	decision-journal
description	Decision documentation and learning skill for capturing decision context, rationale, and outcomes
allowed-tools	["Read","Write","Glob","Grep","Bash"]
metadata	{"specialization":"decision-intelligence","domain":"business","category":"knowledge-management","priority":"high","tools-libraries":["markdown","sqlite","pandas","jinja2"]}
graph	{"domains":["domain:business-intelligence"],"skillAreas":["skill-area:strategic-analysis","skill-area:business-analysis","skill-area:change-management"],"roles":["role:strategic-planner","role:business-analyst","role:data-analyst"]}

Decision Journal

Overview

The Decision Journal skill provides systematic capabilities for documenting decisions, tracking outcomes, and extracting organizational learning. It creates a searchable archive of decision context, rationale, and results to improve future decision-making and build institutional knowledge.

Capabilities

• Decision record creation (date, context, options, choice, rationale)
• Outcome tracking and hindsight analysis
• Decision pattern identification
• Calibration tracking over time
• Searchable decision archive
• Lessons learned extraction
• Decision audit trail
• Report generation

Used By Processes

• Decision Documentation and Learning
• Cognitive Bias Debiasing Process
• Decision Quality Assessment

Usage

Decision Record

# Create decision record
decision_record = {
    "metadata": {
        "id": "DEC-2024-0042",
        "title": "Market Expansion into Southeast Asia",
        "date": "2024-01-15",
        "decision_maker": "Executive Committee",
        "stakeholders": ["CEO", "CFO", "VP International", "Regional Directors"],
        "category": "Strategic",
        "tags": ["expansion", "international", "growth"]
    },
    "context": {
        "situation": "Company has saturated domestic market with 35% share. Growth targets require new markets.",
        "time_horizon": "5 years",
        "constraints": ["$50M investment cap", "Must leverage existing product line", "Partner preference over greenfield"],
        "urgency": "Medium - 12-month window before competitor moves"
    },
    "options_considered": [
        {
            "name": "Enter Vietnam first",
            "pros": ["Fastest growing economy", "Favorable demographics", "Existing distributor relationship"],
            "cons": ["Regulatory complexity", "IP protection concerns"],
            "expected_outcomes": {"NPV": , : }
        },
        {
            : ,
            : [, , ],
            : [, ],
            : {: , : }
        },
        {
            : ,
            : [, ],
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            : {: , : }
        }
    ],
    : {
        : ,
        : ,
        : [
            ,
            ,
            
        ],
        : []
    },
    : {
        : [
            {: , : },
            {: , : },
            {: , : }
        ],
        : [, , ],
        : [, , ]
    }
}

Outcome Tracking

# Record outcome
outcome_record = {
    "decision_id": "DEC-2024-0042",
    "review_date": "2025-01-15",
    "actual_outcomes": {
        "milestones_met": 2,
        "milestones_total": 3,
        "revenue": 2500000,
        "market_share": 0.02,
        "unexpected_events": ["COVID variant impact Q3", "Key competitor exited"]
    },
    "assessment": {
        "outcome_vs_expected": "Better than expected",
        "decision_quality_vs_outcome": "Good decision, good outcome",
        "key_learnings": [
            "Hub strategy provided flexibility during disruption",
            "Underestimated talent availability"
        ],
        "would_decide_differently": False,
        "process_improvements": ["Include pandemic scenarios in future expansion decisions"]
    }
}

Pattern Analysis

# Query patterns
pattern_query = {
    "analysis_type": "calibration",
    "filters": {
        "category": "Strategic",
        "date_range": ["2022-01-01", "2024-12-31"],
        "decision_maker": "Executive Committee"
    },
    "metrics": [
        "probability_calibration",
        "npv_accuracy",
        "timeline_accuracy",
        "success_rate_by_category"
    ]
}

Input Schema

{
  "operation": "create|update|outcome|query|report",
  "decision_record": {
    "metadata": "object",
    "context": "object",
    "options_considered": ["object"],
    "decision": "object",
    "implementation": "object"
  },
  "outcome_record": {
    "decision_id": "string",
    "actual_outcomes": "object",
    "assessment": "object"
  },
  "query": {
    "filters": "object",
    "analysis_type": "string"
  

Output Schema

{
  "decision_record": {
    "id": "string",
    "created": "string",
    "status": "string"
  },
  "query_results": {
    "decisions": ["object"],
    "patterns": {
      "calibration": {
        "overconfidence_rate": "number",
        "npv_bias": "number",
        "timeline_bias": "number"
      },
      "success_factors": ["string"],
      "common_mistakes": ["string"]
    }
  
   

Decision Quality Framework

The skill supports the 6-element DQ framework:

1. Appropriate Frame: Problem defined correctly?
2. Creative Alternatives: Good options generated?
3. Meaningful Information: Relevant data gathered?
4. Clear Values: Objectives articulated?
5. Sound Reasoning: Logic applied correctly?
6. Commitment to Action: Resources allocated?

Best Practices

1. Record decisions at the time they're made (not retrospectively)
2. Document dissenting views and rejected options
3. Be explicit about assumptions and uncertainties
4. Separate decision quality from outcome quality
5. Review outcomes systematically on schedule
6. Extract lessons without hindsight bias
7. Share learnings across the organization

Integration Points

• Feeds into Decision Quality Assessor agent
• Connects with Hypothesis Tracker for hypothesis outcomes
• Supports Decision Archivist agent
• Integrates with Calibration Trainer for accuracy improvement