ReddRadar
Agent skill
decision-journal
Decision documentation and learning skill for capturing decision context, rationale, and outcomes
Install this agent skill to your Project
npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/business/decision-intelligence/skills/decision-journal
Metadata
Additional technical details for this skill
- domain
- business
- category
- knowledge-management
- priority
- high
- specialization
- decision-intelligence
- tools libraries
-
[ "markdown", "sqlite", "pandas", "jinja2" ]
SKILL.md
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": 25000000, "probability_success": 0.65}
},
{
"name": "Enter Singapore as hub",
"pros": ["Strong IP protection", "Gateway to ASEAN", "Talent availability"],
"cons": ["Higher costs", "Smaller direct market"],
"expected_outcomes": {"NPV": 18000000, "probability_success": 0.80}
},
{
"name": "Defer 1 year",
"pros": ["More market intelligence", "Economic uncertainty resolution"],
"cons": ["Competitor advantage", "Momentum loss"],
"expected_outcomes": {"NPV": 15000000, "probability_success": 0.75}
}
],
"decision": {
"choice": "Enter Singapore as hub",
"rationale": "Higher probability of success outweighs NPV difference. Hub strategy enables sequential expansion with lower risk.",
"key_assumptions": [
"Singapore cost structure remains competitive",
"ASEAN trade agreements stable",
"Partner availability in year 2-3"
],
"dissenting_views": ["CFO preferred Vietnam for higher NPV potential"]
},
"implementation": {
"key_milestones": [
{"date": "2024-Q2", "milestone": "Entity established"},
{"date": "2024-Q4", "milestone": "First local hire"},
{"date": "2025-Q2", "milestone": "First revenue"}
],
"success_metrics": ["Revenue", "Market share", "Partner pipeline"],
"review_dates": ["2024-07-15", "2025-01-15", "2025-07-15"]
}
}
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"]
}
},
"report_path": "string"
}
Decision Quality Framework
The skill supports the 6-element DQ framework:
- Appropriate Frame: Problem defined correctly?
- Creative Alternatives: Good options generated?
- Meaningful Information: Relevant data gathered?
- Clear Values: Objectives articulated?
- Sound Reasoning: Logic applied correctly?
- Commitment to Action: Resources allocated?
Best Practices
- Record decisions at the time they're made (not retrospectively)
- Document dissenting views and rejected options
- Be explicit about assumptions and uncertainties
- Separate decision quality from outcome quality
- Review outcomes systematically on schedule
- Extract lessons without hindsight bias
- 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
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