predict

Intent

Forecast future states or outcomes for a target based on current state, historical patterns, and assumed conditions. This capability consolidates all forecasting tasks (risk, impact, time, etc.) into a single parameterized operation.

Success criteria:

• Prediction for requested target and horizon provided
• Probability or confidence assigned to prediction
• Alternative outcomes considered
• Assumptions explicitly stated

Compatible schemas:

• schemas/output_schema.yaml

Inputs

Parameter	Required	Type	Description
target	Yes	string	What to predict (metric, state, outcome, event)
horizon	No	string	Prediction timeframe (e.g., "1 week", "next release", "end of sprint")
conditions	No	object	Assumed conditions for prediction
method	No	string	Prediction approach (trend, model, heuristic)

Procedure

1. Define prediction target: Clarify what outcome is being predicted

   • Specify the metric, state, or event to forecast
   • Establish the prediction horizon
   • Note any boundary conditions

2. Gather historical data: Collect relevant past observations

   • Identify patterns and trends
   • Note data quality and coverage
   • Look for relevant precedents

3. Establish conditions: Document assumptions about the future

   • Note what must remain constant
   • Identify key variables that could change
   • Consider external factors

4. Generate prediction: Forecast the most likely outcome

   • Apply trend analysis or modeling
   • Calculate probability of primary prediction
   • Identify alternative outcomes

5. Consider alternatives: Evaluate other possible outcomes

   • List plausible alternative scenarios
   • Assign rough probabilities to alternatives
   • Note what would cause each alternative

6. Ground prediction: Document evidence and reasoning

   • Reference data supporting the prediction
   • Note the reasoning chain
   • Explicitly state all assumptions

Output Contract

Return a structured object:

prediction:
  outcome: any  # Predicted value, state, or event
  probability: number  # 0.0-1.0 likelihood of this outcome
  horizon: string  # When this prediction applies
alternatives:
  - outcome: any  # Alternative outcome
    probability: number  # Likelihood
    trigger: string  # What would cause this
trajectory:  # Optional: predicted path to outcome
  - timestamp: string
    state: any
reasoning: string  # Explanation of prediction logic
confidence: 0..1  # Confidence in prediction methodology
evidence_anchors: ["file:line", "data:source"]
assumptions: []  # Critical assumptions
invalidation_conditions: []  # What would invalidate this prediction

Field Definitions

Field	Type	Description
prediction.outcome	any	The predicted future state or value
prediction.probability	number	Likelihood of primary outcome
prediction.horizon	string	Timeframe for prediction
alternatives	array	Other possible outcomes with triggers
trajectory	array	Path from current to predicted state
reasoning	string	Explanation of prediction logic
invalidation_conditions	array	What would make prediction invalid

Examples

Example 1: Predict Sprint Completion

Input:

target: "sprint story completion"
horizon: "end of sprint (5 days)"
conditions:
  team_size: 4
  no_major_blockers: true

Output:

prediction:
  outcome: "7 of 10 stories completed (70%)"
  probability: 0.65
  horizon: "end of sprint (5 days)"
alternatives:
  - outcome: "10 of 10 stories completed (100%)"
    probability: 0.15
    trigger: "No unexpected blockers, all estimates accurate"
  - outcome: "5 of 10 stories completed (50%)"
    probability: 0.20
    trigger: "Major blocker or team member unavailable"
trajectory:
  - timestamp: "Day 2"
    state: "3 stories completed"
  - timestamp: "Day 4"
    state: "5-6 stories completed"
  - timestamp: "Day 5"
    state: "7 stories completed"
reasoning: "Based on team velocity of 1.5 stories/day and current progress, projecting linear completion. Historical sprints show 70% completion rate."
confidence: 0.7
evidence_anchors:
  - "tool:jira:sprint-history"
  - "tool:jira:current-burndown"
assumptions:
  - "Team velocity remains consistent"
  - "Story estimates are accurate"
  - "No unplanned work introduced"
invalidation_conditions:
  - "Team member becomes unavailable"
  - "Critical production incident"
  - "Scope change to sprint"

Example 2: Predict System Failure

Input:

target: "disk space exhaustion"
horizon: "next 30 days"
conditions:
  growth_rate: "current trend"

Output:

prediction:
  outcome: "Disk will reach 95% capacity"
  probability: 0.85
  horizon: "18-22 days"
alternatives:
  - outcome: "Disk reaches 100% (service outage)"
    probability: 0.60
    trigger: "No cleanup or expansion action taken"
  - outcome: "Capacity remains stable"
    probability: 0.10
    trigger: "Log rotation reduces growth rate"
trajectory:
  - timestamp: "Day 7"
    state: "85% capacity"
  - timestamp: "Day 14"
    state: "90% capacity"
  - timestamp: "Day 20"
    state: "95% capacity"
reasoning: "Current growth rate of 0.5% per day on 80% utilized 500GB disk. Linear projection shows critical threshold in ~20 days."
confidence: 0.8
evidence_anchors:
  - "command:df -h /data"
  - "tool:monitoring:disk-trend-7d"
assumptions:
  - "Growth rate continues at current pace"
  - "No bulk data imports or exports"
  - "Log retention policy unchanged"
invalidation_conditions:
  - "Growth rate changes significantly"
  - "Disk expanded or data archived"
  - "Application behavior changes"

Verification

• Prediction includes specific outcome and probability
• Horizon is clearly specified
• At least one alternative outcome considered
• Assumptions are explicitly documented
• Evidence supports the prediction reasoning

Verification tools: Read (to verify historical data references)

Safety Constraints

• mutation: false
• requires_checkpoint: false
• requires_approval: false
• risk: low

Capability-specific rules:

• Always provide probability, never claim certainty about future
• Document assumptions that could invalidate prediction
• Consider alternative outcomes, especially failure modes
• Do not predict beyond available data horizon without noting extrapolation
• Flag when prediction confidence is too low to be actionable

Composition Patterns

Commonly follows:

• measure - Measurements provide basis for predictions
• observe - Current state observations inform predictions
• discover - Discovered patterns enable predictions

Commonly precedes:

• plan - Predictions inform planning decisions
• compare - Predicted outcomes can be compared
• simulate - Predictions guide simulation scenarios

Anti-patterns:

• Never use predict for current state (use measure or observe)
• Avoid predict when historical data is insufficient

Workflow references:

• See reference/workflow_catalog.yaml#digital_twin_sync_loop for forecasting in digital twins