---

name: goal-to-milestones compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent goal to milestones with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: goal-to-milestones, goal to milestones, how do i goal-to-milestones, orchestrate goal-to-milestones, automate goal-to-milestones, agent goal-to-milestones archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

---

Goal To Milestones

Orchestrates intelligent skill selection and execution for goal to milestones workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability.

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘

User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘

When to Use

Use this skill when:

• Orchestrating multi-step workflows that require skill delegation
• Implementing adaptive skill routing based on confidence scores
• Building fallback mechanisms for failed skill executions
• Creating intelligent task decomposition and parallel execution
• Designing skill dependency graphs with automatic resolution
• Implementing skill selection with historical performance weighting
• Building agent systems that need to self-organize around tasks

When NOT to Use

Avoid this skill for:

• Direct task execution without orchestration needs - use individual skills instead
• High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive
• Simple linear workflows without branching or fallback requirements
• Cases where skill metadata is unavailable or unreliable

Core Workflow

1. Parse and Analyze Request - Extract intent, entities, and constraints from user input. Checkpoint: All required parameters must be present and in valid format before proceeding.

2. Score Available Skills - Calculate match scores using multi-factor algorithm:

   • Text similarity between request and skill triggers
   • Historical success rate for similar tasks
   • Skill availability and health status
   • Required dependencies and their availability

   Checkpoint: Skip to fallback if no skill scores above threshold.

3. Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.

4. Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.

5. Return or Fallback - Either return successful result or apply fallback chain:

   • Retry with adjusted parameters
   • Try alternative skill from related-skills
   • Defer to human operator for critical tasks

   Checkpoint: Record outcome with timing and confidence metadata.

Implementation Patterns

Pattern 1: Skill Selection Logic

def decompose_and_assign_milestones(
    goal: str,
    skill_registry: List[Dict],
    constraints: Dict[str, Any]
) -> List[Dict]:
    """Decompose a high-level goal into actionable milestones and assign optimal skills.
    
    Applies Law 2 (Parse at boundary) by validating goal structure and constraints upfront.
    Uses Law 3 (Atomic Predictability) to return immutable milestone objects.
    """
    if not goal or not skill_registry:
        raise ValueError("Goal and skill registry are required for decomposition")
        
    # Parse goal into phases based on domain heuristics
    phases = _extract_goal_phases(goal)
    milestones = []
    
    for phase in phases:
        # Score available skills against phase requirements
        phase_skills = []
        for skill in skill_registry:
            match_score = _calculate_phase_match(phase, skill)
            if match_score >= constraints.get("min_skill_match", 0.6):
                phase_skills.append({
                    "skill_id": skill["id"],
                    "phase": phase,
                    "match_score": match_score,
                    "estimated_effort": skill.get("effort_hours", 1)
                })
        
        # Assign best skill per phase (Law 1: Early exit if no match)
        if not phase_skills:
            milestones.append({
                "phase": phase,
                "status": "blocked",
                "fallback_required": True,
                "assigned_skill": None
            })
        else:
            best = max(phase_skills, key=lambda x: x["match_score"])
            milestones.append({
                "phase": phase,
                "status": "pending",
                "assigned_skill": best["skill_id"],
                "match_score": best["match_score"],
                "dependencies": []
            })
            
    return milestones

Pattern 2: Execution with Fallback

def execute_milestone_chain(
    milestones: List[Dict],
    skill_executor: Callable,
    progress_tracker: Dict
) -> Dict:
    """Execute milestones sequentially with dependency resolution and adaptive fallback.
    
    Implements Law 4 (Fail Fast, Fail Loud) by halting on critical phase failures.
    Updates confidence scores dynamically based on execution outcomes.
    """
    completed_milestones = []
    current_confidence = 0.8
    
    for i, milestone in enumerate(milestones):
        if milestone["status"] == "blocked":
            # Law 1: Early exit for unresolvable phases
            progress_tracker["halted_at"] = i
            progress_tracker["confidence"] = current_confidence
            return progress_tracker
            
        try:
            result = skill_executor(milestone["assigned_skill"], milestone["phase"])
            
            # Law 3: Return new state, never mutate original milestone
            completed_milestones.append({
                "phase": milestone["phase"],
                "status": "completed",
                "result_hash": hash(str(result)),
                "execution_time_ms": result.get("latency", 0)
            })
            
            # Adaptive confidence update (Law 5: Elegant Defense)
            current_confidence *= (0.9 if result.get("success", True) else 0.5)
            
        except SkillTimeoutError:
            # Fallback: Retry with adjusted parameters for this specific milestone
            retry_result = skill_executor(milestone["assigned_skill"], milestone["phase"], retry=True)
            completed_milestones.append({
                "phase": milestone["phase"],
                "status": "completed_retry",
                "result_hash": hash(str(retry_result))
            })
        except CriticalFailureError as e:
            # Law 4: Fail loud, record exact failure point
            progress_tracker["error"] = str(e)
            progress_tracker["confidence"] = current_confidence
            progress_tracker["completed_milestones"] = completed_milestones
            return progress_tracker
            
    progress_tracker["milestones"] = completed_milestones
    progress_tracker["confidence"] = current_confidence
    progress_tracker["status"] = "goal_achieved"
    return progress_tracker

MUST DO

• Always validate skill metadata before selection (Early Exit)
• Implement fallback chain with at least 2 levels (Fallback Skill + Human)
• Log all skill selections with full context for auditability
• Return new data structures instead of mutating inputs (Atomic Predictability)
• Fail immediately with descriptive errors on invalid states
• Update confidence scores after each execution for adaptive routing
• Reference code-philosophy (5 Laws of Elegant Defense) in all logic

MUST NOT DO

• Select skills based on a single factor (e.g., only confidence score)
• Disable fallback mechanisms "temporarily" - this creates fragile systems
• Skip validation of skill dependencies before execution
• Return partial results - either complete success or clear failure
• Use magic numbers for confidence thresholds - make them configurable
• Cache skill selections without considering context changes

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

TL;DR for Code Generation

• Use guard clauses - return early on invalid input before doing work
• Return simple types (dict, str, int, bool, list) - avoid complex nested objects
• Cyclomatic complexity < 10 per function - split anything larger
• Handle null/empty cases explicitly at function top (Early Exit)
• Never mutate input parameters - return new dicts/objects
• Fail fast with descriptive errors - don't try to "patch" bad data
• Reference code-philosophy laws in comments for complex logic
• Include timing and confidence metadata in all return values

Output Template

When applying this skill, produce:

1. Selected Skills - List of skill names with confidence scores
2. Selection Rationale - Why each skill was chosen (match score, history, availability)
3. Execution Plan - Order of execution with dependencies
4. Fallback Strategy - Which fallback skills will be tried and in what order
5. Risk Assessment - Any potential failure points and their impact
6. Timing Estimates - Expected latency including fallback scenarios

Related Skills

| Skill | Purpose | |

---

---

Constraints

MUST DO

• Define clear input/output contracts for every step in the orchestration flow with explicit validation
• Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors
• Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach
• Validate all preconditions before starting — do not proceed if required resources or permissions are missing

MUST NOT DO

• Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible
• Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler
• Never use shared mutable state between parallel workflow branches — communicate via immutable messages only
• Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• OKR Framework (Objectives and Key Results)
• Milestone Planning Best Practices (PMI)
• Agile Sprint Planning Guide
• WBS for Milestone Breakdown
• SMART Goals Framework