Audit Protocol Efficiency

Audit Protocol Efficiency Skill

Purpose: Identify opportunities to improve protocol efficiency by reducing execution time, token usage, and increasing deliverable quality through pattern analysis.

When to Use:

• After protocol compliance audit passes (no CRITICAL/HIGH violations)
• To optimize frequently executed workflow patterns
• When identifying bottlenecks in task execution
• For continuous protocol improvement

MANDATORY PRECONDITION

Check compliance first:

# Run audit-protocol-compliance FIRST
Skill: audit-protocol-compliance

# IF violations contain CRITICAL or HIGH severity:
#   SKIP efficiency audit
#   OUTPUT: {
#     "status": "SKIPPED",
#     "reason": "Major protocol violations must be fixed before efficiency optimization"
#   }
# ELSE:
#   PROCEED with efficiency analysis

Skill Workflow

Overview: Parse timeline → Analyze efficiency patterns → Recommend improvements → Quantify impact

Phase 1: Get Structured Timeline

Invoke parse-conversation-timeline skill:

Skill: parse-conversation-timeline

Phase 2: Analyze Execution Time Opportunities

Query timeline for sequential operations that could be parallel:

# Find sequential Task calls
jq '.timeline[] | select(.type == "tool_use" and .tool.name == "Task")' timeline.json

# Count messages containing Task tools
# IF Task tools spread across >1 message for same purpose:
#   → Parallelization opportunity

# Example: 3 separate messages for architect, quality, style
# → Should be single message with 3 parallel Task calls
# → Savings: (3 - 1) × avg_message_tokens

Detect redundant verifications:

# Find duplicate git status checks
jq '.timeline[] | select(.tool.name == "Bash" and (.tool.input.command | contains("git status")))' timeline.json

# Count occurrences
# IF > 3 git status calls in same state:
#   → Redundancy opportunity

Identify late error detection:

# Find validation after all work done
# Pattern: Multiple file creations followed by single checkstyle check
# Better: Validate after each component for fresh context

Phase 3: Analyze Token Usage Opportunities

Find large tool outputs:

# Query timeline for tool results with large content
jq '.timeline[] | select(.type == "tool_result") | {tool_use_id, content_preview, estimated_tokens}' timeline.json

# IF tool result > 5000 tokens AND not essential:
#   → Context reduction opportunity

Detect duplicate Read operations:

# Find files read multiple times
jq '.timeline[] | select(.type == "tool_use" and .tool.name == "Read") | .tool.input.file_path' timeline.json | sort | uniq -c

# IF same file read 3+ times:
#   → Caching opportunity (read once, reference later)

Identify late protocol file loading:

# Find protocol document reads during IMPLEMENTATION (should be in INIT)
jq '.timeline[] | select(.type == "tool_use" and .tool.name == "Read" and (.tool.input.file_path | contains("docs/project")))' timeline.json

# IF protocol files loaded after INIT:
#   → Prefetching opportunity

Phase 4: Analyze Quality Opportunities

Find missing validation before major operations:

# Pattern: Merge without prior checkstyle validation
# Pattern: Commit without verifying build success
# → Fail-fast opportunities

Detect unclear protocol sequences:

# Count retries, failed attempts, corrections
# IF high retry rate on specific operation:
#   → Clarity improvement opportunity

Phase 4.5: Validate Recommendation Safety

CRITICAL: Before generating recommendations, verify they preserve correctness.

Apply safety checks to EACH proposed optimization:

1. Correctness Preservation Test

Question: Does this optimization sacrifice correctness for speed?

Check for:

• Race conditions from parallelization (agents writing to same files)
• Data loss from caching (stale reads of dynamically updated files)
• Validation gaps from reordering (checks skipped due to early exit)

Examples:

• ❌ UNSAFE: "Parallelize all agent invocations" (may cause merge conflicts)
• ✅ SAFE: "Parallelize independent agent invocations with separate worktrees"

2. Technical Debt Assessment

Question: Could this create maintenance burden or technical debt?

Check for:

• Increased complexity (premature optimization making code harder to maintain)
• Brittle dependencies (tight coupling that breaks with small changes)
• Hidden costs (memory usage from aggressive caching)

Examples:

• ❌ RISKY: "Cache all file reads indefinitely" (memory bloat)
• ✅ SAFE: "Cache protocol files during task execution only"

3. Implementation Feasibility

Question: Is implementation guidance concrete enough to execute safely?

Check for:

• Missing edge case handling
• Unclear ordering constraints
• Incomplete rollback procedures

Examples:

• ❌ VAGUE: "Read files earlier"
• ✅ CONCRETE: "During INIT phase, after task.json creation, read all protocol files matching docs/project/*.md"

4. Side Effect Analysis

Question: Could this recommendation cause unintended side effects?

Check for:

• Workflow disruption (breaking existing automation)
• Context pollution (loading unnecessary information)
• State machine violations (skipping required checkpoints)

Examples:

• ❌ HARMFUL: "Skip validation in SYNTHESIS to save time" (violates checkpoints)
• ✅ HARMLESS: "Validate after each component instead of end" (strengthens checkpoints)

Safety Verdict

For EACH recommendation, assign safety rating:

• SAFE: No correctness/debt concerns, proceed with recommendation
• CONDITIONAL: Safe IF conditions met (document conditions clearly)
• UNSAFE: Reject recommendation, do not include in output

Output only SAFE and CONDITIONAL recommendations.

Phase 5: Generate Recommendations (Enhanced)

For EACH opportunity, provide:

• Specific protocol change (what to add/modify)
• Quantified impact (tokens, time, quality)
• Priority (HIGH/MEDIUM/LOW)
• Safety rating (SAFE/CONDITIONAL/UNSAFE)
• Implementation guidance (concrete steps)
• Anti-pattern examples (NEW: ✅/❌ showing violation vs correct)

Anti-Pattern Format

Show concrete before/after examples:

❌ INEFFICIENT PATTERN (current behavior):

[Exact sequence that caused inefficiency]
→ Impact: [specific measurement]

✅ EFFICIENT PATTERN (recommended):

[Exact sequence that would be efficient]
→ Impact: [specific improvement]

Example for Parallelization:

❌ INEFFICIENT PATTERN:

Message 1 @ 10:23:45 - Task(architect, model=opus)
  Wait for completion...
Message 2 @ 10:25:12 - Task(engineer, model=opus)
  Wait for completion...
Message 3 @ 10:26:38 - Task(formatter, model=opus)
  Wait for completion...

→ Impact: 3 sequential round-trips = ~5-6 minutes total
→ Wasted: 2 round-trips (agents are independent)

✅ EFFICIENT PATTERN:

Message 1 @ 10:23:45 - Parallel invocation:
  - Task(architect, model=opus)
  - Task(engineer, model=opus)
  - Task(formatter, model=opus)
  All complete by 10:25:30

→ Impact: 1 parallel round-trip = ~2 minutes total
→ Saved: 2 round-trips = 3-4 minutes (60% faster)

Example for Fail-Fast:

❌ INEFFICIENT PATTERN:

1. Create FileA.java (200 lines)
2. Create FileB.java (150 lines)
3. Create FileC.java (180 lines)
4. Run checkstyle on all files
   → 53 violations across all files
5. Fix violations with stale context (15% slower)

→ Impact: Context decay = 15% slower fixes
→ Cascading errors: FileC violations caused by FileA patterns

✅ EFFICIENT PATTERN:

1. Create FileA.java (200 lines)
2. Run checkstyle on FileA → 18 violations
3. Fix immediately with fresh context (faster)
4. Create FileB.java (150 lines)
5. Run checkstyle on FileB → 12 violations
6. Fix immediately
7. Create FileC.java → 8 violations (learned from A, B)

→ Impact: Fresh context = 15% faster fixes
→ Learning effect: Fewer violations in later files

Phase 6: Self-Review Recommendations

MANDATORY: Before outputting recommendations, validate their quality.

Apply critical questions to EACH recommendation:

Critical Question 1: Impact Accuracy

Check: Is the quantified impact realistic and verifiable?

Validation:

• Token savings: Verify formula math (messages_eliminated × avg_tokens)
• Time savings: Verify percentage calculation (eliminated / total × 100%)
• Quality impact: Verify claim has evidence (measured retry rate, violation count)

Red flags:

• Suspiciously round numbers (e.g., "exactly 50% faster")
• Missing baseline (savings without showing current cost)
• Unverifiable claims ("much faster", "significantly better")

If unrealistic: Revise to show calculation or remove quantification

Critical Question 2: Implementation Detail

Check: Does implementation guidance contain enough detail to execute safely?

Validation:

• Specific file to modify: ✅ "docs/project/main-agent-coordination.md"
• Specific section to update: ✅ "REQUIREMENTS phase workflow"
• Exact change to make: ✅ "Add requirement: 'MUST launch...'"
• Edge cases handled: ✅ "If agents share dependencies: launch sequentially"

Red flags:

• Vague file references ("update documentation")
• Missing section anchor ("add to protocol somewhere")
• Unclear change ("make this better")

If insufficient detail: Add specific file path, section anchor, exact change text

Critical Question 3: Unintended Consequences

Check: Could this recommendation cause side effects?

Validation:

• Does it break existing automation? (hooks expecting specific patterns)
• Does it violate protocol invariants? (state machine checkpoints)
• Does it conflict with other recommendations? (prefetch vs context reduction)

Red flags:

• Recommendation assumes specific workflow (but multiple workflows exist)
• Optimization bypasses safety mechanism (validation, approval checkpoints)
• Change impacts multiple subsystems (without cross-system verification)

If risky: Add conditional safety requirements or reject

Critical Question 4: Priority Justification

Check: Is priority (HIGH/MEDIUM/LOW) justified by impact magnitude?

Validation:

• HIGH: ≥20% time savings OR ≥10k token savings OR critical quality issue
• MEDIUM: 10-20% time savings OR 5-10k token savings OR moderate quality issue
• LOW: <10% time savings OR <5k token savings OR minor quality issue

Red flags:

• HIGH priority with minimal quantified impact
• LOW priority but huge token/time savings
• Priority based on ease of implementation (not impact)

If misaligned: Adjust priority to match quantified impact

Self-Review Checklist

Before outputting:

• All quantified impacts verified with calculations
• All implementation guidance includes file + section + exact change
• All recommendations checked for side effects
• All priorities aligned with impact thresholds
• No UNSAFE recommendations included
• All CONDITIONAL recommendations document conditions clearly

Output Format (Enhanced)

{
  "status": "COMPLETED",
  "precondition": {
    "compliance_check": "No CRITICAL/HIGH violations",
    "safe_to_optimize": true
  },
  "efficiency_opportunities": [
    {
      "id": "EFF-1",
      "category": "execution_time",
      "priority": "HIGH",
      "safety_rating": "SAFE",
      "issue": "3 separate messages launching agents sequentially",
      "impact": "67% more round-trips than necessary",

      "anti_pattern_example": {
        "inefficient": "Message 1: Task(architect)\nMessage 2: Task(engineer)\nMessage 3: Task(formatter)\n→ Impact: 3 round-trips = ~6 minutes",
        "efficient": "Message 1: Task(architect) + Task(engineer) + Task(formatter)\n→ Impact: 1 round-trip = ~2 minutes\n→ Saved: 4 minutes (67% faster)"
      },

      "recommended_changes": [
        {
          "type": "PROCEDURE",
          "file": "docs/project/main-agent-coordination.md",
          "section": "REQUIREMENTS Phase Workflow",
          "anchor": "#requirements-phase-workflow",
          "change": "Add requirement: 'MUST launch all independent agents in single message with parallel Task calls'",
          "rationale": "Reduce message overhead by eliminating sequential delays",
          "edge_cases": [
            "If agents share dependencies: Launch sequentially",
            "If agent outputs feed into next agent: Launch sequentially"
          ]
        },
        {
          "type": "EXAMPLE",
          "file": "docs/project/main-agent-coordination.md",
          "section": "REQUIREMENTS Phase Examples",
          "anchor": "#requirements-examples",
          "change": "Add ✅/❌ example showing parallel vs sequential invocation",
          "rationale": "Provide clear implementation pattern"
        }
      ],

      "quantified_impact": {
        "token_savings": 8000,
        "token_calculation": "(3 messages - 1 message) × 4000 avg_tokens = 8000",
        "time_savings": "67% fewer round-trips",
        "time_calculation": "(3 round-trips - 1) / 3 = 0.67 = 67%",
        "quality_impact": "None (parallelization maintains quality)"
      },

      "self_review": {
        "impact_accuracy": "VERIFIED: Math correct, baseline documented",
        "implementation_detail": "VERIFIED: File path, section anchor, exact change specified",
        "side_effects": "NONE: Agents use separate worktrees, no conflicts",
        "priority_justified": "HIGH: 67% time savings exceeds 20% threshold"
      }
    },
    {
      "id": "EFF-2",
      "category": "quality",
      "priority": "MEDIUM",
      "safety_rating": "CONDITIONAL",
      "safety_conditions": [
        "Preserve fail-fast only for independent components",
        "Do NOT split validation of tightly coupled code"
      ],
      "issue": "Late validation after creating all files (53 violations)",
      "impact": "Fixes applied with stale context, 15% slower",

      "anti_pattern_example": {
        "inefficient": "1. Create FileA (18 violations)\n2. Create FileB (15 violations)\n3. Create FileC (20 violations)\n4. Run checkstyle: 53 violations\n5. Fix all with stale context\n→ Impact: Context decay = 15% slower",
        "efficient": "1. Create FileA, validate: 18 violations, fix\n2. Create FileB, validate: 12 violations, fix\n3. Create FileC, validate: 8 violations, fix\n→ Impact: Fresh context + learning = 15% faster + 35% fewer violations"
      },

      "recommended_changes": [
        {
          "type": "PROCEDURE",
          "file": "docs/project/main-agent-coordination.md",
          "section": "IMPLEMENTATION Best Practices",
          "anchor": "#implementation-best-practices",
          "change": "Add fail-fast requirement: 'Validate after each logical component, not after all files. Fresh context enables faster fixes and prevents cascading errors.'",
          "rationale": "Fresh context = faster fixes, prevent cascading errors",
          "edge_cases": [
            "For tightly coupled files (interface + impl): Validate together",
            "For test files: Validate with corresponding source file"
          ]
        }
      ],

      "quantified_impact": {
        "token_savings": 0,
        "token_calculation": "No direct token savings (same validation count)",
        "time_savings": "15% faster fixes",
        "time_calculation": "Measured: avg 8 min/violation with stale context, 6.8 min with fresh = 15% faster",
        "quality_impact": "Earlier error detection prevents cascading issues (35% fewer violations in later files)"
      },

      "self_review": {
        "impact_accuracy": "VERIFIED: Based on measured violation fix times",
        "implementation_detail": "VERIFIED: File, section, exact text, edge cases documented",
        "side_effects": "CONDITIONAL: Safe for independent components only",
        "priority_justified": "MEDIUM: 15% time savings in 10-20% threshold"
      }
    }
  ],
  "summary": {
    "total_opportunities": 2,
    "by_priority": {
      "high": 1,
      "medium": 1,
      "low": 0
    },
    "by_safety": {
      "safe": 1,
      "conditional": 1,
      "unsafe_rejected": 0
    },
    "estimated_total_token_savings": 8000,
    "estimated_time_improvement": "Combined: 50% fewer round-trips + 15% faster fixes per iteration"
  },
  "verification": {
    "self_review_completed": true,
    "all_impacts_verified": true,
    "all_implementation_details_complete": true,
    "no_unsafe_recommendations": true
  }
}

Optimization Categories

1. Parallelization

Pattern: Independent operations executed sequentially Detection: Multiple Read/Task calls in separate messages Fix: Batch in single message Savings: (messages_before - 1) × avg_tokens_per_message

2. Prefetching

Pattern: Late loading of predictable dependencies Detection: Protocol files read during IMPLEMENTATION (not INIT) Fix: Load all predictable files during INIT Savings: Avoided round-trips × (read_tokens + response_tokens)

3. Fail-Fast Validation

Pattern: Validate after all work done (late error detection) Detection: Single validation at end with many violations Fix: Validate after each component Savings: 15% time reduction from fresh context

4. Context Reduction

Pattern: Verbose outputs, duplicate reads Detection: Large tool results, repeated file reads Fix: Summarize outputs, cache reads Savings: Reduced token count per message

5. Procedural Clarity

Pattern: Multiple retries, confusion, corrections Detection: High retry rate on specific operations Fix: Add clearer documentation, examples, fail-fast checks Savings: Reduced rework iterations

Quantification Methodology

Token Savings:

Parallelization: (messages_eliminated) × (avg_message_tokens)
Prefetching: (avoided_reads) × (read_tokens + response_tokens)
Context Reduction: (original_tokens - optimized_tokens) × (frequency)

Time Savings:

Round-trip reduction: (eliminated_round_trips) / (total_round_trips) × 100%
Fail-fast improvement: estimated 10-20% per iteration

Quality Impact:

Earlier error detection: Prevents cascading issues
Better context: Improved decision accuracy
Clearer procedures: Reduced confusion and retries

Verification Checklist (Enhanced)

Before outputting efficiency recommendations:

Phase Completion:

• Compliance audit passed (no CRITICAL/HIGH violations)
• Timeline analyzed for parallelization opportunities
• Token usage patterns identified
• Quality improvement opportunities found
• Safety checks applied to all recommendations (Phase 4.5)
• Self-review completed for all recommendations (Phase 6)

Per-Recommendation Quality:

• Each recommendation has safety rating (SAFE/CONDITIONAL)
• Each recommendation has anti-pattern example (❌/✅)
• Each recommendation has quantified impact with calculations shown
• Each recommendation has specific file + section + anchor
• Each recommendation has edge cases documented
• Priority assigned and justified by impact thresholds
• Implementation guidance is concrete and executable
• No UNSAFE recommendations included

Output Quality:

• JSON is valid and well-formed
• Verification section confirms self-review completion
• Summary includes breakdown by_priority and by_safety
• All calculations are mathematically correct

Related Skills

• audit-protocol-compliance: Must pass before efficiency audit
• parse-conversation-timeline: Provides structured data for analysis
• /optimize-doc: Optimizes documentation clarity (complementary, different purpose)