CL

claudemem-orchestration

Coordinates multiple agents using shared claudemem output to enable parallel code analysis and consensus building.

Install

mkdir -p .claude/skills/claudemem-orchestration && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/6213" && unzip -o skill.zip -d .claude/skills/claudemem-orchestration && rm skill.zip

Installs to .claude/skills/claudemem-orchestration

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Use when orchestrating multi-agent code analysis with claudemem. Run claudemem once, share output across parallel agents. Enables parallel investigation, consensus analysis, and role-based command mapping.
205 chars✓ has a “when” trigger
Intermediate

Key capabilities

  • Run claudemem once to get a structural overview
  • Write claudemem output to a shared file in a session directory
  • Launch agents in parallel that read the same shared file
  • Consolidate results using consensus analysis
  • Distribute claudemem output to role-specific agents
  • Consolidate feedback from multiple agents into a shared file

How it works

This skill runs claudemem once to generate a structural overview, writes the output to a shared file, and then launches parallel agents that read this file to perform their analyses.

Inputs & outputs

You give it
claudemem commands and agent prompts
You get back
consolidated analysis report and submitted feedback

When to use claudemem-orchestration

  • Orchestrate multi-agent code reviews
  • Perform parallel codebase investigation
  • Sync output for multi-model consensus

About this skill

Claudemem Multi-Agent Orchestration

Version: 1.1.0 Purpose: Coordinate multiple agents using shared claudemem output

Overview

When multiple agents need to investigate the same codebase:

  1. Run claudemem ONCE to get structural overview
  2. Write output to shared file in session directory
  3. Launch agents in parallel - all read the same file
  4. Consolidate results with consensus analysis

This pattern avoids redundant claudemem calls and enables consensus-based prioritization.

For parallel execution patterns, see: orchestration:multi-model-validation skill

Claudemem-Specific Patterns

This skill focuses on claudemem-specific orchestration. For general parallel execution:

  • 4-Message Pattern - See orchestration:multi-model-validation Pattern 1
  • Session Setup - See orchestration:multi-model-validation Pattern 0
  • Statistics Collection - See orchestration:multi-model-validation Pattern 7

Pattern 1: Shared Claudemem Output

Purpose: Run expensive claudemem commands ONCE, share results across agents.

# Create unique session directory (per orchestration:multi-model-validation Pattern 0)
SESSION_ID="analysis-$(date +%Y%m%d-%H%M%S)-$(head -c 4 /dev/urandom | xxd -p)"
SESSION_DIR="/tmp/${SESSION_ID}"
mkdir -p "$SESSION_DIR"

# Run claudemem ONCE, write to shared files
claudemem --agent map "feature area" > "$SESSION_DIR/structure-map.md"
claudemem --agent test-gaps > "$SESSION_DIR/test-gaps.md" 2>&1 || echo "No gaps found" > "$SESSION_DIR/test-gaps.md"
claudemem --agent dead-code > "$SESSION_DIR/dead-code.md" 2>&1 || echo "No dead code" > "$SESSION_DIR/dead-code.md"

# Export session info
echo "$SESSION_ID" > "$SESSION_DIR/session-id.txt"

Why shared output matters:

  • Claudemem indexing is expensive (full AST parse)
  • Same index serves all queries in session
  • Parallel agents reading same file = no redundant computation

Pattern 2: Role-Based Agent Distribution

After running claudemem, distribute to role-specific agents:

# Parallel Execution (ONLY Task calls - per 4-Message Pattern)
Task: architect-detective
  Prompt: "Analyze architecture from $SESSION_DIR/structure-map.md.
           Focus on layer boundaries and design patterns.
           Write findings to $SESSION_DIR/architect-analysis.md"
---
Task: tester-detective
  Prompt: "Analyze test gaps from $SESSION_DIR/test-gaps.md.
           Prioritize coverage recommendations.
           Write findings to $SESSION_DIR/tester-analysis.md"
---
Task: developer-detective
  Prompt: "Analyze dead code from $SESSION_DIR/dead-code.md.
           Identify cleanup opportunities.
           Write findings to $SESSION_DIR/developer-analysis.md"

All 3 execute simultaneously (3x speedup!)

Pattern 3: Consolidation with Ultrathink

Task: ultrathink-detective
  Prompt: "Consolidate analyses from:
           - $SESSION_DIR/architect-analysis.md
           - $SESSION_DIR/tester-analysis.md
           - $SESSION_DIR/developer-analysis.md

           Create unified report with prioritized action items.
           Write to $SESSION_DIR/consolidated-analysis.md"

Pattern 4: Consolidated Feedback Reporting (v0.8.0+)

When multiple agents perform searches, consolidate feedback for efficiency.

Why Consolidate?

  • Avoid duplicate feedback submissions
  • Single point of failure handling
  • Cleaner session cleanup

Shared Feedback Collection:

Each agent writes feedback to a shared file in the session directory:

# Agent writes feedback entry (atomic with flock)
report_agent_feedback() {
  local query="$1"
  local helpful="$2"
  local unhelpful="$3"

  # Use file locking to prevent race conditions
  (
    flock -x 200
    printf '%s|%s|%s\n' "$query" "$helpful" "$unhelpful" >> "$SESSION_DIR/feedback.log"
  ) 200>"$SESSION_DIR/feedback.lock"
}

# Usage in agent
report_agent_feedback "$SEARCH_QUERY" "$HELPFUL_IDS" "$UNHELPFUL_IDS"

Orchestrator Consolidation:

After all agents complete, the orchestrator submits all feedback:

consolidate_feedback() {
  local session_dir="$1"
  local feedback_log="$session_dir/feedback.log"

  # Skip if no feedback collected
  [ -f "$feedback_log" ] || return 0

  # Check if feedback command available (v0.8.0+)
  if ! claudemem feedback --help 2>&1 | grep -qi "feedback"; then
    echo "Note: Search feedback requires claudemem v0.8.0+"
    return 0
  fi

  local success=0
  local failed=0

  while IFS='|' read -r query helpful unhelpful; do
    # Skip empty lines
    [ -n "$query" ] || continue

    if timeout 5 claudemem feedback \
      --query "$query" \
      --helpful "$helpful" \
      --unhelpful "$unhelpful" 2>/dev/null; then
      ((success++))
    else
      ((failed++))
    fi
  done < "$feedback_log"

  echo "Feedback: $success submitted, $failed failed"

  # Cleanup
  rm -f "$feedback_log" "$session_dir/feedback.lock"
}

# Call after consolidation
consolidate_feedback "$SESSION_DIR"

Multi-Agent Workflow Integration:

Phase 1: Session Setup
  └── Create SESSION_DIR with feedback.log

Phase 2: Parallel Agent Execution
  └── Agent 1: Search → Track → Write feedback entry
  └── Agent 2: Search → Track → Write feedback entry
  └── Agent 3: Search → Track → Write feedback entry

Phase 3: Results Consolidation
  └── Consolidate agent outputs

Phase 4: Feedback Consolidation (NEW)
  └── Read all feedback entries from log
  └── Submit each to claudemem
  └── Report success/failure counts

Phase 5: Cleanup
  └── Remove SESSION_DIR (includes feedback files)

Best Practices Update:

Do:

  • Use file locking for concurrent writes (flock -x)
  • Consolidate feedback AFTER agent completion
  • Report success/failure counts
  • Clean up feedback files after submission

Don't:

  • Submit feedback from each agent individually
  • Skip the version check
  • Block on feedback submission failures
  • Track feedback for non-search commands (map, symbol, callers, etc.)

Role-Based Command Mapping

Agent RolePrimary CommandsSecondary CommandsFocus
Architectmap, dead-codecontextStructure, cleanup
Developercallers, callees, impactsymbolModification scope
Testertest-gapscallersCoverage priorities
Debuggercontext, impactsymbol, callersError tracing
UltrathinkALLALLComprehensive

Sequential Investigation Flow

For complex bugs or features requiring ordered investigation:

Phase 1: Architecture Understanding
  claudemem --agent map "problem area"  Identify high-PageRank symbols (> 0.05)

Phase 2: Symbol Deep Dive
  For each high-PageRank symbol:
    claudemem --agent context <symbol>    Document dependencies and callers

Phase 3: Impact Assessment (v0.4.0+)
  claudemem --agent impact <primary-symbol>  Document full blast radius

Phase 4: Gap Analysis (v0.4.0+)
  claudemem --agent test-gaps --min-pagerank 0.01  Identify coverage holes in affected code

Phase 5: Action Planning
  Prioritize by: PageRank * impact_depth * test_coverage

Agent System Prompt Integration

When an agent needs deep code analysis, it should reference the claudemem skill:

---
skills: code-analysis:claudemem-search, code-analysis:claudemem-orchestration
---

The agent then follows this pattern:

  1. Check claudemem status: claudemem status
  2. Index if needed: claudemem index
  3. Run appropriate command based on role
  4. Write results to session file for sharing
  5. Return brief summary to orchestrator

Best Practices

Do:

  • Run claudemem ONCE per investigation type
  • Write all output to session directory
  • Use parallel execution for independent analyses (see orchestration:multi-model-validation)
  • Consolidate with ultrathink for cross-perspective insights
  • Handle empty results gracefully

Don't:

  • Run same claudemem command multiple times
  • Let each agent run its own claudemem (wasteful)
  • Skip the consolidation step
  • Forget to clean up session directory (automatic TTL cleanup via session-start.sh)

Session Lifecycle Management

Automatic TTL Cleanup:

The session-start.sh hook automatically cleans up expired session directories:

  • Default TTL: 24 hours
  • Runs at session start
  • Cleans /tmp/analysis-*, /tmp/review-* directories older than TTL
  • See plugins/code-analysis/hooks/session-start.sh for implementation

Manual Cleanup:

# Clean up specific session
rm -rf "$SESSION_DIR"

# Clean all old sessions (24+ hours)
find /tmp -maxdepth 1 -name "analysis-*" -o -name "review-*" -mtime +1 -exec rm -rf {} \;

Error Handling Templates

For robust orchestration, handle common claudemem errors. See claudemem-search skill for complete error handling templates:

Empty Results

RESULT=$(claudemem --agent map "query" 2>/dev/null)
if [ -z "$RESULT" ] || echo "$RESULT" | grep -q "No results found"; then
  echo "No results - try broader keywords or check index status"
fi

Version Compatibility

# Check if command is available (v0.4.0+ commands)
if claudemem --agent dead-code 2>&1 | grep -q "unknown command"; then
  echo "dead-code requires claudemem v0.4.0+"
  echo "Fallback: Use map command instead"
fi

Index Status

# Verify index before running commands
if ! claudemem status 2>&1 | grep -qE "[0-9]+ (chunks|symbols)"; then
  echo "Index not found - run: claudemem index"
  exit 1
fi

Reference: For complete error handling patterns, see templates in code-analysis:claudemem-search skill (Templates 1-5)


Maintained by: MadAppGang Plugin: code-analysis v2.8.0 Last Updated: December 2025 (v1.1.0 - Search feedback protocol support)

When not to use it

  • When each agent needs to run its own claudemem command
  • When the same claudemem command is run multiple times
  • When the consolidation step is skipped

Limitations

  • Search feedback requires claudemem v0.8.0+
  • dead-code command requires claudemem v0.4.0+

How it compares

This workflow avoids redundant claudemem calls by sharing a single output file, unlike a manual approach where each agent might re-run the expensive claudemem indexing.

Compared to similar skills

claudemem-orchestration side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
claudemem-orchestration (this skill)16moReviewIntermediate
claude-automation-recommender472moReviewBeginner
subagent-driven-development147moNo flagsAdvanced
coding-agent162moReviewAdvanced

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