Skip to main content

lev-cdo-workflows

[WHAT] Four complexity levels of CDO workflows: Quick, Base, Deep, Epic [HOW] Graph-based agent dispatch with disk-based artifact passing (no groupthink) [WHEN] After classification determines complexity level [WHY] Different problems need different depth - prevents over/under-engineering Triggers: Called by lev-cdo router based on complexity classification

소스 정보

저장소
lev-os/lev-content
최근 소스 활동
2026년 2월 13일 18:54
감지된 SKILL.md 언어
영어
스타
0
포크
0

설치 방법

기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.

소스 파일 검토

설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.

SKILL.md 표시 중

SKILL.md
소스 지침 · 읽기 전용 미리보기
name
lev-cdo-workflows
description
[WHAT] Four complexity levels of CDO workflows: Quick, Base, Deep, Epic [HOW] Graph-based agent dispatch with disk-based artifact passing (no groupthink) [WHEN] After classification determines complexity level [WHY] Different problems need different depth - prevents over/under-engineering Triggers: Called by lev-cdo router based on complexity classification
skill_type
playbook
category
process-thinking
lifecycle_integration
{"stage":"crystallizing","input_artifact":"classification metadata + user query","output_artifact":"FINAL-synthesis.md (workflow output)"}
related_skills
["skill://lev-cdo/router","skill://lev-cdo/skill-discovery","skill://bd"]
plankton
false
# CDO Workflows - Complexity-Based Execution Patterns ## Lev Concept **What is this?** CDO workflows are graph-based execution patterns where agents read/write to disk in sequence. Each complexity level (Quick/Base/Deep/Epic) defines agent count, turn structure, and convergence criteria. **Why does it exist?** Graph structure + disk I/O prevents groupthink. Agents never see each other's work during execution - only the FINAL synthesis step reads all artifacts together. **When to use it:** - **Quick**: Simple questions, 1-2 agents, single perspective - **Base**: Multi-perspective analysis, 2-3 agents, fan-out/merge - **Deep**: Root cause analysis, 3-5 agents, multi-turn chains - **Epic**: Strategic decisions, 5+ agents, BD-tracked multi-session --- ## Core Principle **Graph-based layouts + Agentic execution = CDO** NOT: streaming, parallel, adaptive "properties" (fever dream) YES: YAML describes structure → Agents read/write disk → No groupthink ### The Pattern That Works ``` ./tmp/<workflow>-<timestamp>/ ├── 00-input.md # Original prompt/belief ├── 01-step-one.md # Agent A output (reads: 00) ├── 02-step-two.md # Agent B output (reads: 01) ├── 03a-turn-1.md # Multi-turn: Agent C turn 1 ├── 03b-turn-2.md # Multi-turn: Agent C turn 2 ├── 03c-turn-3.md # Multi-turn: Agent C turn 3 ├── 03-step-three.md # Agent C synthesis └── FINAL-synthesis.md # ONLY this reads ALL files ``` **Critical rules:** 1. Each agent reads from DISK (previous step file) 2. Each agent writes to DISK (its output file) 3. Agents NEVER see each other's work during execution 4. Orchestrator ONLY launches agents—never synthesizes itself 5. Only FINAL step reads all artifacts together --- ## CLI Commands ### Primary Commands ```bash # Execute workflow by complexity (called by router) lev cdo execute --complexity=quick --input="00-input.md" lev cdo execute --complexity=base --input="00-input.md" lev cdo execute --complexity=deep --input="00-input.md" lev cdo execute --complexity=epic --bd-epic=clawd-xxx # Resume interrupted workflow lev cdo resume --workflow-dir=tmp/analysis-20260128-143022 ``` ### Execution Protocol ```bash # 1. Create artifact directory mkdir -p tmp/<workflow>-$(date +%Y%m%d-%H%M%S) # 2. Write input file echo "User's question or belief" > tmp/<workflow>-*/00-input.md # 3. Launch agents per turn (use Task tool) # Each agent reads previous step, writes its step # 4. Final synthesis reads all ``` --- ## Workflows ### Level 1: Quick (1-2 agents, 1 turn) **Use case:** Simple question, single perspective, known answer pattern **Complexity factors:** 0-2 - Single question - Known answer pattern - No dependencies **Pattern:** Sequential execution, direct answer **Example:** Prompt: "Summarize this document" ```yaml workflow: quick-summary agents: - step: 01 skill: summarize input: "00-input.md" output: "FINAL-summary.md" ``` **Execution:** ``` Agent: Read 00-input.md → Write FINAL-summary.md Done. ``` **Convergence:** N/A (single agent, single output) --- ### Level 2: Base (2-3 agents, 2 turns) **Use case:** Need 2+ perspectives, then synthesis **Complexity factors:** 3-5 - Multiple perspectives needed - 2-3 related domains - Some prior art exists **Pattern:** Fan-out/merge (parallel perspectives → synthesis) **Example:** Prompt: "Analyze pros and cons of X" ```yaml workflow: pros-cons-analysis turns: - turn: 1 parallel: true agents: - step: 01a skill: argue-for input: "00-input.md" output: "01a-pros.md" - step: 01b skill: argue-against input: "00-input.md" output: "01b-cons.md" - turn: 2 agents: - step: 02 skill: synthesize input: ["01a-pros.md", "01b-cons.md"] output: "FINAL-analysis.md" ``` **Execution:** ``` Turn 1 (parallel): Agent A: Read 00-input.md → Write 01a-pros.md Agent B: Read 00-input.md → Write 01b-cons.md ⏱ Sync Turn 2: Agent C: Read 01a + 01b → Write FINAL-analysis.md Done. ``` **Convergence:** Synthesis agent reads all Turn 1 outputs, no iteration needed --- ### Level 3: Deep (3-5 agents, 3-5 turns) **Use case:** Root cause analysis, debate, drilling into assumptions **Complexity factors:** 6-9 - Root cause unknown - Multiple hypotheses - Cross-domain analysis - 5+ agents needed **Pattern:** Multi-turn chains with optional resonance loops **Example: Axiom Exploration Pattern** Prompt: "Explore foundational assumptions behind belief X" ```yaml workflow: axiom-exploration skill_ref: workshop/poc/skills/domains/axioms/ turns: - turn: 1 agents: - step: 01 skill: paraphrase-engineer input: "00-input.md" output: "01-paraphrase.md" - turn: 2 agents: - step: 02 skill: steelman-enhance input: "01-paraphrase.md" output: "02-steelman.md" - turn: 3 # MULTI-TURN AGENT agents: - step: 03 skill: dig-axioms input: "02-steelman.md" sub_turns: - "03a-level1.md" - "03b-level2.md" - "03c-level3.md" output: "03-dig-axioms.md" - turn: 4 agents: - step: 04 skill: map-elements input: "03-dig-axioms.md" output: "04-map-elements.md" - turn: 5 # MULTI-TURN AGENT parallel: false agents: - step: 05 skill: multi-devils-debate input: "04-map-elements.md" sub_turns: - "05a-devils-advocate.md" # FOR - "05b-anti-devils.md" # AGAINST - "05c-synthesis.md" # INTEGRATE output: "05-multi-devils.md" - turn: 6 agents: - step: 06 skill: synthesize-apply input: "all" # Reads 00-05 output: "06-synthesize.md" - turn: 7 agents: - step: final skill: reflection-loop input: "all" output: "FINAL-synthesis.md" ``` **Convergence criteria:** - All 7 turns complete - FINAL-synthesis.md exists - Optional: Reflection-loop includes confidence score ≥0.80 --- ### Level 4: Epic (5+ agents, 5-10 turns, BD-tracked) **Use case:** Strategic analysis, multi-session work, unknown unknowns **Complexity factors:** 10+ - Strategic decision - Multi-session work - Unknown unknowns - BD epic required **Pattern:** BD-tracked phases with dependencies **Example: Strategic Bet Analysis** Prompt: "Should we bet on technology X? Full strategic analysis." ```yaml workflow: strategic-bet-analysis bd_epic: true # Track in BD with dependencies phases: - phase: research turns: 2 bd_milestone: "clawd-xxx.1" agents: - skill: "hidden-gems/ergodicity" - skill: "hidden-gems/lindy-effect" - skill: "hidden-gems/cynefin" - skill: "domains/systems-thinking/*" - skill: "axioms/paraphrase-engineer" - phase: analysis turns: 3 bd_milestone: "clawd-xxx.2" depends_on: research agents: - skill: "axioms/dig-axioms" # 3-turn - skill: "axioms/multi-devils-debate" # 3-turn - skill: "hidden-gems/morphological" - skill: "hidden-gems/triz-40" - phase: synthesis turns: 3 bd_milestone: "clawd-xxx.3" depends_on: analysis agents: - resonance_loop: true - convergence_threshold: 0.85 - phase: output turns: 2 bd_milestone: "clawd-xxx.4" depends_on: synthesis agents: - skill: "axioms/synthesize-apply" - skill: "axioms/reflection-loop" ``` **BD Structure:** ``` bd://clawd-xxx (Epic: Strategic Bet Analysis) ├── clawd-xxx.1 (Research phase) ✅ ├── clawd-xxx.2 (Analysis phase) - blocked by clawd-xxx.1 ├── clawd-xxx.3 (Synthesis phase) - blocked by clawd-xxx.2 └── clawd-xxx.4 (Output phase) - blocked by clawd-xxx.3 ``` **Convergence criteria:** - All 4 phases complete (BD milestones closed) - Synthesis phase reaches convergence threshold (0.85) - Output phase produces actionable recommendation --- ## Convergence Criteria (CRITICAL) ### When to Stop a Deep Workflow **Problem:** Without clear exit criteria, Deep workflows can loop infinitely. **Solution:** Define convergence criteria per workflow type. --- ### Criteria Type 1: Turn Count (Fixed Structure) **Use for:** Workflows with defined structure (e.g., axiom exploration) **Rule:** Stop when all defined turns complete **Example:** ```yaml workflow: axiom-exploration turns: 7 # Fixed structure convergence: type: turn_count required_turns: 7 exit_condition: "All turns complete" ``` **Validation:** Check that output files exist for all 7 turns --- ### Criteria Type 2: Confidence Threshold (Iterative) **Use for:** Root cause analysis, hypothesis testing **Rule:** Stop when confidence ≥ threshold OR max iterations reached **Example:** ```yaml workflow: root-cause-analysis convergence: type: confidence threshold: 0.85 max_iterations: 5 evaluator_skill: "evaluate-confidence" ``` **Execution:** ``` Turn 1: Generate hypotheses → confidence = 0.60 Turn 2: Test hypotheses → confidence = 0.75 Turn 3: Refine top hypothesis → confidence = 0.88 ✅ STOP ``` **Safety:** Always include `max_iterations` to prevent infinite loops --- ### Criteria Type 3: Perspective Coverage (Fan-out) **Use for:** Multi-perspective analysis (Base workflows) **Rule:** Stop when all requested perspectives have been collected + synthesis complete **Example:** ```yaml workflow: pros-cons-analysis convergence: type: perspective_coverage required_perspectives: ["pros", "cons", "synthesis"] ``` **Validation:** Check that all perspective files exist (01a-pros.md, 01b-cons.md, 02-synthesis.md) --- ### Criteria Type 4: Resonance Loop Convergence (Epic) **Use for:** Strategic decisions requiring consensus **Rule:** Stop when consecutive iterations show <10% change in output OR max iterations **Example:** ```yaml workflow: strategic-decision convergence: type: resonance max_iterations: 5 convergence_threshold: 0.85 delta_threshold: 0.10 # <10% change = converged loop: - skill: synthesize-current - skill: critique-synthesis - skill: integrate-critique - check: | delta < 0.10 AND confidence >= 0.85? yes: exit_loop no: continue (up to max_iterations) ``` **Anti-pattern:** Orchestrator synthesizing → Groupthink **Pattern:** Separate critique + integrate agents → No groupthink --- ### Convergence Validation Checklist Before declaring a Deep workflow complete, verify: - [ ] **Exit criteria met** (turn count, confidence, perspective coverage, or resonance) - [ ] **All artifact files exist** (no missing turn outputs) - [ ] **FINAL-synthesis.md exists** (final step completed) - [ ] **Max iterations not exceeded** (safety check) - [ ] **Confidence score documented** (if applicable) **If criteria NOT met:** Report blocker and either: 1. Add more turns (if under max_iterations) 2. Lower threshold (if unrealistic) 3. Escalate to human (if truly stuck) --- ## Resonance Loops (When Needed) For convergence on complex questions: ```yaml resonance: enabled: true max_iterations: 5 convergence_threshold: 0.85 check_skill: "evaluate-confidence" loop: - skill: synthesize-current - skill: critique-synthesis - skill: integrate-critique - check: confidence >= threshold? yes: exit_loop no: continue ``` **Anti-pattern:** Orchestrator synthesizing → Groupthink **Pattern:** Separate critique + integrate agents → No groupthink --- ## Agent Dispatch Pattern ``` For each turn: If parallel: Launch all agents simultaneously (Task tool, multiple calls) Wait for all to complete Else: Launch sequentially Sync: Verify outputs exist before next turn ``` --- ## BD Integration (Epic Workflows) ### Before Starting Epic Workflow ```bash # Create BD epic bd create --title="Workflow: <name>" --type=epic --priority=P1 # Example: Strategic bet analysis
GitHub에서 보기
이 SKILL.md는 매우 커서 SkillsMP가 여기에는 첫 섹션만 미리 보여줍니다. GitHub에서 보기