Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
Cord Trees
Dynamic task tree orchestration inspired by Cord protocol. Agent builds its own coordination tree at runtime — deciding decomposition, parallelism, and depen...
skill openclawclawhub Free
Dynamic task tree orchestration inspired by Cord protocol. Agent builds its own coordination tree at runtime — deciding decomposition, parallelism, and depen...
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Package facts
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- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, references/state-helpers.md
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Trust & source
Release facts
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- Tencent SkillHub
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- Version
- 1.0.1
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- MoltonBot000
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Documentation
Cord Trees — Dynamic Task Tree Orchestration
Build coordination trees at runtime. You decide the decomposition, not the developer. Inspired by Cord by June Kim.
Core Concept
Instead of following a pre-defined workflow, you analyze the goal and build your own task tree: Goal: "Evaluate whether to migrate from REST to GraphQL" You decide: ├── #1 spawn: Audit current REST API surface ├── #2 spawn: Research GraphQL trade-offs ├── #3 ask: How many concurrent users? (blocked-by: #1) ├── #4 fork: Comparative analysis (blocked-by: #2, #3) └── #5 fork: Write recommendation (blocked-by: #4) The tree emerges from your analysis, not from hardcoded logic.
1. SPAWN — Isolated Context
Child gets only its task prompt. Clean slate. spawn( goal="Research GraphQL adoption patterns", prompt="Search for case studies of REST→GraphQL migrations...", blocked_by=[] # Can start immediately ) Use when: Task is self-contained, doesn't need sibling context.
2. FORK — Inherited Context
Child receives all completed sibling results injected into prompt. fork( goal="Synthesize findings into recommendation", prompt="Based on the research, write a recommendation...", blocked_by=["research-rest", "research-graphql", "user-scale"] ) Use when: Synthesis, analysis, or integration requiring prior work.
3. ASK — Human Elicitation
Pause for human input. Creates a checkpoint. ask( question="How many concurrent users do you serve?", options=["<1K", "1K-10K", "10K-100K", ">100K"], blocked_by=["audit-api"] # Ask after audit provides context ) Use when: Decision requires human knowledge or approval.
4. SERIAL — Ordered Sequence
Children execute in order. Implicit dependencies. serial([ {"goal": "Draft report", "type": "spawn"}, {"goal": "Review draft", "type": "ask"}, {"goal": "Finalize report", "type": "fork"} ]) Use when: Strict ordering required.
5. GOAL — Root Node
The top-level objective. You decompose it into children.
Implementation with OpenClaw
Map Cord primitives to OpenClaw tools: Cord PrimitiveOpenClaw Implementationspawnsessions_spawn(task=prompt, label=id)forksessions_spawn with sibling results in taskaskMessage human, wait for responseserialSpawn sequentially, wait between eachread_treeRead state file + subagents listcompleteWrite result to state file
State File
Track the tree in cord-state.json: { "goal": "Evaluate REST to GraphQL migration", "nodes": { "#1": { "type": "spawn", "goal": "Audit REST API", "status": "complete", "result": "47 endpoints, 12 nested...", "blockedBy": [], "sessionKey": "abc123" }, "#2": { "type": "spawn", "goal": "Research GraphQL", "status": "running", "blockedBy": [], "sessionKey": "def456" }, "#3": { "type": "ask", "goal": "Get user scale", "status": "waiting", "question": "How many concurrent users?", "options": ["<1K", "1K-10K", "10K-100K", ">100K"], "blockedBy": ["#1"] }, "#4": { "type": "fork", "goal": "Comparative analysis", "status": "blocked", "blockedBy": ["#2", "#3"] } }, "nextId": 5 }
Phase 1: Analyze Goal
Read the goal. Think about: What are the major components? What can run in parallel? What has dependencies? Where do I need human input? What needs synthesis (fork) vs isolation (spawn)?
Phase 2: Build Initial Tree
Create nodes for the first level of decomposition: # Initialize state state = { "goal": user_goal, "nodes": {}, "nextId": 1 } # Add initial nodes add_node(state, type="spawn", goal="Research A", blockedBy=[]) add_node(state, type="spawn", goal="Research B", blockedBy=[]) add_node(state, type="fork", goal="Synthesize", blockedBy=["#1", "#2"]) write("cord-state.json", state)
Phase 3: Execute Ready Nodes
Find nodes that are ready (all blockedBy complete): def get_ready_nodes(state): ready = [] for id, node in state["nodes"].items(): if node["status"] != "blocked": continue deps = node["blockedBy"] if all(state["nodes"][d]["status"] == "complete" for d in deps): ready.append(id) return ready For each ready node: If spawn: sessions_spawn( task=node["prompt"], label=node_id, runTimeoutSeconds=600 ) node["status"] = "running" If fork: # Inject sibling results sibling_context = collect_sibling_results(state, node) full_prompt = f"{node['prompt']}\n\nContext from prior work:\n{sibling_context}" sessions_spawn(task=full_prompt, label=node_id) node["status"] = "running" If ask: # Message human message(action="send", message=f"Question: {node['question']}\nOptions: {node['options']}") node["status"] = "waiting" # Wait for response, then mark complete with answer
Phase 4: Monitor & Update
Poll running agents, update state on completion: while has_running_or_blocked(state): # Check agent status agents = subagents(action="list") for agent in agents: node = find_node_by_session(state, agent["sessionKey"]) if agent["status"] == "complete": # Get result from session history result = get_agent_result(agent) node["status"] = "complete" node["result"] = result # Dispatch newly ready nodes for node_id in get_ready_nodes(state): dispatch_node(state, node_id) save_state(state) wait(30) # Don't poll too aggressively
Phase 5: Synthesize
When all nodes complete, the final fork node produces the result.
Dynamic Restructuring
Agents can modify their own subtree at runtime: # Child agent realizes it needs more research add_child_node( parent="#2", type="spawn", goal="Deep dive on performance implications", blockedBy=[] ) This is what makes Cord-style orchestration powerful — the tree evolves based on what agents discover.
Spawn vs Fork Decision Guide
SituationUseIndependent research taskspawnTask that doesn't need sibling contextspawnCheap to restart if it failsspawnSynthesis or analysis across prior workforkFinal integration stepforkTask that builds on discoveriesfork Default to spawn. Use fork only when context inheritance is required.
Approval Gate
#1 spawn: Draft proposal #2 ask: "Approve this proposal?" (blocked-by: #1) #3 fork: Implement approved proposal (blocked-by: #2)
Clarification
#1 spawn: Initial analysis #2 ask: "Which direction should we focus?" (blocked-by: #1) #3 spawn: Deep dive on chosen direction (blocked-by: #2)
Periodic Checkpoints
#1 spawn: Phase 1 #2 ask: "Continue to phase 2?" (blocked-by: #1) #3 spawn: Phase 2 (blocked-by: #2) #4 ask: "Continue to phase 3?" (blocked-by: #3) ...
Example: Full Decomposition
Goal: "Create a comprehensive competitor analysis report" #1 [spawn] List top 5 competitors └── No dependencies, starts immediately #2 [spawn] Research Competitor A (blocked-by: #1) #3 [spawn] Research Competitor B (blocked-by: #1) #4 [spawn] Research Competitor C (blocked-by: #1) #5 [spawn] Research Competitor D (blocked-by: #1) #6 [spawn] Research Competitor E (blocked-by: #1) └── All parallel, isolated research #7 [fork] Identify patterns across competitors (blocked-by: #2-#6) └── Needs all research results #8 [ask] "Focus on pricing, features, or positioning?" (blocked-by: #7) └── Human steers direction #9 [fork] Deep analysis on chosen focus (blocked-by: #8) └── Builds on patterns + human input #10 [fork] Write final report (blocked-by: #9) └── Synthesis of everything
Error Handling
if node["status"] == "failed": # Options: # 1. Retry (reset to blocked) node["status"] = "blocked" node["retries"] = node.get("retries", 0) + 1 # 2. Skip (mark complete with error) node["status"] = "complete" node["result"] = f"FAILED: {error}" # 3. Escalate (ask human) add_node(state, type="ask", question=f"Node {id} failed. Retry, skip, or abort?", blockedBy=[])
Attribution
This skill implements patterns from the Cord protocol by June Kim, adapted for OpenClaw's sessions_spawn and subagents primitives.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs
- SKILL.md
- references/state-helpers.md