Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
Agent Step Sequencer
Multi-step scheduler for in-depth agent requests. Detects when user needs multiple steps, suggests plan and waits for confirmation, persists state, and runs heartbeat-aware flow. Use when requests have 3+ actions, sequential dependencies, output dependencies, or high scope/risk.
skill openclawclawhub Free
Multi-step scheduler for in-depth agent requests. Detects when user needs multiple steps, suggests plan and waits for confirmation, persists state, and runs heartbeat-aware flow. Use when requests have 3+ actions, sequential dependencies, output dependencies, or high scope/risk.
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- CHANGELOG.md, README.md, SKILL.md, references/state-schema.md, scripts/README.md, scripts/step-sequencer-check.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.1.0
Provenance
- Publisher
- gostlightai
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Agent Step Sequencer
Multi-step scheduler for in-depth requests. Enables step-based actions with heartbeat integration—survives gateway reset mid-step.
Core Pattern
Interpret when user request requires multiple steps Suggest step plan, wait for confirmation Persist state.json (with plan format) Agent invokes scripts/step-sequencer-check.py immediately (no wait for heartbeat) Heartbeat (e.g. every 5 min) also invokes the script—keeps sequencer aligned with email jobs and other heartbeat tasks Critical: If gateway resets mid-step, next heartbeat reads state and resumes correctly.
Plan Format
Agent builds a plan when user approves. During approval, agent asks: Use 2-minute delay between steps? Recommended for rate-limit–sensitive API calls. User chooses; agent sets stepDelayMinutes (0 or 2) in state. Each step has title, instruction, and optionally requiredOutputs (paths relative to workspace that must exist before the step is marked DONE): { "plan": { "steps": { "step-1": { "title": "Research topic X", "instruction": "Research topic X and produce a concise summary", "requiredOutputs": ["study/summary.md"] }, "step-2": { "title": "Write paper", "instruction": "Using the summary from step 1, write a research paper..." } } }, "stepQueue": ["step-1", "step-2"], "currentStep": 0, "stepRuns": {}, "stepDelayMinutes": 0, "status": "IN_PROGRESS" } title: Human-readable label instruction: Full instruction for the agent (research, summarize, pull X from Y, etc.) requiredOutputs (optional): List of paths (relative to workspace). Runner marks step DONE only if agent exits 0 and all these paths exist; otherwise step is FAILED with "Missing required outputs: …".
Roles
Agent: Builds plan, persists state; does not touch state during step execution. Takes prompts. Runner (step-sequencer-runner.py): Invokes agent with step instruction, waits for exit, marks DONE/FAILED. Applies stepDelayMinutes. On retry, agent gets troubleshoot prompt. Check script (step-sequencer-check.py): If work to do, invokes runner. Handles FAILED → retry (reset PENDING, invoke runner). Heartbeat: Invokes check script on schedule.
Step execution: autonomous recovery
Do not stop mid-step to ask the user. When executing a step, if something fails (empty fetch, API error, source unavailable): Retry once (same source/URL) if it might be transient. Try an alternative (e.g. CoinGecko instead of CoinMarketCap, different endpoint or token) and complete the step with what you can. Document and exit only if you truly cannot complete the step—then exit non-zero so the runner marks FAILED; the scheduler will retry with a troubleshoot prompt. Do not stop silently. If you cannot complete the step after retry and alternatives: actively prompt the user—post a short message that you hit a snag, what failed, and what you tried (e.g. "Step 2 (research Meteora) failed: CoinMarketCap fetch empty, tried CoinGecko—also empty. Need another source or skip this token."). Then exit non-zero so the runner marks FAILED and the scheduler can retry or add to blockers. Never just stop without telling the user.
How Agent Determines Multi-Step
Agent must suggest before proceeding. When MULTI_STEP, propose the step plan and wait for confirmation before executing. MULTI_STEP = (action_count >= 3) OR has_sequential_language OR has_output_dependency OR high_scope_or_risk OR user_requests_steps OR contains_setup_keywords SINGLE_STEP = (action_count == 1) AND NOT has_output_dependency AND immediate_execution DECISION = IF MULTI_STEP THEN suggest_multi_step → wait for confirm → proceed ELSE single_step Definitions: CriterionMeaningaction_countNumber of distinct actions (file edits, commands, etc.)has_sequential_language"then", "after", "first...then", "step 1"has_output_dependencyStep B needs output from step Ahigh_scope_or_riskMany files, destructive ops, migrationuser_requests_steps"step by step", "break this down", "one at a time"contains_setup_keywords"set up", "migrate", "implement from scratch", "full X", "complete Y"
State Schema
See references/state-schema.md. Key fields: plan.steps: step definitions (title, instruction, optional requiredOutputs) stepQueue, currentStep, stepRuns stepDelayMinutes: 0 = no delay; 2 = 2 min between steps blockers, lastHeartbeatIso, artifacts
Heartbeat Flow
Heartbeat invokes scripts/step-sequencer-check.py. Agent also invokes it right after persisting state. Read state.json If no state or status=DONE → do nothing If step FAILED → bump tries, reset to PENDING, invoke runner (immediate retry) If step DONE → advance currentStep, invoke runner If step PENDING or IN_PROGRESS → invoke runner Update lastHeartbeatIso Runner invokes agent (configurable via STEP_AGENT_CMD). Runner applies stepDelayMinutes.
Failure Flow
Runner marks step FAILED, stores error in stepRuns Runner invokes check script immediately (no heartbeat wait) Check script bumps tries, resets status to PENDING, invokes runner Runner invokes agent with troubleshoot prompt: "Step X failed (tries: N). Previous run ended with: [error]. Please troubleshoot and retry: [instruction]" Repeats until DONE or max retries / blockers
Check script → Runner
flowchart TD A[Heartbeat or Agent] --> B[step-sequencer-check.py] B --> C{Work to do?} C -->|No| D[Do nothing] C -->|Yes| E[Invoke runner] E --> F[step-sequencer-runner.py] F --> G[Invoke agent with instruction] G --> H{Agent exit} H -->|Success| I[Mark DONE] H -->|Fail| J[Mark FAILED, invoke check script] I --> K[Check advances or done] J --> B
User flow (propose + persist)
flowchart TD U[User Request] --> V{Complex enough?} V -->|No| W[Execute directly] V -->|Yes| X[Propose step plan] X --> Y[User confirms] Y --> Z[Persist state.json with plan] Z --> AA[Agent invokes step-sequencer-check] AA --> AB[Runner invokes agent - step 1] AB --> AC[Heartbeat also invokes on schedule]
Configuration
EnvDescriptionSTEP_AGENT_CMDRequired. Command to invoke agent (space-separated). Prompt appended as last arg. Example: openclaw agent --messageSTEP_RUNNERPath to step-sequencer-runner.py (optional)STEP_MAX_RETRIESMax retries on FAILED before adding to blockers. Default: 3 OpenClaw: Wire STEP_AGENT_CMD to OpenClaw's agent invocation (e.g. openclaw agent --message). Security: Set STEP_AGENT_CMD only to your trusted agent binary. Do not use shell interpreters (bash, sh, etc.) or -c/-e—the runner rejects these to prevent command injection. The instruction from state.json is passed as a single argument; it is never executed by a shell.
Final Deliverables Step
When all steps complete: Confirm all requirements of the steps are met Produce summary with links or paths to any files created/written Mark state DONE → on subsequent heartbeats, scheduler does nothing
Installation
clawhub install agent-step-sequencer Manual copy: cp -r agent-step-sequencer ~/.openclaw/skills/agent-step-sequencer Heartbeat integration — Add this to your heartbeat (or have the agent add it): # Agent Step Sequencer check (add to heartbeat cycle) python3 ~/.openclaw/skills/agent-step-sequencer/scripts/step-sequencer-check.py ~/.openclaw/workspace/state.json Or if skill is in workspace: python3 ~/.openclaw/workspace/skills/agent-step-sequencer/scripts/step-sequencer-check.py ~/.openclaw/workspace/state.json Set STEP_AGENT_CMD to your agent invocation before running. Agent should invoke the check script immediately after persisting state.
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
5 Docs1 Scripts
- SKILL.md
- CHANGELOG.md
- README.md
- references/state-schema.md
- scripts/README.md
- scripts/step-sequencer-check.py