Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Auto-learns when to wake. Balances responsiveness with efficiency, grows autonomy over time.
Auto-learns when to wake. Balances responsiveness with efficiency, grows autonomy over time.
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
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. Summarize what changed and any follow-up checks I should run.
Build reliable heartbeat playbooks for OpenClaw agents without noisy checks, missed signals, or runaway costs.
On first use, follow setup.md to capture timezone, active hours, precision needs, and risk tolerance.
User wants a better heartbeat file in OpenClaw. Agent audits current heartbeat behavior, designs a safer file, and tunes intervals using real workflow constraints. Use this for adaptive monitoring, proactive check-ins, and hybrid heartbeat plus cron strategies.
Memory lives in ~/heartbeat/. See memory-template.md for the structure and fields. ~/heartbeat/ βββ memory.md # Preferences, cadence profile, and last tuning decisions βββ drafts/ # Candidate heartbeat variants βββ snapshots/ # Previous heartbeat versions for rollback
TopicFileSetup interviewsetup.mdMemory schemamemory-template.mdProduction heartbeat templateheartbeat-template.mdPractical heartbeat use casesuse-cases.mdInterval strategy referenceintervals.mdTrigger strategy referencetriggers.mdValidation checklist before shippingqa-checklist.mdInternet research sourcessources.md
Define one mission sentence and 1-3 monitored signals first. If scope is broad, split into explicit sections (critical, important, nice-to-have) and only automate the first two.
If nothing actionable is found, heartbeat must return exactly HEARTBEAT_OK. Do not emit summaries on empty cycles. This prevents noisy loops and keeps heartbeat cheap.
Start from OpenClaw defaults and adapt: use a moderate baseline interval, then tighten only during active windows. Always encode timezone and active hours in the heartbeat file to avoid waking during sleep hours.
If a task must run at exact wall-clock times, move it to cron. If a task should react to changing context or event probability, keep it in heartbeat.
Use a two-stage pattern: cheap precheck first, expensive action only on threshold hit. Never call paid APIs on every heartbeat cycle unless the user explicitly accepts the cost.
Each alert condition must have trigger threshold, escalation route, and cooldown period. No escalation path means no alert. No cooldown means likely alert spam.
Before finalizing, run at least one dry simulation against the checklist in qa-checklist.md. Keep a snapshot of the previous heartbeat so the user can rollback in one step.
Polling everything every cycle -> high token/API burn with low signal quality. Using heartbeat for exact 09:00 jobs -> drift and missed exact-time expectations. Missing timezone in heartbeat config -> notifications at the wrong local time. No active-hours filter -> overnight wakeups and user fatigue. No HEARTBEAT_OK fallback -> verbose no-op loops. No cooldown on alerts -> duplicate escalations during noisy incidents.
Data that stays local: Heartbeat preferences and tuning notes in ~/heartbeat/ Draft and snapshot files for heartbeat definitions This skill does NOT: Require credentials by default Trigger external APIs without user-approved instructions Edit unrelated files outside the heartbeat workflow
Install with clawhub install <slug> if user confirms: schedule - Scheduling patterns for recurring workflows monitoring - Monitoring strategies and alert design alerts - Alert routing and escalation hygiene workflow - Multi-step workflow orchestration copilot - Proactive assistant patterns with controlled autonomy
If useful: clawhub star heartbeat Stay updated: clawhub sync
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Largest current source with strong distribution and engagement signals.