// Build and review cron jobs, schedulers, retries, smoke tests, and operational harnesses for Spark Intelligence in a lightweight and highly reliable way. Use when designing job systems, background work, health checks, retry logic, cron behavior, migration jobs, or smoke-test coverage for scheduled work.
| name | reliable-job-harnesses |
| description | Build and review cron jobs, schedulers, retries, smoke tests, and operational harnesses for Spark Intelligence in a lightweight and highly reliable way. Use when designing job systems, background work, health checks, retry logic, cron behavior, migration jobs, or smoke-test coverage for scheduled work. |
Use this skill when work touches:
docs/CRON_JOB_HARNESS_SPEC_V1.mddocs/ARCHITECTURE_SPARK_INTELLIGENCE_V1.mddocs/CODING_RULESET_V1.mddocs/SPARK_INTELLIGENCE_PROMPT_BIBLE.mdThen read:
references/workflow.mdThe correct shape is:
Reject:
If the task is deeper than a quick pass, use the output template and anti-pattern checklist in references/workflow.md.
Return these explicitly:
When reviewing harness code, optimize for:
Findings should prioritize:
Give a concrete answer, not generic principles. The result should usually end in a small harness plan, review finding set, or smoke-test checklist.
Audit local-agent systems, specs, and code for security weaknesses and hardening priorities. Use when reviewing Spark Intelligence features, security-sensitive diffs, identity or pairing flows, host-execution boundaries, webhook adapters, auth and secret handling, or when producing a security grade and remediation list that others can reuse later.
Design secure, lightweight local-agent systems for Spark Intelligence and adjacent agent products. Use when defining identity and pairing rules, provider or channel auth, dangerous command approvals, tool and host boundaries, webhook trust checks, local secret handling, or security guardrails for new features.
Analyze OpenClaw, Claude Cowork, Hermes Agent, and adjacent agent products to learn what to borrow, what to directly copy as patterns, and what Spark Intelligence should keep uniquely its own. Use when comparing architectures, onboarding, adapters, reliability harnesses, migration paths, or competitive product shape.
Make Spark Intelligence product decisions with deep awareness of Spark Researcher, Spark Swarm, domain chips, specialization paths, autoloop flywheels, and the wider Spark ecosystem. Use when defining product scope, modular architecture, ecosystem integration, persistent agent behavior, or self-evolving collective-intelligence systems.
Keep Spark Intelligence code, architecture, security, and documentation highly maintainable, scalable enough, and clean without adding unnecessary complexity. Use when reviewing code, planning architecture, writing docs, simplifying modules, improving quality, or enforcing long-term engineering standards.