Individual skill
live-ai-pipelines
Build or refactor a long-running AI workflow that needs visible progress, durable partial results, resume after interruption, or atomic publication. Use for multi-stage extraction, indexing, batch analysis, evaluation, or report workflows. Do not use for a short synchronous model call or a UI-only streaming feature.
live-ai-pipelines/SKILL.md
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- Last checked
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- Repository last updated
- Nov 3, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- engineering Derived category
- design Derived category
- operations Derived category
- design-interface Derived category
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SKILL.mdcore · 6,191 bytesSKILL.mdcore · 6,191 bytesSKILL.mdcore · 6,191 bytesagents/openai.yamlasset · 264 bytesreferences/architecture.mddoc · 2,936 bytesreferences/event-schema.mddoc · 2,755 bytesreferences/skill-routing.mddoc · 9,170 bytesscripts/event_journal.pyruns · 3,971 bytesscripts/incremental_json.pyruns · 5,830 bytesscripts/publish_snapshot.pyruns · 2,887 bytesscripts/serve_progress.pyruns · 6,056 bytes
Read excerpt
# Live AI Pipelines
Use this skill when people need to inspect useful work while later work is still running, or reopen a run after interruption. It owns logical stages, artifacts, progress, recovery, and publication—not a particular queue, database, or provider SDK.
Pair with `ai-engineering` when model request/retry/rate-limit behavior is itself the problem. Pair with a provider/runtime skill only for that platform’s current API behavior.
## Useful defaults
- Keep canonical complete artifacts separate from provisional previews.
- Give artifacts and independently resumable items stable IDs.
- Make the UI a projection of stored results and events, not the source of workflow truth.
- Publish a complete new snapshot rather than exposing a half-rendered site.
- Let core output publish with optional enrichment visibly pending unless the user makes that enrichment a completion requirement.
