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The official Agent Skills spec (agentskills.io/specification) whitelists exactly 6 top-level frontmatter keys (name/description/license/compatibility/metadata/ allowed-tools). A top-level `origin` key fails the official validator (anthropics/skills quick_validate.py ALLOWED_PROPERTIES; skills-ref validate). This moves `origin: X` -> `metadata.origin: X` across the canonical skills/ tree, preserving each value verbatim. Frontmatter-only, minimal diff. - 251 SKILL.md updated (242 new metadata block, 9 appended to existing metadata) - origin values preserved verbatim (verified 251/251) - YAML validated on all changed files - scoped to canonical skills/ only (docs/<lang> translations + tool mirrors .cursor/.kiro/.agents left untouched; presumably regenerated from canonical) Addresses #2233
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name, description, metadata
| name | description | metadata | ||
|---|---|---|---|---|
| continuous-agent-loop | Patterns for continuous autonomous agent loops with quality gates, evals, and recovery controls. |
|
Continuous Agent Loop
This is the v1.8+ canonical loop skill name. It supersedes autonomous-loops while keeping compatibility for one release.
Loop Selection Flow
Start
|
+-- Need strict CI/PR control? -- yes --> continuous-pr
|
+-- Need RFC decomposition? -- yes --> rfc-dag
|
+-- Need exploratory parallel generation? -- yes --> infinite
|
+-- default --> sequential
Combined Pattern
Recommended production stack:
- RFC decomposition (
ralphinho-rfc-pipeline) - quality gates (
plankton-code-quality+/quality-gate) - eval loop (
eval-harness) - session persistence (
nanoclaw-repl)
Failure Modes
- loop churn without measurable progress
- repeated retries with same root cause
- merge queue stalls
- cost drift from unbounded escalation
Recovery
- freeze loop
- run
/harness-audit - reduce scope to failing unit
- replay with explicit acceptance criteria