canopy-loop-design

Designs reusable Canopy loop graphs with agent, check, and gate nodes via MCP tools.

Updated Jun 12, 2026
One-click install
npx skills add https://github.com/UniverLab/univerlab --skill canopy-loop-design-univerlab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: canopy-loop-design
Source: https://github.com/UniverLab/univerlab/tree/main/public/.well-known/agent-skills/canopy-loop-design
Command: npx skills add https://github.com/UniverLab/univerlab --skill canopy-loop-design-univerlab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Turning a recurring multi-step process into a background agent workflow is error-prone: graphs fail on ambiguous edges, missing entry nodes, wrong timeouts, and quota deaths mid-run. This Skill guides an agent to translate a user goal into ordered specs and a validated, persisted Canopy loop graph that survives real execution failures. ## Core Features & Use Cases - Spec authoring contract: Structures every spec as ROLE / WHAT / HOW so a colder, cheaper model can execute it without the author's context. - Reusable graph patterns: Provides eight field-tested patterns (feature delivery, bugfix, gated implement, resilience branch, cross-platform check) with routing rules learned from real broken runs. - Pre-run validation and recovery: Ships a nine-rule fatal-shape checklist and a recovery matrix covering zombie runs, failed loops, and quota-based autorun scheduling. - Use Case: A user asks to orchestrate a developer/reviewer/verifier pipeline; the Skill splits the work into specs, picks the gated-implement pattern, persists the loop with loop_create/loop_add_node/loop_add_edge, validates it, and summarizes before running. ## Quick Start Ask the agent to plan a Canopy loop that implements a feature with build-and-test verification, review, and automatic retry on failure.

Frequently Asked Questions about canopy-loop-design

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create a Canopy loop with agent and check nodes?▼

Call loop_create first, then loop_add_spec for each ordered spec, loop_add_node for each agent/check/gate node, and loop_add_edge for routing. Verify the final shape with loop_get and only call loop_run after explicit user approval.

How do I write specs that background agents can execute reliably?▼

Structure each spec as ROLE, WHAT, and HOW: the implementer's identity, the acceptance criteria, and the exact route with embedded commands and constraints. A spec that depends on the author's chat context will diverge when a colder, cheaper model executes it.

Why does my Canopy loop fail with ambiguous outgoing edges?▼

Two edges leaving the same node that match the same result but target different nodes abort the loop after the node's work is done. Dedupe edges by (from_node, condition) before inserting; a pass edge and a fail edge from the same node are fine.

Can a Canopy loop recover after a daemon restart or quota exhaustion?▼

Yes. A restart leaves a zombie running loop, recovered with loop_pause then loop_continue(retry_current_node). For quota deaths, a resilience node parses the reset time and calls loop_schedule_autorun to resume once at that exact moment.

When should I use a gate node instead of a check node in a loop?▼

Use a check node for deterministic verification like builds and tests with explicit timeout_seconds. Use a gate node when routing depends on semantics, and always gate on a strict token like APPROVED, never on a word that could appear in narration.