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workflow-insights

Detects anti-patterns in agent workflows and computes health scores

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仓库
ApiliumCode/mayros
最近来源活动
2026年2月26日 12:53
检测到的 SKILL.md 语言
英语
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13
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1

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SKILL.md
来源说明 · 只读预览
name
workflow-insights
description
Detects anti-patterns in agent workflows and computes health scores
type
semantic
user-invocable
true
semantic
{"skillVersion":1,"permissions":{"graph":["read"],"memory":["recall"]},"queries":[{"predicate":"workflow:analysis","scope":"agent"}]}
# workflow-insights Detect anti-patterns in agent workflows and compute health scores based on 7 weighted pattern detectors. ## When to Use Use when analyzing agent execution traces, debugging sluggish workflows, or auditing multi-step agent pipelines for inefficiency. The skill evaluates delegation chains, error patterns, resource usage, and tool utilization to produce a 0-100 health score. ## Anti-Pattern Detectors | # | Pattern | Weight | Description | | --- | ---------------- | ------ | ----------------------------------------------------------------------------------------- | | 1 | Repeated Failure | 20 | Same error appearing 3+ times suggests retry without fix | | 2 | Long Chain | 15 | Delegation chain exceeding 5 steps suggests over-decomposition | | 3 | Unused Tool | 5 | Tool registered but never called suggests bloated configuration | | 4 | Redundant Query | 10 | Same query pattern appearing multiple times suggests missing caching | | 5 | Timeout Pattern | 20 | Multiple timeouts in sequence suggests resource contention | | 6 | Resource Waste | 15 | Large context sent to simple tasks suggests poor task routing | | 7 | Error Cascade | 15 | Error in one step causing errors in 3+ downstream steps suggests missing error boundaries | ## Health Score - **80-100**: Healthy - **60-79**: Needs attention - **40-59**: Degraded - **0-39**: Critical ## Instructions 1. Recall previous workflow analysis with `skill_memory_context` using predicate `workflow:analysis` 2. The skill scans query results for the 7 anti-pattern indicators listed above 3. Each detected pattern reduces the health score by its weight (starting from 100) 4. Review detected anti-patterns and address the highest-weight issues first 5. Re-run periodically to track workflow health improvements over time
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