AC memory-orchestrator
记忆编排器是面向 AI Agent 的智能记忆管理系统，针对"分层体系不够清晰、自动摘要质量不稳定、并发写入冲突、缺乏记忆健康度指标"四大高频痛点而设计。它用四层记忆架构（工作/短期/长期/重要）与多模式检索，提供从存储到检索到摘要的全生命周期编排，让 Agent 记忆真正可控可观测。 核心能力：四层记忆架构（工作记忆/短期记忆/长期记忆/重要记忆）、三模式检索（关键词/语义/混合）、自动摘要生成（支持长会话压缩，token 占用减少 70%）、持久化与加载（内存/磁盘）、记忆健康度仪表盘（容量/分布/命中率/陈旧度）、并发写入冲突解决（乐观锁+版本合并）、过期记忆自动清理、模块化扩展接口（可对接向量数据库）。 适用场景：长会话 Agent、聊天机器人上下文管理、RAG 应用记忆层、任务型 Agent 长期记忆、客服助理上下文治理、多 Agent 共享记忆。 差异化：相比仅提供"add/search/summarize"的基础管理器，本技能新增 (1) 四层记忆架构，工作/短期/长期/重要四层清晰分工，每层独立容量与清理策略；(2) 记忆健康度仪表盘，量化容量、分布、命中率、陈旧度四维指标，主动告警；(3) 并发写入冲突解决，乐观锁 + 版本合并，支持多 Agent 安全并发；(4) 摘要质量评估器，量化摘要的信息保留率与可读性，质量不达标自动重试；(5) 模块化扩展接口，语义检索可插拔对接向量数据库（Chroma/LanceDB/Qdrant）。 触发关键词：记忆编排、记忆管理、长期记忆、语义检索、记忆摘要、记忆持久化、memory manager、memory orchestration、semantic search
记忆编排器是面向 AI Agent 的智能记忆管理系统，针对"分层体系不够清晰、自动摘要质量不稳定、并发写入冲突、缺乏记忆健康度指标"四大高频痛点而设计。它用四层记忆架构（工作/短期/长期/重要）与多模式检索，提供从存储到检索到摘要的全生命周期编排，让 Agent 记忆真正可控可观测。…
As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "summary" - note
frontmatter-keyunknown frontmatter key "tools"
Process rating: all ten parameters 51/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 20When it triggers. No condition that starts the skill
- 40Consistency. Frontmatter name (memory-orchestrator) differs from the folder (memory-orchestrator-v2)
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 17 steps
- 100Execution cost. Instruction body is 2227 tokens
- 100Running it twice. No mutating operations
- 100Progress reporting. Reports progress
- low 15 top-level sections: this looks like several domains in one skill
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +5Description quotes 2 example trigger phrases
- +3Description length 727: enough signal without eating the budget
- +4Structure: 38 headings
- +3Step-by-step instructions: 17 items
- +4Has examples (18 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.