SKILLEMALL.ai

BC tool-orchestrator

工具编排器是一个端到端加密、去中心化存储的智能体记忆编排系统。针对传统记忆工具"安装复杂、凭据暴露风险、网关重启困难、槽位绑定冲突"四大痛点,构建了统一记忆工具接口、零暴露凭据管理、自动恢复机制和智能槽位管理四大核心能力。 核心能力包括:通过memory_search/memory_get原生工具进行记忆检索;后台自动从对话中提取事实(无需手动调用工具记忆每个事实);显式记住仅按需触发;端到端加密确保记忆仅用户可解密;去中心化网络存储避免单点依赖;CLI工具支持记忆固定、作用域设置、导出等高级管理。 适用场景:需要跨会话持久化用户偏好的智能助手、注重隐私的端到端加密记忆需求、去中心化存储避免供应商锁定的场景、需要记忆固定与作用域管理的高级用户、希望自动捕获事实而非手动记录的效率导向用户。 差异化亮点:相比原始版本,新增零暴露凭据硬约束(禁止读取/列出/展示任何凭据文件)、自动恢复流程(/tool-orchestrator-restart自主重启无需用户介入)、智能槽位管理(避免disabled插件槽位卡死)、简化配对流程(in-process HTTP路由替代CLI子进程)、安全短语防护(12词恢复短语绝不进入聊天或LLM上下文)、FAQ与故障排查决策树。 触发关键词:工具编排、加密记忆、去中心化存储、记忆检索、凭据管理、tool-orchestrator、memory-search、encrypted-memory、decentralized

ClawHub Agent Skills author: 天轰穿 v1.0.0 MIT-0 2 files body ≈ 1 576 tokens Open the sourceclawhub.ai analyzed 25 h ago

工具编排器是一个端到端加密、去中心化存储的智能体记忆编排系统。针对传统记忆工具"安装复杂、凭据暴露风险、网关重启困难、槽位绑定冲突"四大痛点,构建了统一记忆工具接口、零暴露凭据管理、自动恢复机制和智能槽位管理四大核心能力。…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureCustomer supportAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
68
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • 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: 0. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "summary"
  • note frontmatter-key unknown frontmatter key "tools"

Process rating: all ten parameters 53/100

  • 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
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 22 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1576 tokens
  • 100Running it twice. Mutating operations check current state
  • low 11 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

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -2localhost URLs: will not work for another user
  • +2Single-language instructions
  • +3Description length 636: enough signal without eating the budget
  • +4Structure: 19 headings
  • +3Step-by-step instructions: 22 items
  • +4Has examples (6 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.

External checks

ClawHub: suspicious
The skill is coherent for encrypted memory management, but it asks the agent to restart runtime behavior without user approval and can launch a detached pairing process that may outlive normal tool controls.
LLM: suspicious (high) · 17 Jul 2026