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BC aws-agentcore-langgraph-free

AWS AgentCore与LangGraph部署助手(云写操作需谨慎)。Multi-agent systems on AWS Bedrock AgentCore with LangGraph orchestration. Source: <。适用于多种工作场景,提供专业的能力支持。轻量级设计,低资源占用,适配云端与本地部署。

ClawHub Agent Skills author: 天轰穿 v1.0.3 MIT-0 2 files body ≈ 2 122 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

AWS AgentCore与LangGraph部署助手(云写操作需谨慎)。Multi-agent systems on AWS Bedrock AgentCore with LangGraph orchestration.

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

IntegrationAWSAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 8 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 85Steps. 33 steps, 1 vague phrases
  • 100Tools and files. Tools declared in frontmatter
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2122 tokens
  • low 22 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
  • +2Single-language instructions
  • +3Description length 164: enough signal without eating the budget
  • +4Structure: 38 headings
  • +3Step-by-step instructions: 33 items
  • +4Has examples (7 code blocks)
  • +1License stated

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

External checks

ClawHub: suspicious
This is an AWS deployment guide, but its read-only declaration, broad activation terms, and under-scoped cloud command guidance make it something users should review before installing.
LLM: suspicious (high) · 3 Aug 2026