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BC aws-graph-agent

AWS Bedrock AgentCore与LangGraph多代理部署编排工具。提供StateGraph状态图编排、AgentCore Runtime HTTP封装（8080端口）、 Memory跨会话STM/LTM持久记忆、Gateway外部API/Lambda工具集成、CLI全生命周期管理五大核心能力. 适用于多代理协调的复杂业务系统、跨会话持久记忆代理、外部API集成到代理工具链、生产级AI代理部署.

ClawHub Hermes author: 天轰穿 v1.0.4 MIT-0 2 files body ≈ 3 304 tokens Open the sourceclawhub.ai↗ analyzed 22 h ago

AWS Bedrock AgentCore与LangGraph多代理部署编排工具。提供StateGraph状态图编排、AgentCore Runtime HTTP封装（8080端口）、 Memory跨会话STM/LTM持久记忆、Gateway外部API/Lambda工具集成、CLI全生命周期管理五大核心能力.

As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationAWSDockerOperations and projectsInfrastructureAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Hermes Agent
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
64
Run on models
none yet
Process rating
C
56/100
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.
  2. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 206 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • warning description-no-when neither description nor a "## When to Use" section says when to use the skill
  • 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 "summary_zh"
  • note frontmatter-key unknown frontmatter key "tools"
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 56/100

  • 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. 1 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 28 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3304 tokens
  • low 21 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 206: enough signal without eating the budget
  • +4Structure: 52 headings
  • +3Step-by-step instructions: 28 items
  • +4Has examples (13 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.

External checks

ClawHub: suspicious
This AWS deployment skill is mostly coherent, but it gives risky, under-scoped guidance for cloud resource deletion and secret handling.
LLM: suspicious (high) · VirusTotal: · 2 Aug 2026