SKILLEMALL.ai

AB aws-best-practice-research

Use when researching, compiling, or assessing best practices for any AWS service, building HA/DR/security checklists from official AWS documentation, or checking whether live AWS resources follow official recommendations. Requires aws-knowledge-mcp-server. Triggers on "best practices", "compile checklist", "summarize HA/DR best practices", "what are the best practices for", "find all best practices", "check my cluster", "audit my redis", "assess my redis", "assessment", "是否符合最佳实践", "检查现有资源", "查找最佳实践", "编译检查清单", "总结最佳实践", "帮我查找", "汇总成表", "帮我检查", "审计一下", "评估一下".

ClawHub Agent Skills author: panlm v1.0.0 MIT-0 7 files body ≈ 4 096 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 71/100 · Nearly there — weak spots: result and completion, running it twice

AnalyzerAWSInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
93
Run on models
none yet
Process rating
B
71/100
Nearly there
Result and completion w 14
0
Running it twice w 4
30
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

    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: 7. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 71/100

    • 0Result and completion. Does not say what the result is
    • 30Running it twice. 3 mutating operations with no state check
    • 60Tools and files. Uses tools (node) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4096 tokens
    • 100Steps. 68 steps
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 16 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 14 example trigger phrases
    • +3Description length 566: enough signal without eating the budget
    • +4Structure: 20 headings
    • +3Step-by-step instructions: 68 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (2 of 3)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.

    External checks

    ClawHub: suspicious
    The skill has a legitimate AWS assessment purpose, but its live-assessment path handles credentials in a risky way that users should review before installing.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026