SKILLEMALL.ai

AD skill-manager

管理 Agent Skills 全生命周期:多源发现、去重溯源、评测对比、安全审查、经 skills CLI 安装/更新/卸载、各 Agent 链接验证与状态诊断。 TRIGGER(中文):"找个适合 X 的技能""有没有现成的 X 技能""对比/评测一下这几个技能""安装这个技能""更新已装技能""卸载/删掉某技能""检查技能状态""技能装不上/看不见了""skill manager"。 DO NOT TRIGGER:从零创作自研技能(用 skill-creator);把自家技能发布到 GitHub 等多平台(用 skill-publisher);项目版本发布流程(用 release-skills);普通编码任务虽提及 skill 一词但无管理意图。

ClawHub Agent Skills author: Steven v1.2.0 MIT-0 4 files body ≈ 2 271 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

管理 Agent Skills 全生命周期:多源发现、去重溯源、评测对比、安全审查、经 skills CLI 安装/更新/卸载、各 Agent 链接验证与状态诊断。 TRIGGER(中文):"找个适合 X 的技能""有没有现成的 X…

As a process D 48/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationGitHubAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
98
Quality 40%
93
Run on models
none yet
Process rating
D
48/100
Unfinished process
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

    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 · 2

    ✓ No critical or high findings

    Medium and low: 2

    ✓ Guard found no suspicious behaviour. 2 matches are attack strings quoted in this security skill's own documentation.

    Files scanned: 4. 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 48/100

    • 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. 5 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (git) that frontmatter does not declare
    • 100Steps. 46 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2271 tokens
    • low 15 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (9 tags): a typed call is more reliable

    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 9 example trigger phrases
    • +3Description length 330: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 46 items
    • +4Has examples (7 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

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

    External checks

    ClawHub: suspicious
    This appears to be a legitimate skill manager, but it gives agents broad lifecycle control over installed skills through unpinned remote CLI commands.
    LLM: suspicious (high) · 2 Oct 2026