SKILLEMALL.ai

AC skill-manager-all-in-one

One-stop skill management for OpenClaw. 一站式技能管理,引导式使用,嵌套搜索、审计、创建、发布、批量更新等必要 skill。Use when reviewing installed skills, searching ClawHub, checking updates, auditing security, creating or publishing skills. Triggers: skill, 技能, ClawHub, clawhub, 安装技能, install skill, 搜索技能, search skill, 更新技能, update skill, 创建技能, create skill, 发布技能, publish skill, 卸载技能, uninstall skill, 管理技能, manage skills, 技能管理, skill manager, 技能更新, 技能审计, audit skill, 查看已发布, view published, 宣传技能, promote skills, 技能帮助, skill help, 技能问题, skill problem, 批量更新, bulk update, 技能搜索, 技能安装, 技能发布, ClawHub发布, ClawHub搜索, ClawHub安装, 技能体系, skill system, 技能目录, skill directory, 找技能, find skill, 新技能, new skill, 技能版本, skill version.

modbender/skill-library-mcp Agent Skills author: modbender MIT 6 files body ≈ 434 tokens Open the sourcegithub.com analyzed 3 d ago

One-stop skill management for OpenClaw.

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerAI and agentsSoftware developmentData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
86
Run on models
none yet
Process rating
C
51/100
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

    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: 6. 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 51/100

    • 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
    • 30Running it twice. 4 mutating operations with no state check
    • 100Tools and files. No external tools needed
    • 100Steps. 12 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 434 tokens

    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
    • -213 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 687: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 12 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (4 of 5)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.