SKILLEMALL.ai

AD project-organization

大项目组织方法论 + 可执行脚手架(跨机跨系统)。当用户开始一个会长期演进、多阶段/多子任务的项目, 需要按研究/业务主题分层组织文件、或项目文件已散落在根目录需归整时使用。 核心是"大项目制 + 按语义分层 + agent 自动判断归属 + .trash 软删除"。 提供新建大项目/子项目骨架、结构自检、结构安全删除(.trash)脚本与模板。 含项目根下的「缓冲区 / buffer」——人类把文件丢进 `buffer/`,AI 说"读取 buffer"即按语义归位到 projects/。 触发词:组织项目、项目结构、大项目、项目管理、目录整理、项目化、分层结构、新建项目、归档项目、 读取 buffer、处理 buffer、缓冲区、把文件丢进 buffer、buffer 归位。 Use ONLY for long-lived / multi-phase projects;一次性临时任务走 `temp-workspace`,不要用本 skill。

ClawHub Agent Skills author: TOMUIV v2.1.1 MIT-0 17 files · 7 scripts body ≈ 2 995 tokens Open the sourceclawhub.ai↗ analyzed 6 d ago

大项目组织方法论 + 可执行脚手架(跨机跨系统)。当用户开始一个会长期演进、多阶段/多子任务的项目, 需要按研究/业务主题分层组织文件、或项目文件已散落在根目录需归整时使用。 核心是"大项目制 + 按语义分层 + agent 自动判断归属 + .trash 软删除"。…

As a process D 45/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI and agentsPersonal productivitytype 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
D
45/100
Unfinished process
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: 17. 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 45/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 8 mutating operations with no state check
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 100Steps. 50 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2995 tokens
    • 100Progress reporting. Reports progress
    • low 12 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

    • +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
    • +5Description quotes 2 example trigger phrases
    • +3Description length 429: enough signal without eating the budget
    • +4Structure: 22 headings
    • +3Step-by-step instructions: 50 items
    • +4Has examples (4 code blocks)
    • +3All 7 scripts are documented
    • +1License stated

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

    External checks

    ClawHub: suspicious
    This skill mostly does what it says, but it deserves review because some file-moving and soft-delete commands are not clearly limited to the project folder.
    LLM: suspicious (high) · 27 Sept 2026