SKILLEMALL.ai

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管理 git/非 git 项目的长期上下文与断点恢复。触发场景:继续/恢复之前的项目、提交代码后更新项目进度、会话结束前保存项目状态、接手或纳管一个已有项目、为长期推进的任务建立项目档案。Manage long-term project context and session-resume checkpoints for git and non-git projects. Use when resuming a project, updating progress after commits, saving state before ending a session, adopting an existing project, or archiving a long-running task.

ClawHub Agent Skills author: XiaoM v1.0.1 MIT-0 13 files body ≈ 936 tokens Open the sourceclawhub.ai analyzed 2 d ago

管理 git/非 git 项目的长期上下文与断点恢复。触发场景:继续/恢复之前的项目、提交代码后更新项目进度、会话结束前保存项目状态、接手或纳管一个已有项目、为长期推进的任务建立项目档案。Manage long-term project context and session-resume checkpoints…

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

ReferenceSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
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: 13. 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. 13 mutating operations with no state check
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 100Steps. 48 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 936 tokens
    • 100Progress reporting. Reports progress
    • low 15 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
    • -44 reference files, but SKILL.md never points to them: the model will not open them
    • +1No license
    • +2Single-language instructions
    • +3Description length 351: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 48 items
    • +4Has examples (4 code blocks)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.

    External checks

    ClawHub: suspicious
    This skill appears purpose-built for local project context recovery, but it should be reviewed carefully because setup automatically adds a persistent Git hook that records commit metadata.
    LLM: suspicious (high) · 4 Sept 2026