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发布并验收 TrailSnap 新版本：汇总提交、同步全项目版本号、更新官网日志与客户端更新清单、执行发布前测试、创建 GitHub Release、监控标签 CI、核验 Release 附件，并生成小红书和公众号文案。Use when the user asks to 发布新版本、发版、创建或检查 GitHub Release、总结版本变更、监控发布 CI、检查发布产物，或撰写版本更新帖子。

LC044/TrailSnap Agent Skills author: LC044 1 file body ≈ 1 616 tokens Open the sourcegithub.com↗ analyzed 17 h ago

发布并验收 TrailSnap 新版本：汇总提交、同步全项目版本号、更新官网日志与客户端更新清单、执行发布前测试、创建 GitHub Release、监控标签 CI、核验 Release 附件，并生成小红书和公众号文案。Use when the user asks to 发布新版本、发版、创建或检查 GitHub…

As a process D 49/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureGitHubSoftware developmentWriting and documentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
D
49/100
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: 1. 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 49/100

    • 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
    • 30Running it twice. 9 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 135 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1616 tokens
    • 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

    • +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
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 198: enough signal without eating the budget
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 135 items

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.