BD github-autosetup
自动化配置 pi 在 GitHub/git 的行动（纯 Git Bash 驱动）：环境探测 → 传输通道决策（https/GCM 或 SSH）→ 浏览器 OAuth → 带口令 SSH 密钥 + agent → 建仓推送 → post-commit 自动推送 + 计划任务兜底。内置敏感信息传递 SOP（令牌/口令/密钥/cookie 零明文）。当用户说"配置 github / 自动推送 / 建仓库 / 推送仓库 / 配置 git"时使用。
自动化配置 pi 在 GitHub/git 的行动（纯 Git Bash 驱动）：环境探测 → 传输通道决策（https/GCM 或 SSH）→ 浏览器 OAuth → 带口令 SSH 密钥 + agent → 建仓推送 → post-commit 自动推送 + 计划任务兜底。内置敏感信息传递…
As a process D 45/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.
The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".
If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.
How to improve
- Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 1
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high Exfiltration
exfil-read-secret-filesSKILL.md:106Reads credential / secret files**验证（必做）**：任一仓库提交临时文件 → `git ls-remote origin main` 与本地 HEAD 一致 → 清理。重启后提醒用户 `ssh-add ~/.ssh/id_e…_gh`（agent 重启清空）。
Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
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. 6 mutating operations with no state check
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 100Steps. 20 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1177 tokens
- 100Progress reporting. Reports progress
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
- +2Single-language instructions
- +3Description length 221: enough signal without eating the budget
- +4Structure: 9 headings
- +3Step-by-step instructions: 20 items
- +4Has examples (7 code blocks)
- +3All 2 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.