AD opensource-skill-to-github
Quickly open-source a local skill to GitHub (primary) and optionally clawhub.com. Workflow: slug pre-check, fork to opensourceskills, strip internal info, normalize SKILL.md, generate LICENSE/README, init git, push to GitHub with configurable token source, and optionally publish to clawhub. Use when the user says "open-source this skill", "把这个 skill 开源", "发到 github", "publish skill publicly", or "把本地 skill 发开源". Hard rules: never modify the original skill in place, never auto-add force/yes flags, never write tokens to git/remote/memory, and ask the user at decision points.
Quickly open-source a local skill to GitHub (primary) and optionally clawhub.com. Workflow: slug pre-check, fork to opensourceskills, strip internal info…
As a process D 49/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
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
- 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
✓ No critical or high findings
Medium and low: 1
-
medium Exfiltration
net-credential-usereferences/opensource_playbook.md:252Credential used in a network call (verify the destination is the intended service)REMOTE=$(curl -s -H "Authorization: Bearer $GITHUB_TOKEN" \
Files scanned: 24. 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. 19 mutating operations with no state check
- 60Tools and files. Uses tools (bash, web, node) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 70 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3387 tokens
- low 18 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (11 tags): a typed call is more reliable
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
- -241 emoji in the instructions: noise for the model
- -31 of 14 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +5Description quotes 5 example trigger phrases
- +3Description length 579: enough signal without eating the budget
- +4Structure: 28 headings
- +3Step-by-step instructions: 70 items
- +4Has examples (15 code blocks)
- +4Reference files are cited in the instructions (5 of 5)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.