SKILLEMALL.ai

BF manage-gh-runners

Add, remove, list, or swap GitHub Actions self-hosted runners on a CI host that uses the env-var-driven `gh-runner` docker image (raw `docker run`, no compose). Triggers, "add gh runner", "remove gh runner", "list ci runners", or similar. Run `setup-ci-host` first if /data/miles_ci is not yet provisioned. Boundary vs the `actions-runner` + docker-compose flow documented in `tests/ci/README.md`, only use this skill when `docker ps` shows the `gh-runner` image. Does NOT prepare the host filesystem layout — that is `setup-ci-host`'s job. Host tags (`--tags aliyun`) give job containers RDMA on RoCE hosts.

radixark/miles Agent Skills author: radixark 4 files · 2 scripts body ≈ 1 881 tokens Open the sourcegithub.com↗ analyzed 4 d ago

Add, remove, list, or swap GitHub Actions self-hosted runners on a CI host that uses the env-var-driven gh-runner docker image (raw docker run, no compose).

As a process F 33/100 · No process to follow — References files that are not bundled: ../../../tests/ci/README.md, ../setup-ci-host/SKILL.md, hooks/aliyun/docker

GeneratorGitHubDockerInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
F
33/100
No process to follow
References files that are not bundled: ../../../tests/ci/README.md, ../setup-ci-host/SKILL.md, hooks/aliyun/docker
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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: 4. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: ../../../tests/ci/README.md
  • warning missing-ref reference to a missing file: ../setup-ci-host/SKILL.md
  • warning missing-ref reference to a missing file: hooks/aliyun/docker
  • warning missing-ref reference to a missing file: ../../../../.github/workflows/_run-ci.yml
  • note frontmatter-key unknown frontmatter key "user_invocable"

Process rating: all ten parameters 33/100

Will not run. References files that are not bundled: ../../../tests/ci/README.md, ../setup-ci-host/SKILL.md, hooks/aliyun/docker
  • 0Tools and files. 4 referenced file(s) missing: ../../../tests/ci/README.md, ../setup-ci-host/SKILL.md, hooks/aliyun/docker
  • 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
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 14 mutating operations with no state check
  • 100Steps. 13 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1881 tokens
  • low The response is described with custom markup (18 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
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 3 example trigger phrases
  • +3Description length 608: enough signal without eating the budget
  • +4Structure: 7 headings
  • +3Step-by-step instructions: 13 items
  • +4Has examples (4 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.