SKILLEMALL.ai

BB container-host-aiops

Use this skill whenever the user needs to operate a single container host through the Docker Engine API, Portainer, or Podman — a one-shot host overview; container reads (list/inspect, logs tail, CPU/memory stats, top processes, restart summary); image reads (list, inspect with history, dangling, disk usage); volume reads (list, inspect, dangling); network reads (list, inspect); system reads (info, version, df disk-usage, recent events); Portainer stacks + endpoints; Compose-project rollups (list_compose_stacks, docker+podman); Podman pods (list_pods, podman-only); three flagship analyses — restart-loop RCA (crash-looping containers + cause/action), resource-pressure analysis (CPU/memory vs limits), and image & volume bloat (prune candidates + reclaimable bytes); and eight guarded writes (restart/stop/start/remove a container, prune images/volumes, update resource limits, recreate a Portainer stack). Always use this skill for "Docker host overview", "which containers are crash-looping", "restart loop", "why does this container keep restarting", "container CPU/memory usage", "docker logs", "which containers are near their limits", "resource pressure", "dangling images/volumes", "reclaim disk", "prune images", "stop/start/restart a container", "update a container's memory limit", "Portainer stacks", "compose stacks", "Podman pods" when the context is a Docker, Portainer, or Podman container host. Do NOT use when the target is a cluster orchestrator, a hypervisor, a storage appliance, a backup product, network device config, or OT/industrial equipment — route those to the appropriate other AIops-tools skill. This is for NON-orchestrator container hosts. Governed Docker/Portainer/Podman container-host operations with a built-in governance harness (audit, policy, token budget, undo, risk-tiers). Exercised against a live Docker Engine 27.5.1 daemon (doctor, overview, the three flagship analyses, and a governed stop_container with audit + undo recorded); the Portainer and

ClawHub Claude Code author: wei zhou v0.11.2 MIT-0 6 files body ≈ 2 945 tokens Open the sourceclawhub.ai analyzed 12 h ago

container reads (list/inspect, logs tail, CPU/memory stats, top processes, restart summary); image reads (list, inspect with history, dangling, disk usage)…

As a process B 68/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureDockerAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
83/100
safety, quality, tests
Safety 60%
95
Quality 40%
64
Run on models
none yet
Process rating
B
68/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

How to improve

  1. Shorten the description to 1024 characters.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Bash

Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error description-long description is 2079 chars, limit 1024
  • note description-budget description takes 2079 of the ~15000-char shared budget for all skills
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "summary"
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "installer"

Process rating: all ten parameters 68/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 11 mutating operations with no state check
  • 55Failures and branches. 1 branches
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 39 steps
  • 100When it triggers. States when to use and when not to
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2945 tokens
  • 100Progress reporting. Reports progress
  • low The response is described with custom markup (3 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

  • +3Description length 2078: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +5Description quotes 16 example trigger phrases
  • +4Description says when NOT to use the skill
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 39 items
  • +4Has examples (2 code blocks)
  • +4Reference files are cited in the instructions (3 of 4)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.

External checks

ClawHub: suspicious
This skill is openly for container-host administration, but it installs an unpinned executable and can let an agent change or delete Docker, Podman, or Portainer resources without a built-in approval gate.
LLM: suspicious (high) · 12 Sept 2026