SKILLEMALL.ai

CD opencode

Monitor and manage your OpenCode tasks using helper scripts.

volcengine/OpenViking Agent Skills author: volcengine AGPL-3.0 4 files · 2 scripts body ≈ 193 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Monitor and manage your OpenCode tasks using helper scripts.

As a process D 42/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureSoftware developmentAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
C
86/100
safety, quality, tests
Safety 60%
98
Quality 40%
69
Run on models
none yet
Process rating
D
42/100
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

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Dangerous commands cmd-background-process opencode_utils.py:61
    Starts a background / autostarted process
    preexec_fn=os.setsid,  # 关键:创建新的会话ID
  • low Secrets in code secret-high-entropy-token status.json:1
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    {"ses_…F6K": 1771705418600.0, "ses_…DAQ": 1771705404886.0, "ses_…MUA": 1771681820419.0, "ses_…Ms5": 177168174041
    quoted

Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 42/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 75Steps. 3 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 193 tokens
  • 100Running it twice. No mutating operations

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)
  • +3Description length 60: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 6 headings
  • +3Step-by-step instructions: 3 items
  • +4Has examples (1 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.