SKILLEMALL.ai
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DD model-only-skill

Prove user-disabled skills remain available to the model.

The skillemall take

The skill claims to prove that user-disabled skills remain available to the model. The folder contains a single reference file with 12 tokens—essentially documentation without working code. No execution data: sandbox and model_run are empty. Quality scores are low: 43 and 38. No critical issues found, but no functionality either—this reads as a memo, not a tool. Skip it unless you're collecting reference materials on skill access control.

deepseek-ai/deepseek-harness Agent Skills author: deepseek-ai MIT 1 file body ≈ 12 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Prove user-disabled skills remain available to the model.

As a process D 38/100 · Unfinished process — weak spots: steps, result and completion, when it triggers

Referencetype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
D
77/100
safety, quality, tests
Safety 60%
100
Quality 40%
43
Run on models
none yet
Process rating
D
38/100
Unfinished process
Steps w 15
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. 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 · 0

✓ No critical or high findings

Files scanned: 1. 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 38/100

  • 0Steps. Prose only: no discrete steps
  • 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
  • 100Tools and files. No external tools needed
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 12 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 57: 120–800 characters recommended
  • +4Structure: 0 headings, hard to scan
  • +3No numbered steps or checklist
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • -15SKILL.md body under 300 characters: nearly empty
  • +1No license
  • +2Single-language instructions

Quality base 70; lint remarks subtract, signals add up to 100. Result: 43.