SKILLEMALL.ai
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Generate or update chat customization files for AI coding agents

The skillemall take

This skill generates or updates chat customization files for AI coding agents. Single file, no scripts. Audits found only minor issues—nothing critical or high-risk. Quality score sits at 61, process score at 56; solid but unremarkable work. Safety checks out, though it doesn't offset the mediocre functionality rating.

Works across Claude, Cursor, Copilot, and eight other platforms on the list. Install if you need quick config generation without extras. Skip it if you expect polished output or deep integration—wait for updates or pick something else.

microsoft/vscode Claude Code author: microsoft MIT 1 file body ≈ 942 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Generate or update chat customization files for AI coding agents

As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorGitHubSoftware developmentAI and agentstype 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
C
84/100
safety, quality, tests
Safety 60%
100
Quality 40%
61
Run on models
none yet
Process rating
C
56/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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 56/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 6 mutating operations with no state check
  • 60Failures and branches. 2 branches
  • 85Steps. 16 steps, 3 vague phrases
  • 100Tools and files. No external tools needed
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 942 tokens
  • 100Progress reporting. Reports progress

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 64: 120–800 characters recommended
  • +4Structure: 1 headings, hard to scan
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Step-by-step instructions: 16 items

Quality base 70; lint remarks subtract, signals add up to 100. Result: 61.