SKILLEMALL.ai
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Vovk.ts segments in depth (`base` already covers the one root segment and its `route.ts`) — the unit of backend slicing in a Vovk project. Use whenever the user talks about splitting a backend into parts, API paths, catch-all routes, `route.ts`, `initSegment`, per-segment runtime/maxDuration, static APIs (OpenAPI specs, historical datasets), static export (`output: 'export'`), `generateStaticParams`, or the `vovk new segment` CLI. Triggers on phrasings like "split my backend", "add an API slice", "make a static OpenAPI endpoint", "separate admin API", "pre-render API at build time", "I need a `/api/foo` namespace", "add a root/named/static segment". Does NOT cover procedures/handlers themselves — hand off to the `procedure` skill for anything inside a controller. Does NOT cover OpenAPI authoring beyond wiring per-segment `openAPIObject` — hand off to the `openapi` skill. Does NOT cover multi-tenant routing mechanics (subdomains, `multitenant()` proxy, per-tenant frontend pages) — hand off to the `multitenant` skill.

The skillemall take

The skill teaches an AI to distinguish Vovk.ts segments — a backend slicing layer with separate `route.ts`, runtime settings, and static exports. Triggers on requests like "split my API into `/admin` and `/public`" or "make OpenAPI static at build time". The description lists triggers and use cases, but contains no actual tool: no config templates, no working examples, no code snippets for `initSegment` or `generateStaticParams`. The description cuts off mid-sentence and leaves unclear what the AI should actually do — generate folder structure, write boilerplate, or just explain syntax. Without concrete examples, it reads as a reference card, not a skill.

Useful only if your AI already handles Vovk and you need to narrow its scope. If you're new to segments, this won't teach you. Set it only if you're already shipping Vovk projects.

finom/vovk Agent Skills author: finom 1 file body ≈ 2 973 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Vovk.ts segments in depth (base already covers the one root segment and its route.ts) — the unit of backend slicing in a Vovk project. Use whenever the user…

As a process C 53/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureSoftware developmenttype 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
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

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 · 0

✓ No critical or high findings

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error description-long description is 1031 chars, limit 1024

Process rating: all ten parameters 53/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 7 mutating operations with no state check
  • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
  • 60Failures and branches. 2 branches
  • 70When it triggers. States when to use, but not when not to
  • 85Steps. 25 steps, 3 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2973 tokens
  • low 10 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (9 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)
  • +3Description length 1031: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 7 example trigger phrases
  • +4Structure: 23 headings
  • +3Step-by-step instructions: 25 items
  • +4Has examples (9 code blocks)

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