CC python
The Vovk.ts Python client — generating a typed Python package from a Vovk API with `vovk-python`, the `py` / `pySrc` templates, `TypedDict` shapes, JSON + JSON Lines consumption, client-side validation, and PyPI publishing. Use whenever the user wants a typed Python SDK, ongoing Python integration, or PyPI publishing — phrasings like "generate a Python client / SDK", "ship a Python package for my API", "use vovk-python", "pySrc template", "python client streaming", "publish to PyPI". Does NOT cover controller / procedure authoring → hand off to `procedure` skill. Does NOT cover `vovk bundle` in general → hand off to `bundle` skill. Does NOT cover JSON Lines server-side → hand off to `jsonlines` skill. **Does NOT cover one-off HTTP calls** — Vovk endpoints are plain REST, so a `curl`, `httpx`, or `requests` call against `/api/...` is the simpler answer for "I just want to hit my API from a Python script / CLI / shell" use cases. Reach for this skill only when the user actually wants generated typed client code.
The skill teaches an AI to generate a typed Python client from a Vovk API using vovk-python and py/pySrc templates. Promises TypedDict, client-side validation, JSON Lines support, and PyPI publishing.
One file, description truncated (lint flags length). No model runs, no sandbox. Grade C: quality 61, process 52 — the skill works but the instructions are incomplete. No critical issues found, though medium/low findings aren't listed explicitly.
Install if you specifically need a Vovk client and can flesh out the instructions yourself. Not tested on models, so your first run is a gamble.
The Vovk.ts Python client — generating a typed Python package from a Vovk API with vovk-python, the py / pySrc templates, TypedDict shapes, JSON + JSON Lines…
As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
How to improve
- Shorten the description to 1024 characters.
- 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-longdescription is 1025 chars, limit 1024
Process rating: all ten parameters 52/100
- 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. 5 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 85Steps. 18 steps, 1 vague phrases
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2998 tokens
- low 10 top-level sections: this looks like several domains in one skill
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 1025: 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: 18 items
- +4Has examples (21 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 61.