validate-python-stack

A validation workflow for a Python template or a project generated from it. It checks generated files, dependencies, formatting, tests, security workflows, skills, and representative runtime behavior.

In plain words
What is it for?
Use it before committing, publishing, deploying, or reviewing the Python template or a project created from it.
Why use it?
It catches broken configuration, stale generated files, failed tests, and integrations that only look complete but do not run before release or deployment.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/leynier/python-template/validate-python-stack
Any agent
npx skills add leynier/python-template --skill validate-python-stack
Clone the repo
git clone --depth 1 https://github.com/leynier/python-template

Made for: Claude Code, Codex.

Per session 51 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 437 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00051 $0.00437
Opus 5 $0.00026 $0.00218
Sonnet 5 $0.00010 $0.00087
Haiku 4.5 $0.00005 $0.00044

Measured yesterday against content hash bf1a59dd5a26, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

validate-python-stack scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/validate-python-stack/SKILL.md · 31 lines

What it actually says

Validate Python Stack

Choose the validation path that matches the current directory and report exact failures without hiding skipped checks.

Template Repository

  1. Run uv sync --all-groups.
  2. Run uv run python scripts/compile_catalog.py --check.
  3. Run uv run ruff check . and uv run ruff format --check ..
  4. Run uv run pytest -n auto. Use focused tests first while iterating, but finish with the full suite.
  5. For every directory under skills/, run the skill-creator validator when available and run npx --yes skills add . --list to verify discovery.
  6. Generate the changed presets or custom combinations and exercise their real CLI, API, MCP, training, serving, or deployment entry point as applicable.
  7. If GitHub workflows changed and Docker is available, run the repository's local Actions validation before pushing.

Generated Project

  1. Read .copier-answers.yml, readme.md, and pyproject.toml to identify enabled layers.
  2. Run uv sync --all-groups, uv run ruff check ., uv run ruff format --check ., and uv run pytest.
  3. Run npx --yes skills add . --list; expect project-workflow and only the conditional skills appropriate to the selected stack.
  4. Exercise the primary entry point. For a containerized target, build the Docker image and verify its health route locally when Docker is available.
  5. Validate generated JSON, TOML, YAML, Python, and HCL as structured data rather than with string-only assertions.

Reporting

Separate passed, failed, and unavailable checks. Include the command, relevant error, and whether the failure belongs to the template, the generated project, local infrastructure, or an external service.

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 31 lines · 51 tokens per session scan A bf1a59dd5a26

Subscribe to this mod's changes

validate-python-stack is a skill published in the GitHub repository leynier/python-template (37 stars, last pushed 3d ago), licensed MIT. It adds 51 tokens to every session and 437 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

heaven-style

Standalone personal code-quality, architecture, documentation, graphical-interface, and project-management guide for TypeScript and Python repositories. Use when writing or reviewing code or docs, designing architecture or UI, refactoring, or aligning tests; apply repository policy first, then shared design philosophy…

Magolor/Blueprint · 63 tokens

datamodel-code-generator

Use this skill when the user wants Python data models, Pydantic models, dataclasses, TypedDicts, msgspec structs, or type-safe Python classes generated from OpenAPI, AsyncAPI, JSON Schema, GraphQL, JSON/YAML/CSV sample data, MCP tool schemas, Protocol Buffers, XML Schema, Apache Avro, or existing Python model objects.…

koxudaxi/datamodel-code-generator · 147 tokens

background-task

Add or modify work that runs outside the request/response cycle — emails, document ingestion, webhooks, cleanups, scheduled jobs. Use when something is slow or fire-and-forget, or when adding a periodic/cron task. This project's queue is {{ cookiecutter.backgroundtasks }}.

vstorm-co/full-stack-ai-agent-template · 62 tokens

frontend-feature

Build a new page, view, or data-driven feature in the Next.js frontend. Use when adding a route under the dashboard/marketing area, wiring UI to a backend endpoint, adding client state, or creating a localized page. Covers App Router, data fetching, Zustand stores, and i18n.

vstorm-co/full-stack-ai-agent-template · 64 tokens

rag-knowledge

Work with the RAG knowledge base — ingest documents, run semantic search, manage collections, or add a sync source/connector (Google Drive, S3). Use when populating or debugging the knowledge base, tuning retrieval, or adding a new document source. This project uses {{ cookiecutter.vectorstore }} + {{…

vstorm-co/full-stack-ai-agent-template · 76 tokens

alembic-migration

Create, review, and apply database schema changes with Alembic. Use whenever a SQLAlchemy model is added or changed, a column/index/constraint needs to change, or a data backfill is required — anything that alters the PostgreSQL schema.

vstorm-co/full-stack-ai-agent-template · 56 tokens