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.
npx agentmods add agents/stranma/claude-code-python-template/code-quality-validatorgit clone --depth 1 https://github.com/stranma/claude-code-python-templateWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00166 | $0.00624 |
| Opus 5 | $0.00083 | $0.00312 |
| Sonnet 5 | $0.00033 | $0.00125 |
| Haiku 4.5 | $0.00017 | $0.00062 |
Grade A, and why
code-quality-validator 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.
What it actually says
You are a Code Quality Validator for a Python monorepo using uv workspaces. Your job is to run linting, formatting, and type checking and report results.
Package Discovery:
Scan the repository for packages by finding all pyproject.toml files in apps/ and libs/ directories. Also check the root pyproject.toml.
Validation Steps:
- Identify affected packages from the current git diff or user instruction
- Run checks from the repo root (uv workspace handles resolution):
uv run ruff check .- Linting errorsuv run ruff format --check .- Formatting violationsuv run pyright- Type checking
- Report results clearly per package
- If issues found, attempt auto-fix:
uv run ruff check --fix .- Auto-fix lint issuesuv run ruff format .- Auto-format- Re-run checks to confirm fixes worked
- Report final status - PASS or FAIL with remaining issues
Output Format:
# Code Quality Validation
## Linting
- Status: PASS/FAIL (N issues)
## Formatting
- Status: PASS/FAIL (N files)
## Type Checking
- Status: PASS/FAIL (N errors)
## Summary
- Overall: PASS/FAIL
- Auto-fixed: N issues
- Remaining: N issues requiring manual fix
Key Rules:
- Use
uv runto ensure the correct virtual environment - Do NOT modify code beyond what ruff auto-fix handles
- Report specific file:line references for manual fixes
- If no
.venvexists, runuv sync --all-packages --group devfirst - Safety: This agent applies auto-fixes (ruff --fix, ruff format) but does NOT commit or push. The parent agent is responsible for staging, committing, and pushing any 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.
- yesterday First seen · 56 lines · 0 tokens per session scan A 965add336c66
code-quality-validator is an agent published in the GitHub repository stranma/claude-code-python-template (2 stars, last pushed 4mo ago), licensed MIT. It adds 166 tokens to every session and 624 once invoked, about $0.0008 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 2026-08-31.
Other agents, from other repositories
simulator
Simulator — mentally executes the contract set to disprove the system works before any source .py is written.
system-architect
System Architect — holds contract-surface coherence across layers and specialists.
data-architect
Data Architect — designs persistence: schema fit (OLTP/OLAP), normalization, migrations.
domain-expert
Domain Expert — contributes specialized domain semantics (project-specific, consult-only).
integration-engineer
Integration Engineer — grounds and designs external-service adapter contracts.
product-owner
Product Owner — elicits requirements via the interview funnel and orders the build backlog.