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 skills/alexei-led/architect/tools-pythonnpx skills add alexei-led/architect --skill tools-pythongit clone --depth 1 https://github.com/alexei-led/architectWhat 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 | $0.00118 | $0.01016 |
| Opus 5 | $0.00059 | $0.00508 |
| Sonnet 5 | $0.00024 | $0.00203 |
| Haiku 4.5 | $0.00012 | $0.00102 |
Grade A, and why
tools-python 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.
How it starts
The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python tools
Dependency, complexity, and quality evidence for Python targets. These cover the import graph, layering contracts, type state, dead code, and dependency hygiene.
Evidence dimensions: dependency, structural, semantic (type checkers), and complexity hotspots.
When to use
Use when the system map shows pyproject.toml / setup.py / requirements.
Pick to the question: layering contracts (import-linter), module graph (pydeps),
types (pyright/basedpyright), lint (ruff), dependency hygiene (deptry,
pipdeptree, uv tree), complexity/size (radon, lizard), dead code (vulture). Use
the results to judge module boundaries, dependency direction, cohesion, and
architecture fitness.
Commands
Redirect caches to $TMPDIR (RUFF_CACHE_DIR=$TMPDIR/ruff). Prefer
uv run/uvx so the target env is untouched.
# Layering / boundary contracts (config in .importlinter or pyproject)
uvx --from import-linter lint-imports
# Import/module dependency graph (DOT for the system map)
uvx pydeps src/pkg --max-bacon 2 --noshow -T dot
# Type errors (semantic)
uvx pyright # or: uvx basedpyright
# Lint (style + many bug classes)
RUFF_CACHE_DIR=$TMPDIR/ruff uvx ruff check .
# Declared-vs-used dependency mismatches
uvx deptry src
# Installed dependency tree
uvx pipdeptree # or, for a uv project: uv tree
# Cyclomatic complexity / maintainability hotspots
uvx radon cc -s src # or: uvx lizard src
# Dead code (unused functions/vars — confirm before trusting)
uvx vulture src
Evidence output
Record:
dimension: dependency, structural, semantic, or complexity hotspot.source: Python command, package path, environment/tool runner, and cache location.facts: import contracts, module edges, cycles, type/lint findings, dead-code hypotheses, or clean scope.limits: missing deps, dynamic imports, optional deps, cache failures, or partial package coverage.
Confidence impact
- import-linter contracts and pydeps cycles are direct dependency/boundary
evidence:
tools_used, raisesdependency_graph_healthandboundary_integrityconfidence. - An existing import-linter contract run in CI is an enforced fitness check —
count it toward
architecture_fitness. A contract you'd recommend is not. - radon/lizard complexity flags cohesion/size hotspots (god modules); pair with
the dependency graph before scoring
cohesion_modularity. - vulture and deptry have false positives (dynamic imports, optional deps) — treat their output as hypotheses to confirm, not settled findings.
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 · 107 lines · 118 tokens per session scan A edd1d282d0c7
tools-python is a skill published in the GitHub repository alexei-led/architect (2 stars, last pushed 1mo ago), licensed MIT. It adds 118 tokens to every session and 1,016 once invoked, about $0.0006 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.
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