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/n-n-code/n-n-code-skills/coding-guidance-pythonnpx skills add n-n-code/n-n-code-skills --skill coding-guidance-pythongit clone --depth 1 https://github.com/n-n-code/n-n-code-skillsWhat 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 | $0.00053 | $0.01969 |
| Opus 5 | $0.00026 | $0.00984 |
| Sonnet 5 | $0.00011 | $0.00394 |
| Haiku 4.5 | $0.00005 | $0.00197 |
Grade A, and why
coding-guidance-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 2d ago.
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 — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Coding Guidance
This skill adds portable Python implementation, refactoring, and review guidance.
Adjacent Skills
This skill provides portable Python engineering principles. Compose with:
- Workflow: thinking (ambiguous decision framing), recursive-thinking (stress-testing), security (threat modeling)
- Domain overlays: backend-guidance (server-side code), backend-systems-guidance (stronger backend architecture, reliability, and trust-boundary work), ui-guidance (graphical UI/web frontend), project-core-dev (repo-specific completion discovery and reporting when needed)
Open bundled references only when the task needs that depth:
- references/python-language-and-testing-rules.md for typing, interface, test, data-state, module, and architecture decisions
- references/python-packaging-and-layout.md for distribution, entrypoint, import-layout, or package-structure work
- references/python-service-boundaries.md for async cancellation, subprocesses, workers, and real external I/O or service boundaries
When Not to Lean on This Skill
- non-Python work
- notebook-first exploratory work where reproducibility and module design are not the main concern
- one-off throwaway scripts where the repo explicitly does not want production Python standards
Implementation Workflow
- Read the touched modules, entrypoints, tests, package metadata, and nearby docs before editing.
- Infer intended behavior from existing code, imports, and tests when the request is only partially specified. Ask only when multiple plausible designs would change semantics.
- Choose the narrowest change that keeps contracts, side effects, error handling, and public API shape explicit.
- Implement with simple functions, clear module boundaries, explicit types where they improve the contract, and production-safe behavior at I/O boundaries.
- Add or update tests close to the changed behavior using the repo's existing framework and conventions. Use fixtures or parameterization when they make setup and cases clearer rather than merely shorter.
- Run the narrowest relevant formatter, linter, type checker, packaging check, and test targets the repo supports.
What ships with it
3 files 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.
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.
- 2d ago First seen · 196 lines · 53 tokens per session scan A 34c84e84f8ad
coding-guidance-python is a skill published in the GitHub repository n-n-code/n-n-code-skills (4 stars, last pushed 4d ago), licensed MIT. It adds 53 tokens to every session and 1,969 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 2026-08-31.
Other skills, from other repositories
cocoscout
Relevance-ranked context loading — Tier 2 async subagent (Haiku, <5s) that fires after Tier 1 deterministic checks in UserPromptSubmit. Injects ranked context from CocoGrove, CocoContext, Environment Inspector, Prompt Studio, and CocoDream.
cocowatch
Developer engagement observer — non-blocking, always-on observational layer that tracks Delegation Intensity, Review Depth, and Engagement Zone throughout a session. Summary surfaced at $ship and FULL checkpoints.
the-grilling
Use when adversarial brainstorming is needed after reconnaissance — the Proposer, Devils-Advocate, and Synthesizer each run ONE pass (no rounds); the Don is grilled on the IDEA first, one question at a time, then the proposal is attacked.
using-gangsta
Use when starting any conversation — establishes how to find and use Gangsta skills, enforces the 1% invocation rule, maps platform tools, and routes the Don's intent through the Gangsta Agents Family hierarchy.
interrogation-debugging
Use when encountering any bug, test failure, or unexpected behavior — finds the rat in the code through systematic root-cause interrogation before any fix attempts.
the-hit
Use when the execution plan is approved and it is time for parallel execution — dispatches workers through crew leads to implement work packages with TDD enforcement, report collection, and escalation protocols.