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/secondlifes/code-intel/pythonnpx skills add SecondLifes/code-intel --skill pythongit clone --depth 1 https://github.com/SecondLifes/code-intelWhat 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 | $0.00067 | $0.00763 |
| Opus 5 | $0.00034 | $0.00381 |
| Sonnet 5 | $0.00013 | $0.00153 |
| Haiku 4.5 | $0.00007 | $0.00076 |
Grade A, and why
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.
This is a copy
100% identical to python — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python
Consolidated from three separately-sourced skills (wshobson/agents:
python-performance-optimization, python-testing-patterns,
python-design-patterns — reviewed for correctness, no issues found)
into one, per this workspace's "one skill per topic" policy.
When to Use
- Writing an ad-hoc Python script to accomplish a task (data processing, file manipulation, automation, validation)
- Identifying and fixing performance bottlenecks (CPU, memory, I/O)
- Writing or reviewing tests (unit, integration, async, property-based)
- Designing a new component/service or refactoring a tangled one
- Deciding between inheritance and composition, or whether to add an abstraction
Usage
| You say | What happens |
|---|---|
"Write a script to parse these CSVs" / ad-hoc helper script request |
Applies the Golden Rules directly (simplicity first, single responsibility) — no reference file needed for a small script. |
"This function is slow, why?" / "Bu kodu optimize et" |
Loads references/performance.md — profiles with cProfile/py-spy first, then applies the matching optimization pattern. |
"Write tests for this" / "Şunun için pytest yaz" |
Loads references/testing.md — fixtures, mocking, async tests, property-based testing as the case requires. |
"Refactor this class" / "Composition mi inheritance mi kullanmalıyım?" |
Loads references/design-patterns.md — SRP, composition-over-inheritance, Rule of Three, dependency injection. |
Golden Rules
- Profile before optimizing. Measure with
cProfile/py-spyfirst — don't guess at bottlenecks. - Simplicity first (KISS). Choose the simplest solution that works; complexity must be justified by concrete requirements.
- Single Responsibility. Each function/class has one reason to change — separate HTTP parsing, business logic, and data access.
- Compose, don't inherit for flexibility and testability; inject dependencies through constructors.
- Rule of Three — don't abstract until you have three real instances of the duplication; a wrong abstraction is worse than repetition.
- Test in isolation. Fakes/mocks for external dependencies; never hit a real database in unit tests.
- Use built-ins and generators for large datasets; prefer
dict/setlookups over list scans.
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.
- yesterday First seen · 47 lines · 67 tokens per session scan A 85ae1ce2af1d
python is a skill published in the GitHub repository SecondLifes/code-intel (2 stars, last pushed 21d ago), licensed Apache-2.0. It adds 67 tokens to every session and 763 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to python, differing in 0 lines, and is treated as a copy.
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