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/stranma/claude-code-python-template/designnpx skills add stranma/claude-code-python-template --skill designgit 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 | $0.00035 | $0.00623 |
| Opus 5 | $0.00017 | $0.00311 |
| Sonnet 5 | $0.00007 | $0.00125 |
| Haiku 4.5 | $0.00003 | $0.00062 |
Grade A, and why
design 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Design
Crystallize brainstorming into a structured implementation plan. Use at the start or end of brainstorming to formalize an approach.
Steps
1. Check for Conflicts
- Read
docs/DECISIONS.md-- scan for entries that conflict with or overlap the proposed work - Read
docs/IMPLEMENTATION_PLAN.md-- check for active phases or overlapping planned work - If conflicts found: present the contradiction to the user before proceeding
2. Auto-Classify Scope
This is a planning-time estimate based on conversation context. /done will later auto-detect actual scope from workspace signals (branch, files changed, diff size, plan state) at completion time.
| Scope | Criteria |
|---|---|
| Q (Quick) | Trivial, obvious, single-location change (typo, config tweak, one-liner) |
| S (Standard) | Fits in one session, clear scope (new feature, multi-file refactor, investigation) |
| P (Project) | Needs phased execution across sessions (multi-phase feature, architecture change) |
3. Output Structured Plan
The plan format varies by scope:
Q (Quick)
## Plan (Quick)
**Fix**: <what to change>
**File**: <target file>
**Recommendation**: Proceed directly -- this is a single-location change.
S (Standard)
## Plan (Standard)
**Scope**: <1-2 sentence summary>
**Branch**: `<fix|feat|refactor>/<short-name>`
### Files to Modify
- <file> -- <what changes>
### Approach
<numbered steps>
### Test Strategy
<what to test and how>
### Risks
- <potential issues>
P (Project)
## Plan (Project)
**Scope**: <1-2 sentence summary>
### Phase 1: <name>
**Acceptance Criteria**:
- [ ] <criterion>
**Files**: <list>
**Approach**: <summary>
### Phase 2: <name>
...
For P-scoped plans: write the phase breakdown to docs/IMPLEMENTATION_PLAN.md using the same structure shown above (phase name, acceptance criteria, files, approach). The .claude/agents/implementation-tracker.md agent validates this format.
4. Decision Candidates
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 · 88 lines · 35 tokens per session scan A ff645ed33124
design is a skill published in the GitHub repository stranma/claude-code-python-template (2 stars, last pushed 4mo ago), licensed MIT. It adds 35 tokens to every session and 623 once invoked, about $0.0002 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
test-guide
Test-writing guide for Backend.AI — propose success/exception/edge scenarios first, refine them with the user, then implement while reporting per-scenario verification status. Covers fixtures, withtables, mock repositories, pants test, optional TDD cadence.
wu5-dev-flow
使用可审计的 SDD、严格 RED-GREEN-REFACTOR TDD 与安全 Git 门禁初始化、开发、修复、重构和交付 Python 项目。用于任何会修改项目源码、测试、规格、依赖或 Git 历史的任务，也用于继续跨 Session 的现有 spec/ 变更、审查代码、验证完成状态、创建提交或准备 GitHub PR。.
simulate-contracts
Compile the contract set in your head — simulate how the real application would run from the specs, and disprove (or confirm) it works before any source .py is written.
model-data-schema
Author the persistence schema as a binding contract from the captured external shapes and the interview — before any test stub is written.
author-test-stubs
Author test stub files (.pyi) whose signatures express the domain, relationships, and compositions, as the first contract surface.
derive-source-stubs
Derive the source stub files (.pyi) from the reviewed tests — every type the tests reference gets a definition.