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/sisibeloved/cpython-optimize-skill/workflow-feature-driven-optimizationnpx skills add sisibeloved/cpython-optimize-skill --skill workflow-feature-driven-optimizationgit clone --depth 1 https://github.com/sisibeloved/cpython-optimize-skillWhat 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 | $0.00041 | $0.00426 |
| Opus 5 | $0.00020 | $0.00213 |
| Sonnet 5 | $0.00008 | $0.00085 |
| Haiku 4.5 | $0.00004 | $0.00043 |
Grade A, and why
workflow-feature-driven-optimization 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
Feature Driven Optimization Workflow
Agent 分派
| 阶段 | Agent | 技能 |
|---|---|---|
| 环境确认 | cinderx-environment-verifier |
cinderx-env-validate |
| 功能/集成测试 | cinderx-jit-analyst / orchestrator |
cpython-runtime-test-run、cinderx-smoke-check |
| JIT/路径分析 | cinderx-jit-analyst |
cinderx-jit-entry-check、cinderx-hir-lir-analyze |
| 性能验证 | pyperformance-candidate-runner |
pyperformance-worker-run / pyperformance-suite-run |
| 结果分析 | pyperformance-benchmark-analyst |
pyperformance-result-compare |
| 报告 | pyperformance-benchmark-analyst |
cinderx-optimization-report |
TDD 要求
修改代码前先做测试缺口判断：
- 检查是否需要补充或修改 RuntimeTests 功能用例；不需要时写明理由。
- 检查是否需要补充或修改 test_cinderx/lib test 集成用例；不需要时写明理由。
- 新增或修改的功能用例必须使用 Python
unittest框架，不能改成 pytest 风格或只写脚本式断言。 - 功能用例和集成用例先于性能验证；没有对应行为覆盖时，不能只靠 pyperformance 收益证明特性正确。
Gate
先功能后性能。L1 功能/集成测试未通过时，不讨论性能收益；目标 benchmark 无收益时，先解释假设失败原因。
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 · 31 lines · 41 tokens per session scan A 2e5b7da8c734
workflow-feature-driven-optimization is a skill published in the GitHub repository sisibeloved/cpython-optimize-skill (2 stars, last pushed 3d ago), licensed MIT. It adds 41 tokens to every session and 426 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.
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