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 skills add sisibeloved/cpython-optimize-skill --skill workflow-jit-optimization-analysisgit clone --depth 1 https://github.com/sisibeloved/cpython-optimize-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sisibeloved/cpython-optimize-skill/workflow-jit-optimization-analysis)<a href="https://agentmods.dev/skills/sisibeloved/cpython-optimize-skill/workflow-jit-optimization-analysis"><img src="https://agentmods.dev/badge/skills/sisibeloved/cpython-optimize-skill/workflow-jit-optimization-analysis.svg" alt="Measured on agentmods" height="20"></a>What 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 5.1 | $0.00044 | $0.00534 |
| Opus 5 | $0.00022 | $0.00267 |
| Sonnet 5 | $0.00009 | $0.00107 |
| Haiku 4.5 | $0.00004 | $0.00053 |
Grade A, and why
workflow-jit-optimization-analysis 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 6d 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.
What it actually says
JIT / Interpreter Case Analysis Workflow
定位
Supporting Workflow：单 benchmark 用例分析分支。端到端任务中由主 Workflow 在 L2 阶段调用，先判定 JIT / 非 JIT，再分流。
Agent 分派
| 阶段 | Agent | 技能 |
|---|---|---|
| 环境确认 | cinderx-environment-verifier |
cinderx-smoke-check |
| worker 运行 | cinderx-jit-analyst |
pyperformance-worker-run |
| 进入 JIT 判定 | cinderx-jit-analyst |
cinderx-jit-entry-check |
| JIT 用例 | cinderx-jit-analyst |
cinderx-hir-dump、cinderx-hir-lir-analyze |
| 解释执行用例 / 非 JIT | cinderx-jit-analyst |
cinderx-interpreter-case-analyze |
| 报告 | cinderx-jit-analyst |
cinderx-optimization-report |
Gate
未证明 benchmark 本体进入 CinderX JIT，不进入 HIR/LIR 优化结论。必须先由 cinderx-jit-entry-check 分流：
entered_cinderx_jit=true：进入 JIT 用例路径，查看 HIR、排查 deopt、分析 LIR / uop / 机器码和平台差异。entered_cinderx_jit=false或目标热函数不进入 gate：进入解释执行用例路径，使用cinderx-interpreter-case-analyze输出穿刺证据、分阶段平铺表、函数形状表和 gate 策略。
进入 JIT 和 HIR dump 前必须复用 ../using-cpython-optimize/references/pyperformance-env-contract.md，确认真实 worker 继承了目标 PYTHONPATH、JIT flags、hook 和非 debug/diagnostic 口径，并提供 .pth、pyvenv.cfg / include-system-site-packages、cinderx.is_initialized() 等 worker 内 CinderX JIT 证据。
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.
- 6d ago First seen · 31 lines · 44 tokens per session scan A aca8a475a997
workflow-jit-optimization-analysis is a skill published in the GitHub repository sisibeloved/cpython-optimize-skill (2 stars, last pushed 8d ago), licensed MIT. It adds 44 tokens to every session and 534 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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