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-platform-differential-discoverynpx skills add sisibeloved/cpython-optimize-skill --skill workflow-platform-differential-discoverygit 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-platform-differential-discovery)<a href="https://agentmods.dev/skills/sisibeloved/cpython-optimize-skill/workflow-platform-differential-discovery"><img src="https://agentmods.dev/badge/skills/sisibeloved/cpython-optimize-skill/workflow-platform-differential-discovery.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 | $0.00045 | $0.00370 |
| Opus 5 | $0.00023 | $0.00185 |
| Sonnet 5 | $0.00009 | $0.00074 |
| Haiku 4.5 | $0.00005 | $0.00037 |
Grade A, and why
workflow-platform-differential-discovery 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 4d 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
Platform Differential Discovery Workflow
Agent 分派
| 阶段 | Agent | 技能 |
|---|---|---|
| 环境确认 | cinderx-environment-verifier |
cinderx-env-validate |
| 平台建模 | cinderx-platform-analyst |
cinderx-isa-microarch-compare |
| 覆盖数据 | pyperformance-benchmark-analyst |
pyperformance-result-compare |
| JIT 细节 | cinderx-jit-analyst |
cinderx-jit-entry-check、cinderx-hir-lir-analyze |
| 报告 | cinderx-platform-analyst |
cinderx-optimization-report |
Gate
先建立 ISA、微架构、perf、benchmark 覆盖矩阵，再进入具体 HIR/LIR。
输出契约
产物是候选用例清单，供深钻 workflow-platform-differential-discovery-deepdive 的阶段 0 选例使用（推荐输入，非强制）。每个候选至少带：
- 用例名
- 双平台 wall clock 差距
- 热度信号
- 为何值得深钻（一句话）
清单回答"先钻哪个用例"，不回答"这个用例怎么优化"——后者交给深钻 workflow 的证据表。不要默认跑三小时全量。
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.
- 4d ago First seen · 32 lines · 45 tokens per session scan A 68ecbc75b8bf
workflow-platform-differential-discovery is a skill published in the GitHub repository sisibeloved/cpython-optimize-skill (2 stars, last pushed 6d ago), licensed MIT. It adds 45 tokens to every session and 370 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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