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 FuRongJun-1999/dsh-memory --skill analyze-type-infergit clone --depth 1 https://github.com/FuRongJun-1999/dsh-memoryWrote 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/furongjun-1999/dsh-memory/analyze-type-infer)<a href="https://agentmods.dev/skills/furongjun-1999/dsh-memory/analyze-type-infer"><img src="https://agentmods.dev/badge/skills/furongjun-1999/dsh-memory/analyze-type-infer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/furongjun-1999/dsh-memory/analyze-type-infer"><img src="https://agentmods.dev/badge/skills/furongjun-1999/dsh-memory/analyze-type-infer.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00121 | $0.00570 |
| Opus 5 | $0.00060 | $0.00285 |
| Sonnet 5 | $0.00024 | $0.00114 |
| Haiku 4.5 | $0.00012 | $0.00057 |
Grade A, and why
analyze-type-infer 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 today.
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
分析-类型推断（analyze-type-infer）
When to use
任务「类型推断」；对照：C2 语义深化——类型推断（赋值→数值/文本/布尔，冲突→混合）+ 条件空间声明登记（目标3 分析器完整化）。
克制条款（不适用条件）
kind 非 {COND, assign} 时
How to execute
逐语句分派，冲突赋值 → 类型标记'混合'（编译期拦截候选）
Verification
- 单元样例 4 条（cases 断言）
- 物理基底：按 calibration 对照（编译/运行/断言裁决）
References
- 单元库：compiler_code_units.py「分析-类型推断」
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.
- today First seen · 46 lines · 121 tokens per session scan A 3ac32f827eb8
analyze-type-infer is a skill published in the GitHub repository FuRongJun-1999/dsh-memory (159 stars, last pushed today), licensed MIT. It adds 121 tokens to every session and 570 once invoked, about $0.0006 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 2026-09-11.
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