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 compiler-a8399248git 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/compiler-a8399248)<a href="https://agentmods.dev/skills/furongjun-1999/dsh-memory/compiler-a8399248"><img src="https://agentmods.dev/badge/skills/furongjun-1999/dsh-memory/compiler-a8399248/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/compiler-a8399248"><img src="https://agentmods.dev/badge/skills/furongjun-1999/dsh-memory/compiler-a8399248.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 | $0.00110 | $0.00485 |
| Opus 5 | $0.00055 | $0.00243 |
| Sonnet 5 | $0.00022 | $0.00097 |
| Haiku 4.5 | $0.00011 | $0.00049 |
Grade A, and why
compiler-a8399248 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
语法-语句分隔(compiler-a8399248)
When to use
任务「语句分隔」;对照:语法——分号语句分隔(多语句序列)。
克制条款(不适用条件)
条件不满足即不适用(负路由:输入不满足生效条件时返回 None/不执行)
How to execute
语句分隔:按分号拆分多语句(语句序列解析)
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「语法-语句分隔」
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 · 46 lines · 110 tokens per session scan A 77bb3c1e3583
compiler-a8399248 is a skill published in the GitHub repository FuRongJun-1999/dsh-memory (164 stars, last pushed today), licensed MIT. It adds 110 tokens to every session and 485 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.
Other skills, from other repositories
python-code-quality
Code quality checks, linting, formatting, and type checking commands for the Agent Framework Python codebase. Use this when running checks, fixing lint errors, or troubleshooting CI failures.
graphify
Use for any question about a codebase, its architecture, file relationships, or project content — especially when graphify-out/ exists, where the question should be treated as a graphify query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community…
verify-dotnet-samples
How to build, run and verify the .NET sample projects in the Agent Framework repository. Use this when a user wants to verify that the samples still function as expected.
potpie-change-timeline
Use when an agent needs recent or historical change context: what changed recently, regressions, merged PRs, tickets, docs, incidents, deployments, releases, and source-history ingestion.
potpie-project-preferences
Use before writing, modifying, reviewing, refactoring, or testing code so repo/project preferences surface: error handling, file structure, frameworks, logging, dependency choices, testing, security, API style, and naming. Also use after code work when a reusable project preference should be recorded.
install-openviking-memory
Install and configure the OpenViking long-term memory plugin for OpenClaw via natural conversation. Once installed, the plugin automatically captures facts from chats and recalls relevant context before each reply (auto-capture + auto-recall, cross-session). Covers prerequisites, install through OpenClaw's plugin…