sid-code CLAUDE.md

A set of instructions for sid-code, a coding agent designed for company development environments. It describes how the agent can connect to internal tools, be changed by its users, keep data local, and measure its own performance.

In plain words
What is it for?
It helps define company integrations, customization rules, local data handling, performance measurements, and release checks for sid-code.
Why use it?
It gives a team a shared basis for adapting and reviewing a coding agent while keeping code, conversations, costs, and usage records under company control.

Instructions file

Install

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.

agentmods
npx agentmods add instructions/rushengzhou/sid-code/claude-md
Clone the repo
git clone --depth 1 https://github.com/rushengzhou/sid-code
Per session 9,710 This file is loaded in full into every session.
When invoked 9,710 The same file — it is already loaded in full.
Security scan A 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.09710 $0.09710
Opus 5 $0.04855 $0.04855
Sonnet 5 $0.01942 $0.01942
Haiku 4.5 $0.00971 $0.00971

Measured yesterday against content hash c5c7f1cd07f6, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

sid-code CLAUDE.md scanned grade A with 1 finding 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -fsSL https://www.sid-code.cc/releases/sid-code/install.sh | SID_CODE_CHANNEL=beta bash
CLAUDE.md · 414 lines

How it starts

The opening of the file, as written. The whole thing — 414 lines — stays where its author put it; the contents beside it link to each section on GitHub.

sid-code — 长在企业研发环境里的 coding agent

北极星:长期宗旨与方向(最高纲领,长期不变)

一句话宗旨:别人给你一个 agent;sid-code 给你一个你能改、能量、能审、数据不出门的 agent 底座——然后用你自己的轨迹数据,让它每个版本都更快、更省、更少返工。

四大特性(能演示)

  1. 企业级 —— 长在企业研发环境里:内部网关(含计费取价口径)、内网 GitLab、企业 SSO、MCP、团队默认配置分发、企业 policy、缺陷系统闭环。用户收益是「装上就接得上你公司已有的那套东西,不用先改造企业来适配工具」。
  2. 可定制 —— harness 整套可改,模型任你换。三层:配置层(多 provider 三族协议、降级链、权限规则、团队默认值,改一行配置)/ 扩展层(Hook、Skill、子代理、MCP、插件多种源,写一个文件)/ 源码层(工具、上下文工程、主循环全部开源可改,提一个 PR)。
  3. 数据主权 —— 代码、对话、轨迹、评测、成本账本全部本地,不进任何人的训练集,数据在自己手上,才谈得上用它做优化。
  4. 可观测 —— 每一轮的耗时、成本、决策都留有轨迹,发布前能跑评测知道有没有退步。它不是技术细节,是四大方向的载体:没有轨迹就画不出那四条曲线。

四大方向(能复算 · 按 release 出一条曲线)

每个方向都是一个主口径(那条曲线)+ 一组辅助口径(曲线动了要能归因到哪一层)。 只有主口径进 release 曲线,辅助口径是排查时的分解项——但辅助口径缺失时主口径动了也说不清为什么, 所以两者都列。三档标注按事实分:✅ 轨迹里已有(可直接复算)/ ⚠️ 有采集但口径要小心 / ❌ 尚未采集(想用先补埋点,别拿它当结论)。

四条跨方向通用铁律(都是踩出来的):

  1. 一律看 p95/p99,均值会骗人。慢尾巴才是用户流失点。
  2. 每个指标必须能指到源字段。说不出取数源的数字就是自我感觉,见自检第 2 问。
  3. 分母比分子重要。「命中率」「成功率」「触发率」的分母口径一变,曲线就整体平移——分母必须和指标一起写死。
  4. 区分 stock 与 flow。末次快照值(如 total_tokens_sent)除以累加值(如 total_cost_usd)得到的是错数, 要用累积字段(total_cumulative_prompt_tokens)。
更快 —— 延迟类
指标 状态 / 取数源
主口径 TTFT p50/p95/p99 + 端到端耗时(用户回车 → 最终答复) StreamPhase(first_content).ttft_ms唯一干净源
归因:卡在哪一段 TTFB(首字节,拆「网关握手」vs「模型 prefill」) ⚠️ headers_received.ttfb_ms:协议层同口径,语义受网关缓冲策略影响,跨路由不可比——必须按 model 分组看(latencyByModel
纯生成耗时 gen p50/p95/p99(单次 fetch,不含重试) RetryTelemetry(stream_completed).elapsedMs
整轮 API 耗时(含握手 + 生成 + 重试) ⚠️ AfterModelRaw.elapsed_ms别与 TTFB 混,渲染必须标「整轮」
工具执行耗时 —— 回答「慢在模型还是慢在工具」 PostToolUse.duration_ms / total_tool_duration_ms
体感 tokens/sec(输出流速,比 TTFT 更贴「生成快慢」体感) output_tokens_per_sec
TTFT 按缓存命中分桶 —— 缓存到底让首字快了多少的唯一对照口径 ttftByCache.hit/miss
缺口 TPOT / ITL(token 间隔,决定打字流畅感)、Goodput(满足 SLO 的有效吞吐) ❌ 全仓零命中;goodput 还需先定义 SLO

TTFT 的口径铁律(这条有 P0 bug 教训):必须是首个任意内容 chunk(含 thinking / tool_use), 且每次 fetch 单独计、不跨重试累计。只在可视文本上计 → 对 thinking 模型和纯工具调用轮系统性虚高数十秒。

Read the full file on GitHub · 414 lines

Changes

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.

  1. yesterday First seen · 414 lines · 9,710 tokens per session scan A c5c7f1cd07f6

Subscribe to this mod's changes

sid-code CLAUDE.md is an instructions file published in the GitHub repository rushengzhou/sid-code (2 stars, last pushed 2d ago), licensed MIT. It adds 9,710 tokens to every session, about $0.0485 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.