dsh-context-lens
Request Context Profiler for DeepSeek Harness — see what changed between model requests, and how cache reuse changed with it.
gordonlu
@gordonlu
⬇ 1
★ 1
main
安装
dsh plugin --profile web add github:gordonlu/dsh-context-lens
需要可复现安装时,可在仓库后追加 #commit 固定提交。
Request Context Profiler for DeepSeek Harness — see what changed between model requests, and how cache reuse changed with it.
该插件未提供要点说明,请参考仓库 README。
cache-reusedeepseek-harnessdsh-pluginobservabilitypluginprofiling
- 安装并启动 DeepSeek Harness:
npx @deepseek-ai/dsh web - 在终端执行上面的安装命令(CLI 会解析插件并核验来源)
- 用 dsh plugins list 确认已安装,必要时重启 Harness 生效
插件以当前 dsh 进程的权限运行,安装时可能执行代码。请先通读仓库源码与许可证,确认无破坏性命令与越权访问;本站只做索引,不对第三方插件安全性作担保。
| 代码仓库 | github.com/gordonlu/dsh-context-lens |
| 许可证 | MIT |
| 主要语言 | main |
| 下载量 | 1 |
| GitHub 星标 | 1 |
| 最近推送 | 2026-08-14 |
| 收录日期 | 2026-09-19 |
| 分类 | 开发与运行时 |
事实信息来自公开插件目录快照(2026-10-01),介绍文案由本站再加工。
以下为插件仓库 README 全文(原始内容,由公开目录抓取整理)。
[图片: dsh-context-lens]
# dsh-context-lens
Request Context Profiler for DeepSeek Harness — see what changed between model requests, and how cache reuse changed with it.
## What it is
`dsh-context-lens` is a DeepSeek Harness plugin (server unit + client view) that answers one question continuously: **"what did the harness send the model this time, and what changed since the last request?"** It is a pure observer — it reads the session log, adds nothing to it, and never touches a model call.
[图片: dsh-context-lens dashboard]
## Quick start
```sh
dsh plugin --profile web add dsh-context-lens
```
Open any conversation, switch to the **Request Context** tab, and watch every
model request get one line: what changed vs the previous request, and how
cache reuse moved with it.
For every real LLM request it records one compact card:
- **Request identity** — turn:step, provider, model, context window, status (completed / failed / aborted).
- **The committed request context** — canonical fingerprints of the system prompt, the tool set (each tool's schema hash + estimated tokens), the request config, and the tool declaration order. Only state actually committed to a real model request is compared; the harness's mutable state is never observed.
- **Cache reuse readout** — computed strictly from the provider's disjoint usage buckets (uncached input + cache reads + cache writes = billed input). Missing fields stay absent (rendered `-`), never zero.
- **Diff vs the previous request** — model, provider, config, system prompt, tool set (+added/−removed/~modified), tool order, estimated surface delta, and the cache-reuse boundary in percentage points.
- **Drop alarm** — when reuse dropped across the threshold, a ranked list of coincident changes (correlation, never causation) with an explicit disclaimer.
**The view is change-first** (a `conversation.view` slot, zh/en). Opening it answers "is anything wrong, and where?" in one glance:
- a session status strip — ✓ cache stable / ✓ structure stable / analyzed count, flipping to ⚠ alarm counts on anomalies;
- the recent-requests list, newest first (up to 100 retained), one line per request — a session-global ordinal, a change tag (Stable / Cache drop / Tools changed / System changed / +X tok), the cache readout, and a "hide unchanged requests" filter on by default;
- the inspector — cache reuse with its delta, new uncached input, estimated context surface, a line-by-line comparison vs the previous request (system / tools / tool order / config / model / provider), and a green conclusion when nothing is cache-impacting;
- raw usage buckets, header hashes, and the full tool list behind a "technical details" fold.
## Accuracy boundaries
Everything on the left is genuinely observable; nothing on the right is ever claimed.
| Can determine | Cannot determine (and never claims) |
| --- | --- |
| System prompt, tool set, tool schemas, declaration order, request config — as committed to the request | The provider's internal cache key construction |
| Model and provider of each request | The exact token at which prefix reuse breaks (KV-causality) |
| Provider-reported usage buckets (uncached input / cache reads / cache writes / output / reasoning) | Which single change caused a drop — only correlation |
| Reuse ratio and its delta between consecutive requests | Cache state of sessions/requests that left the 100-entry window |
| A heuristic surface estimate (chars/4 + per-block + per-role overhead) | Anything about the harness's in-memory state |
## Architecture
**Server** — one pure, replayable projection (`contextLens`) folds the session log: `request/header` events (epoch-logged, committed only on change) define the snapshot in force at each `step/start`; a header landing inside the step replaces it (that is the header the provider actually saw). `step/end` marks the span closed; finalization happens at `turn/end` for the last step, at the next `step/start` for intermediate steps, and crash-orphaned logs close as failed. Retries do not mint new records (mainline retries inside the same step; the fold also splits cleanly if a future mainline opens a fresh turn). Uninteresting events return the same state reference — the registry's zero-work `Object.is` gate.
**Replay consistency is a tested invariant**: folding the log incrementally (live) and folding the same log from `init` (replay) produce identical state and projection.
**Client** — registers the `context-lens` entry (order 30) in the `conversation.view` slot, reads the projection through the framework's `useProjection('contextLens')` seat, and ships its own zh/en locale namespace. Selection is component-local. No heavy UI dependencies; CSS Modules compiled with lightningcss and injected as one idempotent `
数据来源:公开的 DeepSeek Harness 插件目录与各插件 GitHub 仓库。本站为独立第三方目录,与 DeepSeek、幻方(High-Flyer)及插件作者均无隶属或背书关系。