Plugins

Drives a Chromium browser you are already running — Chrome, Edge, Brave, Vivaldi, Opera, Chromium, Arc or Thorium — over one long-lived CDP connection, so the remote-debugging approval is asked once instead of per command. Commands for tabs, navigation, JS evaluation, DOM snapshots, clicks, typing, screenshots and CSS/DOM/pixel assertions go over a loopback HTTP route that the agent calls with the shell tool it already has, so no new tools are registered, and the plugin registers its own usage skill with no runtime dependencies.

Compares existing DSH sessions or runs controlled A/B trials between agent presets, showing aligned timelines, paired metric deltas, explicit success checks, and exportable reports.

Adaptive model discovery, evaluation, tiering, and subagent routing for DeepSeek Harness.

Langfuse observability for DeepSeek Harness (DSH): per-turn trace trees aggregated by session id, with prompt versioning and evaluation.

Replay real DeepSeek Harness turns against Standard, Minimal, Anchored, or plugin candidates with frozen request-surface evidence

Check which DeepSeek Harness plugins actually loaded, and look up public evaluation boards on trapstreet.run

CDP bridge to an already-running Chrome/Edge for DSH: screenshots, pixel assertions, DOM/CSS checks, and page JS evaluation over HTTP + WebSocket, with zero child processes, zero npm dependencies, and zero per-use approval.

Registers one `valuation_service` tool that reads a workpaper tree of 01/02/04 JSON and returns one unified schema: six ratio groups, forecast ratios, DCF input slots with explicit missing markers, comparable PS/PE and IRR. Deterministic and local: missing WACC, growth, capex, D&A or working-capital inputs stay blank instead of being guessed, and assumption differences are flagged for review.

Checks a bid evaluation scoring matrix for internal arithmetic consistency: that each factor's maximum, the scores recorded and the total agree with each other. Reports the differences it finds; it does not judge whether the evaluation itself was correct or fair.

Cross-session memory for your dsh agent: remembered, recalled, and re-verified instead of assumed.

DSH 试金石：给「自改造」补上评测这一环——用一套金标准用例，把改动前 vs 改动后跑一遍、打分、对比，好就留、不好就撤。

Retrospective agent-session evaluation: deterministic grade cards and cross-session regression diffs from persisted logs — no benchmark runs, no LLM judges.

Measure whether a dsh setup change actually helped: register repeatable cases, run them, and diff the results before and after you change rules, skills, or models.

DSH plugin for bounded agent evaluation: compare an original with candidate prompt or supported configuration changes against user-supplied checks, record evidence and costs, and leave adoption to the user.

DSH web plugin: a LiveBench tab in the Trajectory view (right of 对话/轨迹). Run LiveBench evaluations against every model configured in the DeepSeek Harness — pick provider/model, category, task, release and question range from dropdowns, watch progress, and

Paired experiments and promotion gates for DSH plugins.

Runs one coding prompt across multiple configured models in isolated Git worktrees, streams side-by-side tool traces and results, and commits the candidate the user adopts for later rounds.

Evidence-first multi-model coding comparison workbench for DeepSeek Harness

Audit an agent harness against the harness-evaluation criteria, with machine-enforced evidence validation.

Exports metadata-only DSH turn, model, tool, approval, retry, and error events to Tuning Engines for governed traces, policy evaluation, cost analysis, and Work Session review, with a disk-backed retry queue.

Agent evaluation platform: benchmark YAML, headless run orchestration, trace-based metrics, and run reports