플러그인

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.

DeepSeek Harness를 위한 적응형 모델 탐색, 평가, 계층화, 서브에이전트 라우팅.

DeepSeek Harness(DSH)를 위한 Langfuse 관측 기능: 세션 id로 집계되는 턴별 트레이스 트리와 프롬프트 버저닝 및 평가.

동결된 요청 표면 증거를 사용하여 Standard, Minimal, Anchored 또는 플러그인 후보에 대해 실제 DeepSeek Harness 턴을 재생

실제로 로드된 DeepSeek Harness 플러그인을 확인하고 trapstreet.run의 공개 평가 보드를 조회합니다.

DSH용으로 이미 실행 중인 Chrome/Edge에 연결하는 CDP 브리지: HTTP + WebSocket을 통한 스크린샷, 픽셀 검증, DOM/CSS 검사, 페이지 JS 평가를 제공하며, 자식 프로세스 제로, npm 의존성 제로, 사용 시 승인 요청 제로.

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.

메타데이터만 포함한 DSH 턴, 모델, 도구, 승인, 재시도, 오류 이벤트를 Tuning Engines로 내보내 거버넌스가 적용된 추적, 정책 평가, 비용 분석, Work Session 검토에 사용합니다. 디스크 기반 재시도 큐를 포함합니다.

에이전트 평가 플랫폼: 벤치마크 YAML, 헤드리스 실행 오케스트레이션, 추적 기반 지표, 실행 리포트