Plugins

DeepSeek Harness plugin that wraps @stardeckai/lgtm — a Jev-powered linter for tests that pass but prove nothing. Ships the CLI as a dependency, reuses the TypeSafe credential stored in DSH, and exposes lgtm_audit / lgtm_status to the agent.

Read-only side branch in the right sidebar: quote text to ask follow-ups without polluting the model context of the main session; every follow-up rebuilds the branch from the latest settled turns of the main session, and writes are refused at the tool-execution layer so the prompt prefix stays cacheable.

Replaces the DSH sidebar with a fixed 2×4 board of eight standing agent seats; each seat shows its own status, latest tool call, reasoning and result, and a settings switch restores the stock sidebar at any time.

DSH plugin that registers the ai-eight-honors-eight-shames skill: eight checkable working rules for coding agents (verify before calling, confirm ambiguity, cite evidence, reuse before creating, test before claiming done, follow local conventions, separate fact from inference, keep changes reversible) plus a ninth rule that logs each user request and the change made in two fixed report files at the workspace root.

TypeSafe Jev (modelo de decisión System One) para DeepSeek Harness, de principio a fin: jev_decision (choice/score/noul, en paralelo, respuestas tipadas + confianza), una tarjeta en Ajustes > Plugins para la API key y los conmutadores, vistas de herramientas en línea que muestran cada decisión en la conversación (respuesta, confianza, latencia, coste), un autoguard (comprobaciones deterministas + de riesgo/bucle con Jev), un panel local /jev y un benchmark de verificación en línea (96,3% en 27 preguntas etiquetadas, mediana ~283 ms en jev-latest, 2026-09-21). Honesto por diseño: solo API real, sin fallback simulado.

Ren'Py galgame production workbench: a host-side project service (write gateway plus git snapshots, a dialogue/scene dialect parser, derived progress, human-only review stamps, pinned-SDK validation and playtest, local publish) behind one seam, driven by an embedded web panel and agent tools.

DSH Plugin DevKit: runtime observation kernel, isolated debug boot, host contract tests, slot preview

Model channel manager: several provider/model candidates share one virtual route (roundrobin/<group>) with failover, retry, timeout and cooldown; a settings tab edits providers and groups, runs per-model speed tests and real single-model request tests, and a seven-day health panel aggregates latency, failures and upstream token usage (input, output, cache and reasoning).

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.

Combines fault knowledge-base symptom-to-cause rules with similar past cases to rank candidate motor diagnoses with evidence terms and suggested extra tests.

Fullscreen business-app framework for DeepSeek Harness Web with a dark-sidebar workbench bundling a database connectivity tester and SQL query panel, a two-level knowledge base (bill and accounting) with local search and Word document ingestion, metadata environment comparison with alter-SQL generation, a data dictionary, agent log-search report demos, a system test-case runner, Evernote-style notes, developer utilities, and mini-games.

Scans the whole C drive and classifies every large folder into A/B/C/D/E/P (safe cache / relocatable / junction-movable / abandoned software / config-memory redline / personal files), then deletes, moves, or redirects on demand — with strong redline protection, dry-run preview and undo. Unknown software decided by LLM fallback + human confirmation. Works on DeepSeek Harness 0.1.7 through 0.2.x, tested on 0.2.0-rc.1.

DeepSeek Harness (Cordis) plugin — bi-temporal VSA memory + cognition engine + BLAKE3 provenance, exposed as a DSH storage+session plugin. End-to-end tested with a real Python subprocess.

Conversation-level multi-agent workbench for editable task planning, real child-session observation, plan-versus-runtime comparison, and evidence-backed recovery previews; tested with DSH 0.1.1-rc.2.

Runs a parallel code review squad of read-only reviewer subagents (security, correctness, tests, style), each optionally on its own model, and aggregates one severity-grouped report from /review or a code_review tool call.

Uses a GitHub App to report open PRs and the latest Actions run for the current repo without returning secrets.

Parses DSH session logs to attribute AI-written files, overlays c8 coverage, and reports AI vs human code coverage with a trust score, plus a check/fix/verify tool loop for closing test gaps.

Stavros RedTeam for DeepSeek Harness (DSH) — authorized red-team persona + scope-guarded pentest tooling (persona, 24 subagents, methodology and 78 zero-dependency guard tools).

Adds a /dshpp slash command that reports session and token usage, cache-hit rate, and estimated cost from harness session logs; the repo also ships a CLI and local web console for lifecycle, provider, session, and plugin-security management.

Activa flujos de trabajo de GitHub Actions y canalizaciones de pruebas locales, transmite sus registros de vuelta y, en caso de fallo, entrega la cola del registro al agente para su diagnóstico.

Plugin de DeepSeek Harness (dsh) para @tabilo/ai-code-review: registra la herramienta ai_code_review (review | context) que reutiliza el motor de Node.js para recopilar diffs, ejecutar pruebas/cobertura, realizar análisis de impacto y alimentar al modelo con skills del paquete.

Pruebas de carga con Grafana k6: primero una smoke test, luego ejecuta scripts y lee los percentiles desde el resumen JSON.

Operaciones SSH mejoradas: prueba de conectividad, ls remoto, importación de ssh-config, verificaciones de huellas en known_hosts y ejecución en múltiples hosts.

Herramientas de humano en el bucle (`human_task` / `human_task_ready_check`): pausan el agente hasta que el usuario realice una acción en el mundo real o devuelva una observación (operación de GUI, prueba de juego, verificación visual, comprobaciones de hardware), sujetas al consentimiento de la sesión y a una comprobación de presencia AFK, devolviendo un resultado JSON estructurado a través de un diálogo de tarea web.