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 (System-One-Entscheidungsmodell) für DeepSeek Harness, durchgängig: jev_decision (Choice/Score/Noul, parallel, typisierte Antworten + Konfidenz), eine Karte Einstellungen > Plugins für API-Key und Schalter, Inline-Tool-Ansichten, die jede Entscheidung in der Konversation zeigen (Antwort, Konfidenz, Latenz, Kosten), ein Auto-Guard (deterministische + Jev-Risiko-/Loop-Prüfungen), ein lokales /jev-Dashboard und ein Online-Verifikationsbenchmark (96,3 % bei 27 gelabelten Fragen, Median ~283 ms auf jev-latest, 2026-09-21). Ehrlich ausgelegt: nur echte API, kein Mock-Fallback.

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.

Löst GitHub Actions-Workflows und lokale Testpipelines aus, streamt deren Protokolle zurück und übergibt bei einem Fehler das Ende des Protokolls an den Agenten zur Diagnose.

DeepSeek-Harness- (dsh-) Plugin für @tabilo/ai-code-review: registriert das ai_code_review-Tool (review | context), das die Node.js-Engine wiederverwendet, um Diffs zu sammeln, Tests/Coverage auszuführen, Impact-Analyse durchzuführen und Pack-Skills dem Modell zuzuführen.

Lasttests mit Grafana k6: zuerst Smoke-Test, dann Skripte ausführen und Perzentile aus der JSON-Zusammenfassung lesen.

Erweiterte SSH-Operationen: Konnektivitätstest, entferntes ls, Import von ssh-config, Fingerprint-Prüfungen der known_hosts und Ausführung auf mehreren Hosts.

Human-in-the-Loop-Tools (`human_task` / `human_task_ready_check`): pausieren den Agenten, bis der Nutzer eine reale Aktion ausführt oder eine Beobachtung zurückgibt (GUI-Operation, Spieltests, visuelle Verifikation, Hardware-Prüfungen), gesteuert über die Zustimmung der Session und eine AFK-Anwesenheitsprüfung, und geben ein strukturiertes JSON-Ergebnis über einen Web-Task-Dialog zurück.