Plugins

Evidence-first attention supervision for DeepSeek Harness with C0–C3 policy, deduplicated Inbox alerts, quiet hours, and redacted outcome records.

Project memory for dsh with native task-system integration: session todos and file reads auto-condense into cross-session task records (task↔file links), so workflows are switchable and resumable without re-contextualizing. Read-time memory (no upfront scan): extracts code symbols, summarizes docs, and cross-links docs↔symbols; optional TS semantic enhancement (auto on when TS installed). Experience notes auto-dedup. Every recall includes a traceable citation. Pure JS, single dep, works out of the box, silent operation, zero overhead.

Runs isolated install-and-smoke test drives for DSH plugins in throwaway profiles, returning structured pass/fail records and batch matrices without touching your real profile.

DSH sidebar file-activity tab: records file reads/creates/modifies by the agent and the sidebar (including file ops hidden inside bash commands), per-session isolation, survives restarts, click a file for a floating preview (npm: `dsh-file-activity`).

Records every agent write, edit and shell `rm` into a shadow git repository, then reviews them hunk by hunk in VS Code — accept or revert each one. Needs the companion dsh-review-vscode extension; the panel only appears inside VS Code's webview.

Connects DeepSeek Harness to Busabase: search workspace knowledge, work with structured records and apps, submit writes as reviewable ChangeRequests, and inspect results in live cards.

Adds local PDF reading, a durable paper library, traceable evidence records, and cross-paper comparison and synthesis tools.

Manage conversation records from the DSH web GUI settings page: delete a whole session (archive plus physical log removal) or a single message (deleting a user message cascades to its assistant replies and tool results). Running and current sessions are protected.

Technical-preview local graph memory for DSH and Codex: recalls owner-global and current-project evidence before each turn, writes USER and ASSISTANT records separately, and preserves project, session, actor, and source provenance for a visual atlas and cited knowledge pages.

Technical-preview local graph memory for DSH and Codex: recalls owner-global and current-project evidence before each turn, writes USER and ASSISTANT records separately, and preserves project, session, actor, and source provenance for a visual atlas and cited knowledge pages.

SSH terminal panel and AI connection manager for DeepSeek Harness: model-driven SSH connections (connect/exec/list/status/disconnect), auto-saved connection records, keep-alive + auto-reconnect, execution mutex, credential-safe auth, and an XShell-style m

Shows DeepSeek balance and estimated daily spend, merges external vision-call JSONL records, and adds a per-session dashboard with model statistics, TTFT, cache rate, call logs, cost estimates, filters, and CSV export.

Feishu (Lark) integration for DeepSeek Harness: 25 tools for chat messages and history, cloud documents (including knowledge-base pages, generated charts and images), bitable records, spreadsheet ranges and drive permissions, using your own enterprise self-built Feishu app.

Context manager for DeepSeek Harness: auto-summarizes every turn into priority-sorted records, injects them plus pinned cross-session notes and custom text into each model request, and folds any-size real conversation ranges into summaries to save tokens. Persistent and restart-safe. NOTE: manual multi-step install — copy the three package folders into profiles/node_modules and add the provided cordis.patch.yml rows, then restart DSH; not a standard one-command install.

Electrical and electronics calculation plugin for DeepSeek Harness: every computation runs through dedicated tools on self-describing complex value objects in SI base units, with an isolated calculation agent preset and a records panel that archives every solve on disk.

Keeps a local task-outcome ledger for DSH, verifies acceptance criteria from deterministic evidence, records user disposition and token usage, and supports digest-bound export.

Smart form skill: structured record-keeping for bills, ledgers and logs with auto-categorization and periodic reports.

Let a local DeepSeek Harness Agent use BailingHub to query data, update records, and run permitted actions in an online store, SaaS, or other admin system.

Self-maintaining handoff memory per working directory and git branch: records turns, folds them into concise Markdown, and injects the result into future sessions from ~/.agent/agent-handoff, byte-compatible with pi-handoff.

Keeps a project's documentation in step with its decisions on DeepSeek Harness: the same append-only decision log and derived design snapshot, kept fresh in-process instead of by a hook process.

On-demand skill tools for DeepSeek Harness: registers skill_search / skill_load / skill_ref so the model searches a staged skill library first and loads a skill body only when it needs it, keeping the library out of the resident session catalog and out of every turn's context; adds a Skills tab to the conversation view ring that reads back the whole session and lists which skills were actually loaded, and in which turn. Zero dependencies, and the records render client-side only, never entering the model's context.

Workspace checkpoints per tool call: records every file mutation inside the workspace (excluding .git and node_modules by default), including changes made by shell commands rather than by write/edit, and stores the previous content of each changed path in a content-addressed store kept outside the workspace. Restores have two modes — undo one record, or return to a point in time — with a line-level diff preview, a dry run by default, and a rescue record written before every restore so the restore itself can be undone.

One settings page that lists every subscription sign-in the harness can offer and walks you through it — ChatGPT (Codex), Claude, GitHub Copilot, OpenRouter, Kimi and xAI — reading the list from the authorization seam instead of hardcoding providers, and showing credential records no registered flow claims any more.

Go (weiqi) review coach: parses SGF records, backfills winrate and score analysis with a bundled KataGo engine, explains the costly moves in the chat, and writes the commentary back into an SGF copy; a web panel shows the board with problem moves, AI choices and variations.