Plugins

Schedules Akasha sleep distillation on idle or timer, and injects a wake note for new sessions plus a per-turn memory pulse; the optional idle-resume behaviour is off by default. Companions: Hindsight (repo memory), OpenViking (user memory), and the built-in session search (see repo README). Setup: clone the repo, copy core/ aside, then replace <AKASHA_DIR> in cordis.patch.yml with its absolute path (or set the AKASHA_DIR env var); plugins fail loudly while the placeholder remains.

Records host events — turn ends, tool results, agent errors, idle transitions and assistant output — as append-only JSONL for Akasha metrics, recall checks and output audit. Companions: Hindsight (repo memory), OpenViking (user memory), and the built-in session search (see repo README). Setup: clone the repo, copy core/ aside, then replace <AKASHA_DIR> in cordis.patch.yml with its absolute path (or set the AKASHA_DIR env var); plugins fail loudly while the placeholder remains.

Write guard for the Akasha Book store: blocks tool calls that write into the memory data directory, and injects the store usage contract and self-layer sections into the system prompt. Companions: Hindsight (repo memory), OpenViking (user memory), and the built-in session search (see repo README). Setup: clone the repo, copy core/ aside, then replace <AKASHA_DIR> in cordis.patch.yml with its absolute path (or set the AKASHA_DIR env var); plugins fail loudly while the placeholder remains.

Chunked compaction for oversized sessions: takes over only when the live context exceeds 85% of the model window (or the request exceeds 4 MB), summarizes the history in 1.5 MB chunks and merges them; smaller sessions keep the built-in compaction.

Automatic, versioned backup of your DeepSeek Harness home — sessions, memories, settings and the profile plugin tree — to a private git remote you own, with commits and pushes on a schedule and every failure surfaced in the DSH log.

A DSH conversation workspace: turn actions, provenance quotes, local notes and summaries, in-conversation find, focus lock, and per-workspace command buttons.

Context observability and control for DeepSeek Harness: cross-session token analytics, session-level budget/prompt/tool controls, and optional agent scaffolds. It reuses the official tokenMeter, compaction, systemPrompt, and tools.restrict seams; monitoring is read-only.

Deletes a whole conversation from the sidebar session menu, and opens a right-click menu on a chat text selection with Copy, Quote (insert a Markdown quote block into the composer) and Ask: Ask sends the selection plus your question to one reused continuable subagent (label `ask`, found again through the subagent catalog after a restart) and streams the answer into the right sidebar.

Opens any session as a second full chat tab in the right sidebar, with its own transcript and composer, while the main conversation stays put; a split right pane allows up to three concurrent conversations.

Live context-pressure meter in the composer dock. When usage nears the limit it asks the current model for a handoff brief, starts that brief in a fresh session and switches you there, so long work can continue without hitting the window.

Delete a DSH conversation from its sidebar menu or the conversation header, optionally cascading into that conversation's associated memory nodes when you tick the box (the confirmation dialog shows the hit count before you commit); deletes land in a plugin recycle bin with restore, every action is appended to an audit log, and batch deletes are rate-limited with per-conversation rollback.

Indexes session transcripts into the Akasha session layer as turns end, records successful compactions, and adds a per-turn rhythm line to the runtime context. Companions: Hindsight (repo memory), OpenViking (user memory), and the built-in session search (see repo README). Setup: clone the repo, copy core/ aside, then replace <AKASHA_DIR> in cordis.patch.yml with its absolute path (or set the AKASHA_DIR env var); plugins fail loudly while the placeholder remains.

DeepSeek Harness plugin: restart without losing work. It waits until every conversation is idle, restarts the host, and picks the interrupted work back up instead of dying with it. Every restart is preceded by a snapshot of the configuration and the installed plugins; if the host does not come up, a one-shot watchdog restores that snapshot, starts the host once more, and records the outcome. Where a supervisor already manages the host it asks that supervisor instead of starting a second one. When the work is done it counts down and powers the machine off. It works on the Windows desktop client and on a hosted dsh web instance (Windows, WSL, Linux).

Registers /compact_queue, a compaction command that does not drop the request when the session is busy: it compacts immediately when idle, otherwise queues the request in that session and runs it at the session's next idle window, with a 2-second fallback tick beside the agent status event so the trigger is not missed, a 30-minute wait cap, and /compact_queue cancel to withdraw a queued request or abort a running one. Each session keeps its own queue state from a single command registration, completion and failure arrive as a collapsed notice that does not wake the model, and every ManualCompactionError code except busy is terminal so a failure is not retried. Host half only; the core /compact command is left untouched.

Read-only lazy session viewer: lists sessions with stat only, then decompresses just the tail Zstandard frames to inspect recent events; includes search, stats, and export in a standalone /lazyview panel. Requires Node.js 24+.

A human-in-the-loop curation workbench inside a DSH session: mark whole turns or single messages to keep verbatim or to summarize, rewrite user, assistant, tool-result and injected-context messages in place without a model call, then compress the marked ranges with model-written summaries or the official /compact. A local JSON backup is written before every write to the session.

Persona manager for DeepSeek Harness — a JSON-backed persona library, a per-session binding so each session can run a different persona, an optional default persona for sessions that have no binding, and switching that applies either at once or from the next session; the Web UI adds a Model personas settings page and a persona chip next to the composer, and three model tools let the model list, switch and create personas itself.

Gives the model a private glossary: reads 「滚木」 as "none" and 「棍母」 as "doesn't exist", and decides on its own whether to phrase a sentence with those words when expressing that meaning. Registers one system-prompt section and rewrites nothing; word lists ship as per-category YAML (moba/fps/comments/ai/dev/private) with a settings page to toggle files, change reminder strength and open the glossary folder.

Describe the persona you want and the agent writes it: nickname, bio, taglines and links come from the host model, the avatar comes from your local asset library (or a path you give), then it fills your profile page and hands the picture to the site's own uploader. Dry-run by default - it only touches profile fields, never passwords or security settings.

Stops compaction summaries and session titles from burning their output budget on reasoning, by lowering the thinking level and raising the token floor for auxiliary calls only.

Use your ChatGPT account to bring Codex models into DSH: choose models and thinking effort, chat with images, use tools and skills, approve local tasks, answer interactive questions, continue and compact long conversations, check quota and Token history, and maintain a supported Windows screenshot helper.

Answers model calls by driving your own logged-in chatgpt.com session in a local Chrome, so inference spends the ChatGPT web quota rather than Codex quota, and adds a JSON tool-call protocol so the web model can run DSH tools.

Bridges the third-party providers saved in a local cc-switch app into DeepSeek Harness: Anthropic-format tool groups become adapter routes, Codex groups are handed to the built-in pi-ai adapter, and a settings page picks which groups to import and in which order.

Exposes every model of every provider configured in DeepSeek Harness through one OpenAI-compatible endpoint on a fixed loopback port, with a sidebar page for the base URL, key, port and model ids.