Plugins

Persistent personal todos shared across DeepSeek Harness sessions and projects: SQLite storage, Agent tools, a native sidebar and turn-tail cards, with user-managed workflows or optional dispatch of individual todos to Agent sessions.

Make the AI remember how you think, not what you said: actively captures your principles, redlines, and working habits across sessions — with memory capacity, graceful decay, and human-readable reasoning for every entry. 0.7.0 brings an intuitive persona portrait with evidence tracing.

Session cleanup for the DSH sidebar: drag a conversation row onto the balance widget's whale and it eats the row — the session directory goes to a recycle bin, not to rm. Deletion asks one short question first (how many tokens it burned, and that prompt can be turned off in Settings). Undo from the persistent toast or the 12-entry log in Settings. Clicking the whale jumps straight to the plugin's Settings, where you can swap the avatar, drag the mouth into place and rewrite all 17 UI strings; blank, currently-open and running sessions are refused in place with a reason.

Sidebar card that prices this session's tokens in RMB at official peak/off-peak rates and splits context share by source, with per-turn pricing and a Markdown/CSV history report.

Live progress for long-running background jobs in the DSH web GUI: a draggable floating ball in the session that owns the job, expanding into a panel with done/total, speed and ETA.

Power button in the DSH Web UI session header that lets you close or restart the whole DSH process.

Manual model probe for DeepSeek Harness: a session-view tab where you pick provider, API type, model and route, then send one real raw HTTP request over openai-completions, openai-responses or anthropic-messages — streaming or not — and read latency, TTFT, HTTP status, token usage and a three-layer diagnosis chain (API request / model response / strict check) on one screen, with errors in plain language.

In-page task notifications with collapsible status toasts, sound, and session navigation.

Model-callable compact_agents tool that force-compacts context in every live session (main session, ordinary sub-agents and AgentTeams members alike), queues busy targets and compacts them when the turn ends, reports the shadowed node count and estimated tokens per target, and ships a browser form for the trigger ratio, retained ratio, controlled-phase output budget, auto-continue budget and an end-of-turn pre-compaction ratio that compacts near the trigger line once a turn ends, so a reply no longer opens with a wait for the summarizer.

Session management for DSH: archive, restore and delete persisted sessions, cap how many sessions each workspace keeps, and review disk usage in a size governance panel.

Session health check for DSH, read-only: scans the whole session library (totals, largest sessions, warnings for oversized sessions and event-count blowups) and deep-inspects a single session log (tail-balance / crash-tail check, tool-call/result pairing, event-type distribution).

Route each WeChat group session to a different Agent preset by group name, reusing dsh-wechat-assistant without changing its code.

Model-driven context compaction for DeepSeek Harness. The agent decides when and what to compress, commits durable session-log replacements, and ships reversible tool-result compression with byte-exact hash retrieval.

Rotate the outbound LLM session ID after each successful DSH compaction without creating a new DSH session.

Cross-version session migration for DSH: sweeps legacy session stores (v0 plaintext, .zstd and .zip exports), re-pins the header cwd to the target workspace and drops sessions in so the persistence layer upgrades them on open; settings sidebar with env/import/list/convert tabs plus a CLI (check / fix / import).

Shows the DeepSeek account balance as a row in the sidebar footer with a refresh button, and opens a popover with the balance card, daily token and estimated-cost charts over a 7/30-day window, a per-model table, and a link to the platform usage page. The balance comes from the official /user/balance endpoint; usage is aggregated from local DSH session logs and priced with the published peak/off-peak rate card in CNY, so it is labelled an estimate and its scope is stated on the panel.

Shows token and CNY cost per turn and per session in the harness's own statistics form, a whole-account bill over 1/7/30-day windows with cost-per-unit-of-work trends, and an advisor that re-measures its own suggestions after you adopt them.

Prompt scene picker for DSH: choose a scene from a dropdown and it opens a new session with the prompt already filled in (with {variable} placeholders), so you only tweak and send.

A full-page console panel opened from the sidebar, with eight tabs: an overview; a session list read from the sessions service (title, directory, running state, updated time, with filters); installed bundles and Loader entries from pluginInventory and pluginManager, with per-bundle enable switches; a plugin market listing public topic:dsh-plugin repositories from the GitHub search API; a model and provider editor that writes the llm-pi-ai settings namespace through remote.settings.mutate (switch a session model, add or edit providers and their model lists, and store API keys via remote.credentials, merging over the stored profile so fields the form does not expose survive an edit); MCP and skills tabs; and a usage tab that reads the host's loopback /api/dsh-usage/overview document. Services are declared through ctx.inject, so a deployment that omits one mounts without the console rather than crashing.

Diagnose and repair DSH sessions that stopped opening after a harness upgrade — from the CLI, from an agent tool, or from a dashboard in the DSH web UI.

Cross-session memory for your dsh agent: remembered, recalled, and re-verified instead of assumed.

One-click Approve & escalate on the approval card: answers the pending request and switches the live session to a permission preset.

Audits a DSH profile for capabilities nothing consumes: inventories the declared composition rows, counts how often each registered tool and skill is actually called in the session logs, separates calls that were made from calls that returned a result, and reports the ones with no observed consumer. Ships a skill that fixes the evidence-chain format for completion claims.

WeChat bridge for DeepSeek Harness over Tencent's iLink bot channel: one session per contact, inbound text/images/files, and WeChat's native typing indicator while a turn runs.