Плагины

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

Plays a chime when DeepSeek Harness asks for approval; tone, volume and on/off are set in Settings → Notifications.

Skill center for DSH: browse skills by region, install them from a zip archive, and manage them afterwards with enable/disable, uninstall, trash restore and search.

A DeepSeek Harness bundle of the 25 promoted mattpocock/skills with a native agent preset, an invocation-aware skill provider, and a light grilling-to-implement bootstrap.

Resolves the original local path of files uploaded to .dsh-uploads by matching file name, size, and sha256.

Indexes the current workspace into a relation graph and exposes one agent tool, codegraph_explore, for reachability, callers, and impact, plus a Web sidebar graph pane.

Figma design context for the agent: reads a frame's node tree, auto-layout, paints and typography, resolves the variables it is bound to, and renders a screenshot to compare code against — plus four bundled design-to-code skills. Connect with one button against Figma's own consent page; the plugin ships its own OAuth app, refreshes tokens automatically, and never asks for or displays a token.

Appends actionable hints to failed tool results, such as the nearest existing directory when a path does not exist.

Hard stop for runaway turns: blocks repeated identical tool calls and ends the turn when per-turn step or time budgets are exceeded.

Local relationship-memory workbench with a confirmation gate: every AI write — extracted memories and newly created contacts alike — lands in a pending queue until the user confirms it in the GUI; materials are archived verbatim with quote-level provenance checks on AI extraction, and confirmed memories aggregate into per-contact timelines, gift occasions and reciprocity views. Ships a native relationship agent preset for conversation capture; JSON storage by default with an optional SQLite backend, zero production dependencies.

Model-autonomous context compaction for DeepSeek Harness: a mind_map tool surveys the agent context surface (stable seq ids, roles, token weights, clear-boundary markers, the clear-mind playbook), a clear_mind tool replaces a chosen conversation-history span with a model-written checkpoint through the native compaction transaction, call and result pairs self-collapse into one-line tombstones, and a proactive reminder nudges the model when the context grows long or a turn runs too many steps.

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).

Create and configure Agent presets from a single settings section — persona prompt, a checked knowledge-base entry list injected into the system prompt, individual switches for four knowledge-base tools, per-Agent narrowing of MCP connections, and batch knowledge-base import.

Model switching and reasoning-effort control for DeepSeek Harness: set_model and list_models agent tools let the agent autonomously change its provider, model or thinking depth (reasoning effort) mid-task with a mandatory reason, a provider whitelist and a context-window token-pressure guard, the active model is injected into the runtime context, and Plan Mode auto-transition switches to a configured planning model on entering plan mode and restores the previous execution model on exit.

Registers the Xunfei Loomy cloud channel as an OpenAI-compatible supplier in dsh-router, adding model listing and streaming chat completions.

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.

Replaces the chat input model picker with a provider-grouped, collapsible list: groups models by provider, folds vision-bridge mirror providers back into their upstream, and carries the reasoning-effort options each model declares in settings. Tested on DSH Desktop only; the DSH web build is untested.

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.