Plugins

Grades free physical and commit memory and refuses heavy tools (shell, workflow, subagent) before the machine thrashes. A reserve you set on the panel is held back from heavy tools so light calls still have room. Also shows in the composer how much of the machine dsh itself is using, including whole-machine CPU.

Emotion, energy and per-user portrait engine for AI characters: a 303-word emotion library scored in two steps (pick one of 6 categories first, then choose within it), novelty reward and same-word cooldown for diversity, a spirit meter with easter-egg state transitions (excited / suppressed / angry), and per-user portraits. File mode works with any AI; the optional OneBot mode reads friend message history from a OneBot 11 endpoint such as NapCat. Requires your own OpenAI-compatible LLM endpoint and API key (EMOTION_LLM_KEY or emotion/config.json).

Local-first, file-native long-term memory for DSH with per-turn preflight, compact context injection, citations, human-approved writes, manual consolidation, nightly report-only maintenance, and a Web panel that is read-only for knowledge while allowing workspace-config edits.

Three ledgers for a model that loses context between restarts. Self-note: what I have become — capped at 40 entries, and every eviction first leaves a one-line crease recording which entry was dropped and why. Judgment ledger: record a claim together with how to verify it, come back when it is due, and mark it right or wrong; wrong ones stay in a mistakes book so you stop assuming you were always correct. Handoff: where this run stopped and what the next one should read first — a baton, not a memory. Three tools each taking an action parameter instead of a dozen separate ones (the prompt is paid every turn). Data lives in ~/.dsh/*.jsonl and is shared with the same-named CLI scripts. Zero dependencies.

Diary and narrative memory for companion agents: daily first-person entries scoped per group or private chat, refs that can be checked against that day's messages, and a read-only journal page where hand-drawn doodles and photos follow the sentences they belong to.

Failure memory for DeepSeek Harness. Captures tool failures as they happen, classifies them with 16 built-in rules into lessons pairing a symptom with a fix, adds session/daily/weekly summaries, and injects a short briefing into later sessions through systemPrompt context. Ships 5 pitfall_ tools and a web settings page.

A durable RAG-enhanced long-term memory layer for LLM agents, featuring holographic memory, state-slot conflict detection, and timeline retrieval. Requires Python 3.10+ and this repository's Python dependencies: create a venv and run pip install -e ".\[dev]" in the checkout before enabling the plugin (the bundled installer is a PowerShell script, i.e. Windows).

Stores the agent's long-term memory as plain Markdown in an Obsidian vault — every memory is a .md file with YAML frontmatter that Obsidian indexes natively, so you can read, edit, link and back up it with tools you already use. Nothing is locked in a database. Ships a sidebar panel (tag filter, sort by recency or importance, edit in place), 7 agent tools, three-tier token-budgeted recall, BM25 ranking (no embeddings, no API key, works offline, CJK bigram tokenization), importance decay with archiving (moved to .archive/, never deleted), and optional GitHub Contents API sync. A note containing a secret is refused rather than redacted, because a redacted memory reads as complete while the important part is gone. Zero runtime dependencies; everything comes from the DSH host.

Local three-band memory for DeepSeek Harness: a 24h protection band, query recall and scene replay, injected into sessions without embedding or model calls.

Adds a "Delete session" item under "Archive session" in the sidebar session menu: it detaches the session from memory, deletes its on-disk record and projection cache, and removes the row from the sidebar immediately. Permanent - there is no trash bin.

Windows resource monitor for the DeepSeek Harness (dsh) sidebar: samples CPU, memory and clock speed once a minute, keeps a 30-day history and draws separate 24h / 7d charts for CPU and memory (with 85% / 90% alert lines) plus a frequency chart that marks load-time throttling. Reports sustained CPU >= 85% (10 min), memory >= 90% (10 min) and frequency <= 70% of maximum clock under load (5 min).

把你和 AI 的全部对话，渲染成一片星海，可视化的预览

Claude-Code-compatible memory for every model in DeepSeek Harness: layered CLAUDE.md/AGENTS.md instructions with @import and rules, a persistent MEMORY.md memory with full writing guidance, refresh on resume and compaction, optional recall, background ext

Long-term memory about the owner: extracts schedule, likes, dislikes, emotional triggers, current focus, promises and tone preferences from each turn, injects a short digest into every prompt, speaks up on a schedule or slips a caring line into replies, learns wake/work/sleep times from short messages, and compacts the memory automatically, with plugin-source and outbound-tool recognition, unverified-memory down-weighting and one-click retract.

Memory for DeepSeek Harness with five sections (memories, tree, profile, graph, inbox), a rule-based capture policy that decides what to keep and what to drop, and hybrid BM25 plus vector retrieval over parent and child chunks, backed by twelve agent tools that read and write the same local files.

Character-chat memory layer that keeps persona, voice and relationship state separate from summaries, archives every raw message for retrieval, and ships a SillyTavern card importer plus an OpenAI-compatible gateway.

Lingshu startup injector: runs brain.py startup on every new agent session and injects persona / memory / todos automatically. Requires Python 3, and the Lingshu skill installed at ~/.dsh/skills/lingshu; silently does nothing if either is missing. Main repo also ships a bundled SKILL.md entry point.

Resumes a dormant DSH session on demand: when a session is not currently live, it is resumed from disk with the provider/model route it last used, so external integrations (chat bridges, schedulers, webhooks, other plugins) can deliver into it instead of failing with "that session is not alive". Exposes an HTTP endpoint, a wake_session tool and a ctx.wakeUp service. Resuming only reads history back into memory and spends no model request by itself.

Disk-backed persona card, handover note and long-term memory for DSH agents; each new session reloads them, and every memory carries a source tier, stamped revocation and soft archive.

Local-first research toolkit for DSH: registers kb_grep, kb_list, and kb_save model tools. kb_grep searches the markdown knowledge base with ripgrep when present and falls back to grep, while kb_list and kb_save go through the scoped ctx.fs filesystem service; the KB defaults to ~/dsh-kb. The repo also ships two skills (local-first-research and session-memory) that must be copied into a DSH skills directory yourself; they are not installed by the plugin bundle. The plugin is pure Node ESM with no npm runtime dependencies and ships a framework-free node test harness. Installable via plugin_manager install_bundle; no client UI is bundled.

A persona that grows from experience: remembers what happened, becomes someone from it.

DeepSeek Harness plugin: shared architecture source of truth for agent and human, no full-code reading. System Prompt includes only MEMORY.md with links to per-module SPEC.md; JOURNAL.md logs actions separately.

QQ 通道的人物记忆与画像：滚动聊天总结、人物画像、好感度（主人恒定天花板）、情绪层（心情 + 情绪残留），主人可查可改可删

Layered local memory for DeepSeek Harness: a Markdown index auto-injected into the system prompt, a route layer read on demand, and evidence files for detail. Pure files, zero runtime dependencies.