Plugins

Connects DeepSeek Harness to Postel over MCP: search a library of X posts that went viral by topic, write posts and X threads in the user's saved voice, and save, schedule or publish them to X, LinkedIn, Bluesky and Mastodon; adds a postel-writing skill. Research works without an API key.

LibTV canvas and media tools for DeepSeek Harness, with three director skills bundled and one-time WeChat QR authorization.

Per-turn tool-call step budget for Agent Teams teammates, shipped together with the team-dev and team-boot skills.

Adds an ARIS agent preset that loads the 84 ARIS research-workflow skills and runs their reviewer as an in-harness subagent from a different model family than the session model. Other presets are unchanged. A fork of wanshuiyin/Auto-claude-code-research-in-sleep's dsh-aris branch: install it instead of the upstream dsh-aris plugin, not alongside it - both register the mcp__codex__codex reviewer tools and the same ARIS skills.

Measures one university department's research output: matches author affiliation strings against a college's known name variants (detecting same-named departments at other universities), backfills papers that only name the university by faculty author IDs, classifies each paper by journal tier (CAS partition with Top flag, JCR quartile, CCF, Chinese core via a CNKI export merger), splits staff from student first authors, and writes a workbook with a dashboard sheet, a filterable paper list, a teacher-by-direction matrix and per-teacher statistics. Student authorship is inferred from affiliation and staff lists, not verified against enrolment records. Requires Python 3.9+ with pandas and openpyxl (pip install -r requirements.txt); papers come from the OpenAlex API, whose free per-IP quota is limited, and Chinese-journal coverage needs a manual CNKI export.

Replaces the full skill catalog with keyword-based discovery, per-skill enable/disable switches, and system-prompt section controls. Disabled skills are refused by the skill tool, and their slash-command invocations are blocked at pre-step.

DeepSeek Harness sidebar panel for TeamAI skills, MCP, and prompts.

DeepSeek Harness sidebar for LiteLLM models, MCP servers, skills, and spend.

Left-sidebar Skills panel for the DSH web client: lists the skills of the current DSH instance, pins favourites, and inserts /skill-name into the composer.

Serves the OwlCoda RunKit skill in the DSH skill catalog: project-owned objectives, work items, candidate identity, verification receipts, handoffs and recovery stay in project artifacts. Installs instructions only; the project-local owlrunkit CLI is a separate setup step.

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.

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.

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.

AOCI-CODE cognition layer for DeepSeek Harness: one-shot /aoci command + dynamic MCP bridge for governed, Git-versioned repository/database cognition.

HireSeeker: поиск вакансий из Telegram-каналов и карьерных сайтов компаний. CLI/npx, JSON, skills для AI-агентов и плагин DeepSeek Harness.

Skills, MCP and installable assistant packages with session-scoped capabilities on official DSH services

Skill Forge for DeepSeek Harness: distill a conversation or pasted context into a reusable SKILL.md, so a similar question later hits a skill instead of re-deriving the answer. Includes 4 model tools and a Web GUI panel.

Cross-model continuity for DeepSeek Harness: memory, skills and rules live in local files, so judgement survives session and model changes. Not about storing more memory — about using it well after the model changes.

Inventories installed DSH skills and profile plugins, checks their upstream sources for newer versions, applies the updates, and installs skills from a searchable catalog.

Grouped on/off switches for the skills a DSH profile exposes: the effective set is the union of the groups that are switched on, with a catalog-derived group for the skills shipped with the harness, a panel over the composer, and skill_toggles_* tools for agents.

Bundles 50 AMD ROSS agent skills for Vivado, Vitis HLS, Versal segmented configuration, Vitis AI, and hardware debug into DeepSeek Harness, with a left-sidebar catalog panel for filtering, searching, and copying skill invocations.

Searches the installed skill catalog on a trigger word, requires a use-or-skip decision for every candidate, and blocks other tools until all selected skills are loaded.

Search, inspect, and use selected third-party skills and MCP tools through one on-demand gateway. Catalog trimming depends on the host and tool mode. A local journal records usage metadata.

Turns archived sessions into durable memory instead of dead disk. Distils each archive into reusable skills, a fact ledger with ADD/UPDATE/NOOP decisions plus validUntil and supersededBy so stale facts retire instead of piling up, and topic clusters by single-linkage similarity. Also digests sessions that are still in progress through a read-only seq watermark, leaving the session log byte-for-byte untouched, and can self-check the growing memory to merge duplicate facts, retire stale ones and consolidate duplicate skills — measuring only unless you opt in, and never hard-deleting, only superseding.