Plugins

Pipeline diário automatizado de conteúdo para Insta360 GO 3S: captura remota BLE/WiFi de filmagens de aquário, importação sem fio, avaliação de qualidade ffmpeg com registro de decisões, edição automática (seleção de segmentos, narração TTS, música, legendas) e upload no Bilibili via biliup (dry-run por padrão).

Plugin anfitrião do Kimi WebBridge para o DeepSeek Harness: ferramentas de modelo que controlam o navegador REAL do utilizador (navegar, snapshot, clicar, preencher, avaliar, captura de ecrã, inspeção de rede, gestão de separadores) através do daemon local kimi-webbridge

Pesquisa semântica local de código para o DeepSeek Harness: indexação ao nível de fragmentos ciente de símbolos, embeddings lexicais offline ou um endpoint compatível com OpenAI, e recuperação híbrida de vetores + BM25 fundida com RRF — três ferramentas dsh mais um CLI sema.

Memória de decisões e objetivos com linhagem: comandos explícitos e uma ferramenta de modelo registram decisões de forma determinística, um cartão de status as reancora ao longo da compactação, um fluxo de eventos sem perdas com recuperação BM25+jieba dá suporte a isso, e uma suíte de regressão de fidelidade em nível de cenário controla os releases.

Chat nativo lado a lado com a conversa principal: uma sessão DSH arquivada independente com interface nativa completa, recuperação de contexto pai sob demanda, citação de seleção e ciclo de vida de manter ou excluir.

Executa experimentos reproduzíveis de perfil e patch do DSH com criação guiada de casos, asserções, relatórios de comparação na Web, recuperação e barreiras de política.

30 paletas de temas clássicos de código aberto (Catppuccin, Nord, Tokyo Night, Gruvbox, Solarized etc.) mapeadas para os tokens oficiais do ThemeService do DeepSeek Harness, com um painel de Style Settings para ajustes ao vivo de cor/fonte/raio e exportação/importação JSON para compartilhar pacotes de configuração.

Analisa código local e GitHub/npm antes do desenvolvimento para encontrar implementações reutilizáveis, avalia reutilização versus reescrita e verifica o acoplamento da arquitetura.

Examina as habilidades visíveis para a sessão atual e as classifica por relevância em relação à conversa recente.

Um plugin de meta-agente nativo do modelo para o DeepSeek Harness: o modelo sintetiza workflows específicos da tarefa em tempo de execução a partir de cinco padrões de workflow agêntico (encadeamento de prompts, workers paralelos, roteador, supervisor, ciclo de avaliação) e coordena ferramentas e subagent

Cria pacotes de base de conhecimento auditáveis (SQLite FTS5) a partir de md/txt/docx/pdf, com recuperação determinística e leitura do texto original

Prior-art search for an idea: three tools find similar open-source projects on GitHub, rank them by a score computed in code from a relevance judgement and the star count, then read the chosen repositories (README, dependency manifest, directory tree, key files) and report the class and function declarations found in each file header; files_read lists what was actually fetched.

All-in-one local memory: JSONL files per scope (session, project, global, or a custom name) with per-scope dedup, four model tools (memory_save, memory_search, memory_list, memory_forget), automatic recall injected into agent pre-step, and a settings panel. Retrieval combines lexical IDF with a local embedding backend, which the panel can install and manage itself.

Routes DSH web search through the current session's model and a self-hosted retrieval backend.

A Cyrene-themed agent for DSH, with a customizable look (palette, wallpaper, particle effects, background music, typing sound) and a code security scan. It carries a worldbook, long-term memory and a speech-style correction layer (every reader gets their own Cyrene), plus retrieval over the script to back up its answers.

Cross-compaction memory for a long session: on compaction it stores what is being dropped, and at question time it injects a few relevant passages (or nothing) so the model can recall what you decided before.

Memory-assembly team for DeepSeek Harness: every message triggers a five-step cognition loop (judge, retrieve, assemble, feedback, calibrate) carried by role-playing subagents (judge, retriever, archivist, advocate, editor, scribe), using BM25 retrieval, time-ordered material, and LLM assembly to build useful material packs injected into linghun. Works with dsh-linghun.

Turns a local Obsidian vault into a dedicated RAG chat panel: a sidebar page with the note browser, the retrieval-augmented chat and the cited source side by side, answering from fully local BM25 plus CJK-bigram retrieval over your notes and streaming through the DSH model channel, with clickable \[n] citations and mtime-based incremental indexing.

Read-only Skill inspector for DeepSeek Harness. It shows which Skills a conversation actually loaded — from observed loading evidence only — and which Skills the current environment can discover, and gives each Skill a detail page whose left column switches between the framework, the evidence, validation, evaluation, step evidence, run logic, the before/after of a modification when there is one, the definition facts and the raw SKILL.md. Declaration and run stay apart: the body is split into structure, declared workflow and progressive disclosure, the run logic is read from local receipts, and every declared step carries its own evidence — no observed evidence is reported as exactly that, not as executed or not executed, and the run's instruction hash sits beside the current definition as match, mismatch or unavailable. The evidence view gives each subject one of three states (declared, observed, unavailable), walks definition → load → use → outcome, names what each stage cannot reach, declines to attribute an outcome to the Skill, and can copy the snapshot out as byte-stable JSON. Validation checks the Agent Skills open standard and the platform profiles separately, each finding on a rule id and labelled standard compliance or platform compatibility. Evaluation keeps repeatable cases with the conditions each run was recorded under and the four stages of runtime evidence, comparing two runs of one case item by item without scoring them. The trigger boundary keeps should-trigger and should-not-trigger prompts, has you run them in a real new session, then records what was observed — whether a load record appeared and whether it was model-invoked or user-explicit; a typed /name is not a natural trigger, and an absent load record is reported as not observed, not as not triggered. The same projections are exposed to the agent as a read-only skill_review tool, which returns a structured review packet and says what it omitted when the packet will not fit whole. The inspector, the evidence chain and these reviews are computed on this machine from the definition and the session receipts; the Chinese reading version sends the body through the host's model when you ask for it, keeping code fences, paths and frontmatter verbatim and storing the result on this machine. A Skill can also be cloned into a local copy. It does not write back to the source Skill and does not run its scripts.

Reflect2Evolve memory plugin: layered L1/L2/L3 memory, reflection-weighted value backprop, cross-task policy induction, skill crystallization, and three-tier retrieval for OpenClaw, Hermes Agent, and DeepSeek Harness.

Windows system-drive (C:) cleanup for DSH: rule-based scan, five-tier safety grading, a staging area with a rollback ledger, UAC elevation, and migration to another drive — a model tool plus a Web GUI panel.

Language-pack framework that rewrites model replies: A is an independent LLM rewrite, B injects FTS5 terminology retrieval into that rewrite, C applies local dictionary and regex rules, and composite runs a pipeline such as B then A then C. Ships a GitHub pack marketplace, a ZIP installer with path, size and file-type checks, and a mandatory risk disclaimer gate.

Persistent project intelligence and memory plugin for DSH: architecture analysis, cross-session context, TODOs, and optional hybrid retrieval

Compares existing DSH sessions or runs controlled A/B trials between agent presets, showing aligned timelines, paired metric deltas, explicit success checks, and exportable reports.