Plugin

Plugin di visione per DeepSeek Harness: 8 modalità di analisi (descrizione, OCR, dati dai grafici, revisione UI, rilevamento oggetti, confronto, code-gen, debug), qualsiasi API di visione compatibile con OpenAI o Anthropic, con un modello di visione gratuito integrato e failover automatico sui limiti di frequenza.

Console di debug LLM per DeepSeek Harness: cattura il contenuto completo di ogni chiamata modello alla waterfall llm/stream (opzioni, chunk di stream, usage, wire protocol) e lo mostra in un pannello ancorato stile DevTools, con diffing del contesto shared-prefix, ricostruzione del wire-JSON, esportazione JSON e una politica di cattura regolabile a runtime.

Impacchetta una libreria di skill di metodologia ingegneristica in un unico plugin Cordis: 14 Agent Skills (pianificazione, test, debug, revisione, consegna) registrate su ctx.skills per DeepSeek Harness.

Controllo esatto del profilo del tuo vero browser Chrome autenticato: un server MCP più un'estensione MV3 e un bridge localhost autenticato per schede, locator, iniezione di file, export di media e cattura di scroll durevole, senza profilo di debug né porta di remote debugging.

Cattura ogni payload dell'API del modello upstream in JSON (debug e osservabilità)

Skill di revisione strutturata del codice per DeepSeek Harness -- bug, sicurezza, prestazioni, stile

Ispeziona e gestisci la catena di istruzioni live e le skill di progetto per la sessione dsh corrente, con creazione e modifica in loco — quello che vedi è quello che riceve il modello

Capsule di evidenza dei fallimenti local-first per le sessioni di DeepSeek Harness

Bridge IM Feishu (Lark) per DeepSeek Harness tramite `dsh plugin add`; un utente DM per una sessione dsh persistente, con documentazione di debug completa

One-step local model onboarding plus a light harness for DSH. On load it probes loopback and private-network inference endpoints (llama.cpp, Ollama, vLLM, LM Studio), reads each model's context window and output budget from the backend, and registers the provider through settings, so a local model becomes selectable without hand-writing config. It then trims only that provider: masks the tool list through tools.restrict, drops named system-prompt sections and memory contexts, swaps the injected instruction block for a short one while preserving its first 80 and last 96 characters, and truncates tool-schema descriptions without touching structure or required fields. Cloud providers are returned as the same assembly object, untouched. The injected block is re-read from disk on every assembly, so editing the file takes effect on the next request with no reload. The panel in the plugin detail page is a live operations view: it shows whether the takeover is active and by which hook, how many characters the last assembly was trimmed from and to, and which providers are online (probed concurrently, with a real one-shot completion reporting latency and reply text per provider). If a local backend is not running, the panel offers a one-click launch: it probes the port first (never starting a duplicate), then starts the matching server (ollama serve, or a custom command you supply) detached and polls until the endpoint answers, clamping the wait to 3-600 seconds, reaping the child it just spawned if it never answers, and naming the paths it searched when it cannot find the binary. A stop button kills backends this plugin started (SIGTERM, then SIGKILL) and refuses to touch a server somebody else launched. When a model has just been downloaded and no backend exists yet, the panel can also scan the machine for .gguf files with hard caps (300 models, 20000 directory entries, Downloads only one level deep), start llama-server on the chosen one (skipping the .mmproj. vision projection and reusing a server that already answers), register the provider under the model id the backend reports, and run one real completion to prove it works. Before spawning anything it does a fit check that counts only the part of the file the GPU cannot hold: hostBytes = fileSize - VRAM, compared against max(total RAM - 2GB, free RAM). A file far larger than RAM is refused outright; a heavy host-side remainder is refused unless you confirm; the same remainder under mmap is only a warning, because the weights land in reclaimable page cache. A load-mode selector (auto / mmap / none / mlock / mmap+mlock) maps to llama.cpp --load-mode, and the default is whatever the engine picks. From 0.4.2 everything above is derived from the platform instead of assumed: home and data directories come from os.homedir() and the OS conventions (LOCALAPPDATA on Windows, ~/Library/Application Support on macOS, XDG_DATA_HOME elsewhere); backend binaries are looked up in the official install locations of each OS and can be pinned with OLLAMA_BIN, LLAMA_SERVER_BIN, VLLM_BIN or LMSTUDIO_BIN; model discovery walks DSH_LOCAL_MODELS_DIR, ~/models, ~/llama.cpp/models, ~/.cache/llama.cpp and the LM Studio data directory, with the WorkBuddy layout demoted to one candidate among them. VRAM is asked from nvidia-smi, then rocm-smi, then sysctl hw.memsize on Apple Silicon (flagged unified, so the same bytes are not charged twice), and any flip to a unified-memory budget is handled explicitly. Before building a command line the plugin runs llama-server --help once and keeps only the flags that build actually has, so older engines get --n-gpu-layers 99 and no KV quantization while newer ones get auto plus q8_0; anything it cannot probe is passed through rather than dropped. LM Studio is started with lms server start --port N rather than being lumped in with the engines that need a model argument, and vLLM gets its own --model example instead of llama.cpp syntax. Windows, macOS and Linux are all exercised in CI, including a windows-latest and a macos-latest job, because a cross-platform claim that has only ever run on one OS is not a claim. Two of those suites exist specifically to catch this machine's blind spots: one replays the whole suite with process.platform rewritten to each OS, and one deletes every advantage this development box has — no nvidia-smi, no rocm-smi, no sysctl, an empty PATH, a home directory with no models — and requires graceful degradation rather than a crash. That last one caught a real class of bug: several assertions hardcoded 'lms.exe', which cannot pass where the executable has no suffix. Measured on the reference machine: a 16.9GB 27.2B MoE with 14.5GB of VRAM holds a 285-691MB working set and sustains 76 tok/s after a 1000-2000 token warm-up, so file size alone is not a fit signal. From 0.4.3 the scanned models are shown as a table rather than a dropdown (one row per gguf, with its size and any fit warning), so you can see what is on disk before choosing. From 0.4.4 the delete button on the provider table removes the provider entry, not the weights file on disk. That distinction matters: what people actually want to clear out is the dead entry - one pointing at a port that never answered, one registered twice for the same port, one whose model file was deleted long ago - not a 15.8GB gguf. Deleting a config is undone by importing again; deleting a gguf is not. The removal goes through settings.mutate with an unset op on \['providers', id], because update merges layers: writing null there leaves a null provider key that the loader reads as a broken config rather than an absent entry, and only mutate/unset is implemented as Reflect.deleteProperty. The result is read back afterwards, because unset on a key that comes from a lower layer (the profile cordis.patch.yml or a home patch) does not delete it - it restores the inherited value, so the call succeeds while the entry stays. When the entry is still there the panel says so and points at the file to edit instead of reporting success, and a DSH whose settings service predates mutate() is told plainly rather than silently ignored. Deleting takes two clicks and clearing the confirmation is as easy as clicking another row; on success the online and baseURL caches are cleared and the tool surface is recomputed, since the provider list changed. The gguf itself is never touched by this plugin - the scanned-model table is display and selection only.VRAM reporting is driver-dependent. From 0.4.5 model discovery also reads Ollama's own storage: its blobs are named sha256-… with no extension, so extension matching could never list them. Weights are now accepted by extension OR by GGUF magic header (only dot-free files of at least 8MB, capped at 600 header reads per scan; obviously-not-weights extensions like .json/.md/.safetensors/.part are never opened). Ollama models are resolved from their manifests: the model layer is picked by mediaType application/vnd.ollama.image.model (never adapter/mmproj layers), the human-readable name is restored from the manifest path (…/qwen3/8b becomes qwen3:8b, …/latest stays bare), tags sharing one blob merge into a single row, and the metadata kv supplies name, quantization and context length. Adopting an Ollama model does not start a duplicate llama-server over the same weights: it confirms ollama serve answers on 11434 (launching it if not) and registers the provider under the tag the endpoint itself reports — the blob's sha256 name would never match anything, since 11434 lists qwen3:14b, not sha256-a8cc…. Two real-machine-only traps fixed on the way: existsSync('<file>/') returns true on Windows, so manifest-vs-directory branching now goes by parseable content instead of trailing-slash semantics; and the quantization label table had been copied from a stale ggml enum generation, labelling ggml_type 15 as Q5_K_S while ollama show reports Q4_K_M. Same-blob dedup is by (dev,ino), keeping whichever entry has a name. Verified on the reference machine: the scan goes from 1 row to 4 — qwen3:14b (8.64G) and qwen3:8b (4.87G) now appear with Q4_K_M badges — in 4ms; npm run verify exits 0 (launcher 57, portable 35, panel-host green including the linux/darwin simulated-platform runs); the new panel-host suite builds a real miniature Ollama store in tmpdir and points OLLAMA_MODELS at it, so results no longer depend on whether the test machine happens to have Ollama installed. From 0.4.6 the panel shows each model its real name instead of its file name, because some download tooling stores every weight under one generic file name, leaving the real name only inside the GGUF header. The scan now reads each file GGUF KV section - not a fixed-size head buffer, because tokenizer.ggml.tokens (hundreds of thousands of strings on a large MoE model) and the chat template push general.file_type past any window; and GGUF string arrays are interleaved (u64 length plus payload per element), so treating them as a length table walks the cursor off the rails and produces astronomically large reads. Reading and skipping are accounted separately: key names and types are always read, string values over 256KB and string arrays are only walked past without copying, non-string arrays over 64 elements are skipped outright, and a cursor past the file size stops the read, because an out-of-range position means the cursor is wrong rather than that a limit should be raised. The displayed name goes Ollama tag, then general.basename plus size_label, then general.name, then the file name, with the full string kept for the hover text since it usually carries internal build numbers; mmproj files are now identified by general.type as well as by name. A short read (truncated file) no longer feeds half a value into the parser. On the reference machine, reading one file header parses 44 keys in about 16ms, and a whole scan takes about 31ms. From 0.4.7 the smoke test no longer fails the first time and passes the second. The old code gave both request attempts one shared AbortController and one shared 30-second timer, so the total budget covered both rounds: a cold load of a large model burned it in round one, the abort left the signal permanently aborted, and the second round (the Ollama-style reasoning_effort: none retry) could not even be sent - which is exactly why the first click returned This operation was aborted and the second succeeded. Now each attempt gets its own controller and timer, a timeout retries once automatically (retryOnTimeout, on by default) because a first-request timeout almost always means the weights are still being read from disk, and non-timeout errors (refused connection, HTTP error) are not retried at all. Timeouts report themselves: the message says the wait exceeded smokeTimeout, whether a retry already happened, and points at the new smokeTimeout setting (seconds, default 60, clamped to 5-600) - port responding is not the same as tokens coming out, so it is a separate knob from launchTimeout. Unrelated: an example naming one specific model was dropped from the panel copy, since not every machine has that model downloaded, and the explanation now describes the mechanism instead.

Cockpit di sviluppo a ciclo chiuso integrato in DeepSeek Harness: da un pannello l'agente compila, flasha, verifica tramite debug e monitora schede STM32; l'utente segue ogni passaggio dal vivo, con fasi di avanzamento, log in streaming, uso RAM/FLASH e forme d'onda seriali decodificate. Richiede Python 3.10+ dell'host nel PATH (il plugin non può installarlo). La toolchain ARM si scarica automaticamente al primo uso (~400 MB, solo Windows x64); serve una scheda STM32 con debug probe.

Controllo del browser per siti che richiedono l'accesso: conserva gli indirizzi da autenticare (solo URL e data, nessuna credenziale), verifica prima della visita se un indirizzo è già presente e indica se il browser di debug è autenticato in base ai nomi dei cookie di sessione del sito (certo, probabile o non autenticato; cookie anonimi come _gh_sess esclusi). Apre gli indirizzi salvati e fa clic su un elemento tramite testo visibile, cercando in ogni iframe. Un clic per chiamata, senza crawling.

Controlla tramite CDP un client desktop NetEase Cloud Music già in esecuzione (da avviare con `--remote-debugging-port=9222`) e cerca brani tramite l’API web pubblica NetEase. Nessuna API key o login aggiuntivo; le funzioni per membri usano l’account già autenticato nel client.

Workbench degli elementi Gitee Enterprise nella barra laterale Harness: esplora progetti, attività, requisiti e bug; consulta dettagli, sottoattività, commenti e Markdown; crea o modifica elementi e richiamali con @.

Integra in DeepSeek Harness 50 skills agente AMD ROSS per Vivado, Vitis HLS, configurazione segmentata Versal, Vitis AI e debug hardware. Un pannello catalogo nella barra laterale sinistra consente di filtrare, cercare e copiare le invocazioni delle skills.

Aggiunge il provider subagent `muse` e il tool `subagent_muse` a DeepSeek Harness: l’agente principale delega un task autonomo al CLI Meta Muse Code e riceve solo il report finale. Fork patchato di `@pascapone/dsh-muse-code` che funziona su DSH 0.2.1, con equivalenti locali dell’export rimosso `settleRun` e del metodo `subagents.start`, e proxy opzionale documentato per il bug stream-phase gateway. Richiede CLI `muse`. Installa questo fork oppure l’upstream, non entrambi: registrano la stessa loader row subagent-muse.

Aggiunge due skill di qualità del codice invocabili dall’utente: /simplify ripulisce il diff corrente preservando il comportamento; /code-review cerca bug da più prospettive con un passaggio di verifica indipendente. /review è un alias.

Sfrutta i fumetti del pet desktop WuwaTappo: mostra saldo DeepSeek, spesa odierna e costo per turno, e mantiene un promemoria ambra mentre l’agent attende risposta o approvazione. Comunica col pet via CDP e abilita la porta debug con una patch di byte reversibile, applicata una sola volta.

Gate di pubblicazione per autori di plugin DSH: controlla in modo deterministico l’intero flusso e fornisce i comandi esatti per GitHub/npm/market. Verifica package.json (exports["."], lib/ assente in files, type:module, export ./client), BOM UTF-8, errori JSON/YAML, indentazione/nomi insert in cordis.patch.yml, entry export, require() in ESM, LICENSE/riga d’installazione README, test mancanti, nomi npm non scoped con 403 non diagnosticabile da token granulare, versioni già pubblicate e repository GitHub mancanti. Sei strumenti agent con correzione per ogni rilievo. Sola lettura, senza LLM.

Analizza il budget di contesto speso a ogni turno: separa AGENTS.md, catalogo skill, schemi strumenti e contesto runtime; segnala come spreco la reiniezione di contenuti invariati; classifica i token descrittivi letti a ogni turno con oltre 60 skill installate e l’inflazione degli schemi per plugin. Deterministico, senza LLM. Quattro strumenti, incluso uno sweep tra sessioni.

Analizza ciò che la compattazione del contesto ha davvero perso. Legge il log grezzo degli eventi e verifica la sopravvivenza dei vincoli permanenti dell’utente (non fare, fare, solo in ambito X), percorsi/versioni/porte/ID inventati, riferimenti “vedi sopra” a testo rimosso, dimensione del riepilogo rispetto ai token liberati e potature non conteggiate. Una misura in arXiv 2608.11242 ha rilevato ~17% di sopravvivenza dei vincoli tra i compattatori. Quattro strumenti agent, incluso uno sweep intersessione che ordina le conversazioni per danno. Deterministico, senza LLM: un errore è un bug di regola identificabile.

Pannello ZenTao nella barra laterale DSH: elenca attività, bug e storie assegnati; il login Web supporta istanze senza API RESTful v2 (verificato su ZenTao 18.13 open source). Ricerca, ordinamento smart, filtri scaduto/in scadenza oggi e board di flusso. Apre una chat precompilata con descrizione, commenti recenti e screenshot (o vuota), oppure invia elementi singoli/in blocco a una sessione in background scegliendo workspace, modello, intensità di ragionamento e istruzioni facoltative. Gli screenshot diventano allegati immagine. Alla fine legge l’ultima risposta collegata e segue in corso/serve decisione/completato, con override e completamento manuali. Multi-account, sola lettura, senza scrivere su ZenTao.

Anteprima sidebar DSH e Dart hot reload solo Windows, per progetti configurati derivati da Starcompanion/Operit2. Richiede script preview, APK debug precompilato, Android SDK, Flutter e scrcpy 3.3.3.

Espone un server MCP stdio come strumenti nativi DeepSeek Harness. Include il preset ufficiale Microsoft Playwright MCP per navigare nell’Edge/Chrome già aperto con login attivi; si può configurare un altro comando MCP stdio. Solo host, senza bundle client né dipendenze npm. Per pilotare il proprio browser, avvia Edge/Chrome con remote debugging e consenti a npx di scaricare @playwright/mcp.