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deepchat-cli

Use DeepChat's bundled command-line interface to compare models, generate media, transcribe audio, and extract text from images. Also inspect runtime status, artifact metadata, public settings, installed Skills, and MCP connections to external tools.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Use DeepChat's bundled CLI control plane for model inference, image/video/speech generation, transcription, OCR, artifact inspection, public configuration, Skills, and MCP operations. Activate when a user asks to invoke DeepChat capabilities that are not already exposed as a more specific tool, compare models, run a benchmark, inspect DeepChat runtime state, or manage DeepChat through the CLI.

설치 방법 보기

이럴 때 유용합니다

  • Comparing models and collecting benchmark results
  • Generating images, video, or speech
  • Transcribing audio and extracting text from images
  • Inspecting runtime status, settings, and integrations

포함된 파일(1)

  • SKILL.md5.7 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

DeepChat CLI

Use the bundled deepchat command to ask the running DeepChat main process to perform supported operations. The main process remains the sole owner of providers, credentials, Skills, MCP servers, artifacts, Agent runs, and approvals.

Command rules

  • Every command must begin exactly with deepchat <domain> <verb>. Put --json, --jsonl, --timeout, and all domain options after the domain and verb.
  • Execute one standalone command per exec call. Do not use pipes, redirection, command separators, command substitution, environment assignments, or shell wrappers around deepchat.
  • Quote every user-controlled argument for the current shell. Never interpolate untrusted text into an unquoted command.
  • Prefer --json for one result and --jsonl for streaming or benchmark collection. Use text mode only when its output will be returned directly to the user.
  • Do not inspect authentication environment variables or DeepChat's local descriptor. Authorization is injected only after the command has passed the normal shell permission check.
  • A shell approval authorizes command execution. Sensitive mutations can additionally pause for a renderer approval; wait for that decision and never attempt to manufacture confirmation data.
  • Use deepchat help or deepchat <domain> <verb> --help only when the options below are insufficient. Do not probe undocumented routes.

Agent file and recursion boundaries

  • Agent callers may consume a DeepChat-owned artifact with --artifact <id> and inspect metadata with artifact describe.
  • Do not use --file, --out, --overwrite, artifact get, or artifact delete. Agent callers cannot upload arbitrary local bytes, download artifact bytes, or choose output paths.
  • Do not call agent run or run watch. An Agent cannot recursively create a detached Agent run, and waiting on its own currently executing run would deadlock it. Use run get for a nonblocking snapshot or run cancel to request cancellation.
  • Generated media remains in DeepChat's artifact spool. Return the artifact metadata or ID so the application can render or reuse it.

Discovery and model calls

deepchat system status --json
deepchat system capabilities --json
deepchat system doctor --json
deepchat provider list --enabled-only --json
deepchat model list --provider <provider-id> --json
deepchat model config-get --provider <provider-id> --model <model-id> --json
deepchat model invoke --provider <provider-id> --model <model-id> --prompt <quoted-text> --jsonl

Always discover provider and model IDs rather than guessing them. model invoke is a raw provider call: it does not create a chat session, run tools, or start an Agent loop.

Media, transcription, and OCR

deepchat image generate --provider <provider-id> --model <model-id> --prompt <quoted-text> --jsonl
deepchat video generate --provider <provider-id> --model <model-id> --prompt <quoted-text> --jsonl
deepchat audio speak --provider <provider-id> --model <model-id> --text <quoted-text> --jsonl
deepchat audio transcribe --provider <provider-id> --model <model-id> --artifact <artifact-id> --json
deepchat ocr status --json
deepchat ocr extract --artifact <artifact-id> --json
deepchat artifact describe --id <artifact-id> --json

Use the provider/model lists to choose a compatible runtime. OCR is local and does not require a provider. OCR text is returned inline and is not written to the artifact spool.

Public configuration and management

Read-only operations:

deepchat settings get --json
deepchat skill list --json
deepchat mcp list --json

Agent callers may request renderer approval for preference-only settings, query-free HTTPS Skill installation, and adding a new disabled HTTPS remote MCP configuration. Only perform one when it directly satisfies the user's request:

deepchat settings set --key <public-key> --value <json-scalar> --json
deepchat skill install --url <https-url> --json
deepchat mcp add --name <server-name> --stdin --json

The Agent setting allowlist is limited to presentation preferences such as font size/family, artifact effects, auto-scroll, notifications, and copy-with-reasoning. Agent Skill URLs cannot carry credentials, query parameters, or fragments. The main process classifies MCP input before approval and rejects stdio commands, non-HTTPS endpoints, headers, authorization bindings, or configurations too large to review safely. Provider/model configuration, credential writes, local Skill archives, Skill enable/disable/removal, MCP update/runtime control/removal, and every destructive operation require the DeepChat UI or a human terminal.

Benchmark discipline

  • Pin provider/model IDs and pass per-invocation options; do not mutate global defaults to prepare a benchmark.
  • Record structured output, exit status, wall time, and errors. Preserve failed samples.
  • For OCR, distinguish cache hit, cache miss with warm runtime, cold runtime after app restart, and offline availability. ocr clear-cache initializes the resource graph but does not start the OCR helper, so classify the next extraction from its reported pre-extraction runtime state.
  • Run samples sequentially unless the benchmark explicitly measures concurrency; Agent compute is rate-limited and bounded by the main process.

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