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deepchat-cli

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.

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ThinkInAIXYZ/deepchat
최근 소스 활동
2026년 8월 5일 13:58
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SKILL.md
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name
deepchat-cli
description
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.
allowedTools
["exec","process"]
# 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 ```text 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 ```text 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: ```text 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: ```text 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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