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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

From plugin
deepchat
6.2k18 skills
Install
$ npx -y skills add ThinkInAIXYZ/deepchat --skill deepchat-cli --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/deepchat-cli

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

SKILL.md

deepchat-cli.SKILL.md
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

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.

Read more
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Repo: ThinkInAIXYZ/deepchat