Configure Raw logo

Configure Raw

Community
lploc94
configure-raw

Use to explain, edit or diagnose Raw configuration through files, CLI or dashboard, including package bindings, models, agents, prompts, UI, vars, limits and policy. Use creation skills for new assets or MCP connections.

Overview

Publisherlploc94
Repositoryraw-cli
Skill nameconfigure-raw
Stars
107
Forks
14
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by lploc94 on GitHub. Read the source before you install it.

Installation

Install the Configure Raw AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/lploc94/raw-cli/tree/main/examples/skills/configure_raw
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/lploc94/raw-cli.git /tmp/raw-cli
mkdir -p .claude/skills
cp -r /tmp/raw-cli/examples/skills/configure_raw .claude/skills/configure-raw
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Configure Raw in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Configure Raw on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Configure Raw is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Configure Raw

Edit or diagnose settings; preserve unrelated fields. Creation skills cover new assets.

File and selection

File: $XDG_CONFIG_HOME/raw/config.json or ~/.config/raw/config.json; --config selects another. Strict JSON: no unknown/duplicate fields, comments or trailing commas. raw config init creates once.

Root: models, agents; optional default_agent, mcp, sessions, ui, vars, var_providers. Agent precedence: --agent NAME, RAW_AGENT, default_agent. Prompt precedence: --system-prompt, RAW_SYSTEM_PROMPT, agent prompt, built-in prompt. No --model; agents reference model aliases.

Read references/packages.md for bindings and references/dashboard.md for web editing.

Model: models.<alias>

Required strings: provider (service name), method, exact upstream model_id. Methods: openai-chat-completions, openai-responses, anthropic-messages, google-generate-content.

  • base_url: HTTP(S); required except matching official OpenAI/Anthropic/Google, Ollama chat (http://127.0.0.1:11434/v1), OpenRouter chat (https://openrouter.ai/api/v1).
  • api_key: nonempty literal OR api_key_env: environment identifier, never both. Default env keys: OpenAI OPENAI_API_KEY, Anthropic ANTHROPIC_API_KEY, Google GEMINI_API_KEY then GOOGLE_API_KEY, OpenRouter OPENROUTER_API_KEY. Selected model alone resolves credentials; other providers have no default.
  • vision: boolean, default false; required for builtin/view_image.
  • context_window_tokens, max_output_tokens: positive integers; output below context. Verify limits.

Agent: agents.<name>

Required model: existing alias; tools.use: ordered unique exact IDs (empty allowed): builtin/<id>, local/<id>, agent/<id>, mcp/<server>/<original-name>. No wildcards. builtin=package, local=global Raw folders, agent=config-adjacent folders. Shell tools: builtin/bash, or builtin/powershell (same arguments) on Windows without Git Bash.

Optional:

  • system_prompt: string, empty allowed, OR system_prompt_file: nonempty UTF-8 path relative to config or absolute. Never both.
  • skills.use: unique builtin/local/agent IDs. Nonempty requires both builtin/list_skills and builtin/load_skill.
  • hooks.use: ordered unique exact string IDs: local/<name>, agent/<name> or pkg/<alias>/hooks/<export>; empty/omitted means none. The folder has hook.json and owned scripts. Read references/hooks.md for every field and value type; create_hook covers authoring and tests.
  • vars: ordered unique existing root variable names; omitted means none. Add builtin/list_vars/builtin/read_var to tools.use for discovery/reading.
  • Positive integers max_steps (10000), max_output_bytes (65536), request_timeout_ms (600000, the longest a model stream may stay silent). Precedence: flags, RAW_* env, agent.
  • tools.rules: ordered {match:string,effect:"allow"|"ask"|"deny"}. Match canonical IDs/globs (*, ?); last matching rule wins; unmatched runs automatically. Only ask permits when:{any:string,regex:string}, a schema-bound string, number or boolean path and RE2 search. Bash uses command; run_in_background matches true. -y cannot bypass ask; no approval channel fails closed. Not a shell parser/sandbox.

request.max_output_tokens: positive, within model/context reserve. Other fields only for matching pairs:

Hook manifest and response fields are specified in references/hooks.md. Gate failures block; notification failures warn. Hook approval never overrides a tool rule.

PairFields/values
OpenAI chat/responsesservice_tier: auto/default/flex/fast/priority; reasoning_effort: none/minimal/low/medium/high/xhigh/max; Responses reasoning_mode: standard/pro
DeepSeek chatthinking: enabled/disabled; reasoning_effort: low/high/max, incompatible with thinking disabled
Anthropic messagesthinking: {type:"adaptive"}, {type:"disabled"} or {type:"enabled",budget_tokens:N} (integer >=1024, below output cap); effort: low/medium/high/xhigh/max; service_tier: auto/standard_only
Google generate-contentthinking_level: minimal/low/medium/high OR nonnegative integer thinking_budget

Verify upstream support. cache: mode auto/no-hints, optional nonempty key (OpenAI), retention (OpenAI/Anthropic), backend generic/llama.cpp (latter requires chat). compact: nonnegative keep_recent_turns (2, a lower bound when it fits), positive keep_recent_tokens (verbatim tail size, min(20000, a quarter of the input budget)), positive max_output_tokens (16384, lowered to the model output cap and a quarter of the context), compact.trigger_tokens: positive threshold with context metadata/output reserve, false for manual only; default is 80% of the input budget when the model declares context_window_tokens. compact.clear_tokens: positive threshold for clearing old tool results to saved copies before compacting, false to turn it off; requires context_window_tokens, default 60% of the input budget. compact.instructions: optional string (at most 16384 characters) appended to every checkpoint prompt, e.g. what the checkpoint must always keep. compact.strategy: "checkpoint" (default) or "native", opt-in provider compaction on Anthropic Messages and OpenAI Responses only, falling back to a checkpoint with a warning.

Variables and providers

vars.<name> requires nonempty description, access:"read"|"use", and source. Read allows read/consume; use only consume. Optional type: string/number/boolean/object/array/null/json. Optional cache_ttl_ms: integer 0..2147483647 (default 0). Names: [a-z][a-z0-9_.-]{0,63}; constructor/prototype reserved.

Source shapes:

  • {kind:"literal",value:JSON}: type inferred, including false/0/empty/null.
  • {kind:"env",name:"ENV_NAME"}: string; unset errors, empty works.
  • {kind:"file",path:"data.json",format:"json"}: config-relative/absolute. text(default) preserves whitespace; json parses. UTF-8, <=65536 bytes.
  • {kind:"provider",name:"provider_name",params:{}}: params: config-defined object. No recursive refs. Built-in system.time requires empty params, returns UTC ISO time.

Type defaults: env/text/time string, literal inferred, others json. Env consumption accepts string/finite number/boolean, rejecting NUL.

var_providers.<name> requires string command; optional args: string array ([]), cwd: config-relative/absolute path (config directory), timeout_ms: integer 1..2147483647 (5000), max_output_bytes: integer 1..1048576 (65536, combined stdout/stderr). system.time is reserved. Bare commands use PATH; command paths resolve from config. Args stay literal and run from provider cwd. No shell/template expansion; process inherits environment.

Providers receive {protocol_version:1,name,params} on stdin, return {value,observed_at?} JSON on stdout, exit 0. Errors fail without stale fallback.

raw [--config PATH] [--agent NAME] vars list|get NAME needs no model/MCP/session. List is metadata; get requires read access. Bash env_refs passes named vars as environment values.

Values cache per runtime; resume starts empty. Vars changes preserve the prefix/key.

UI, MCP and sessions

Root ui: density compact/normal/verbose; reasoning hidden/summary/full; color auto/always/never; icons auto/unicode/ascii; theme terminal/dark/light. Matching flags override config. Show thinking with "ui":{"reasoning":"full"}.

mcp.servers.<name>: stdio {transport:"stdio",command:string,args?:string[],env?:string-map,timeout_ms?} or remote {transport:"streamable-http",url:HTTP(S),headers?:string-map,timeout_ms?}. timeout_ms (1..2147483647, default the agent request_timeout_ms) bounds connect, discovery and each call; progress notifications restart it. Env/headers stay literal. Activate by exact tools.use IDs.

Canonical-only sessions.retention_days: positive integer, default 30. Resume uses current config/agent on the same ID. Meaningful changes rotate once; unchanged resumes stabilize. Old stores cannot block new work.

Edit and verify

Back up, keep mode 0600, validate before replacing. Portable candidate: raw --config candidate.json config list. Canonical candidate with sessions:

sh
set -eu
stage=$(mktemp -d)
trap 'rm -rf "$stage"' EXIT
mkdir "$stage/raw"
cp "$1" "$stage/raw/config.json"
XDG_CONFIG_HOME="$stage" raw --config "$stage/raw/config.json" config list

Test changes.

Complete example

Replace model ID.

json
{"default_agent":"raw","models":{"local":{"provider":"ollama","method":"openai-chat-completions","model_id":"YOUR_INSTALLED_MODEL","context_window_tokens":32768}},"vars":{"now":{"description":"Current UTC time","access":"read","source":{"kind":"provider","name":"system.time"}}},"agents":{"raw":{"model":"local","vars":["now"],"tools":{"use":["builtin/read_file","builtin/bash","builtin/list_vars","builtin/read_var"]},"hooks":{"use":["agent/guard"]},"compact":{"trigger_tokens":24000}}}}

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Configure Raw AI skill do?

Use to explain, edit or diagnose Raw configuration through files, CLI or dashboard, including package bindings, models, agents, prompts, UI, vars, limits and policy. Use creation skills for new assets or MCP connections.

Why use Configure Raw on TypingMind?

Because you install it once and use it with any model. Configure Raw is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Configure Raw in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lploc94/raw-cli/tree/main/examples/skills/configure_raw. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Configure Raw?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Configure Raw?

As many as you like. As long as a model supports skills, you can use Configure Raw with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Configure Raw AI skill free?

Yes. It is published on GitHub by lploc94 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇