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Ralph Config

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jmagly
ralph-config

View and configure agent loop settings — show, set, reset, and apply named presets

Overview

Publisherjmagly
Repositoryaiwg
Skill nameralph-config
Stars
225
Forks
34
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Ralph Config 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/jmagly/aiwg/tree/main/agentic/code/addons/agent-loop/skills/ralph-config
  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/jmagly/aiwg.git /tmp/aiwg
mkdir -p .claude/skills
cp -r /tmp/aiwg/agentic/code/addons/agent-loop/skills/ralph-config .claude/skills/ralph-config
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ralph Config 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 Ralph Config 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 Ralph Config 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.

Skill access pattern (post-kernel-pivot, 2026.5+)

Skill names referenced in this document are AIWG skills, not slash commands. Most are not kernel-listed and cannot be invoked as /skill-name by the platform. Reach them via:

bash
aiwg discover "<capability>"
aiwg show skill <name> --body

Only kernel-listed skills (aiwg-doctor, aiwg-refresh, aiwg-status, aiwg-help, use, steward) are directly invokable as slash commands. See skill-discovery rule.

Al Config

View and modify Al's runtime configuration. Settings control loop behavior defaults such as iteration limits, timeouts, provider selection, and commit behavior. Changes persist to .aiwg/ralph/config.json and apply to all subsequent loops unless overridden per-invocation.

Natural Language Triggers

Users may say:

  • "ralph config"
  • "configure ralph"
  • "ralph settings"
  • "show ralph config"
  • "set ralph max iterations"
  • "reset ralph config"
  • "apply ralph ci preset"

Parameters

Subcommand (required)

One of show, set, reset, or preset.

key (required for set)

The configuration key to change. See the configuration reference below.

value (required for set)

The new value to assign to the key.

Preset name (required for preset)

One of the built-in preset names: ci, thorough, or quick.

Configuration Reference

KeyDefaultDescription
maxIterations10Maximum iterations before the loop halts
timeout60Maximum wall-clock time per loop (minutes)
providerclaudeAI provider for loop iterations (claude, codex, factory, opencode)
model(provider default)Model override for the provider
autoCommittrueCommit after each successful iteration
branchnullDefault branch name (null = work on current branch)
quietfalseSuppress verbose iteration output
memorytrueEnable semantic memory accumulation
crossTaskfalsePull relevant memories from other completed loops
analyticsfalseEnable iteration analytics logging

Built-in Presets

PresetmaxIterationstimeoutquietDescription
ci530trueConservative limits for CI/CD pipelines
thorough20120falseExtended limits for complex tasks
quick315falseFast exploration, fail-fast on difficulty

Behavior

show Subcommand

  1. Read .aiwg/ralph/config.json; if absent, show built-in defaults
  2. Display the current configuration in a readable table

Output (active config):

Al Configuration
Source: .aiwg/ralph/config.json

Key              Value        Default    Modified
───────────────  ───────────  ─────────  ────────
maxIterations    5            10         yes
timeout          30           60         yes
provider         claude       claude     no
model            (default)    (default)  no
autoCommit       true         true       no
branch           null         null       no
quiet            true         false      yes
memory           true         true       no
crossTask        false        false      no
analytics        false        false      no

Active preset: ci (applied 2026-04-01)

To change a value:  /ralph-config set <key> <value>
To reset defaults:  /ralph-config reset

Output (no config file — using defaults):

Al Configuration
Source: built-in defaults (no .aiwg/ralph/config.json found)

Key              Value
───────────────  ─────────
maxIterations    10
timeout          60
provider         claude
model            (default)
autoCommit       true
branch           null
quiet            false
memory           true
crossTask        false
analytics        false

To customize: /ralph-config set <key> <value>
To apply a preset: /ralph-config preset <ci|thorough|quick>

set Subcommand

  1. Validate the key is a recognized configuration key
  2. Validate the value type matches the key's expected type
  3. Read existing config (or start from defaults)
  4. Apply the change
  5. Write updated config to .aiwg/ralph/config.json
  6. Confirm the change

Output:

Al config updated.

  maxIterations: 10  →  15

Saved to: .aiwg/ralph/config.json

Validation error — unknown key:

Unknown configuration key: 'maxIter'

Did you mean 'maxIterations'?

Run /ralph-config show to see all valid keys.

Validation error — wrong type:

Invalid value for 'maxIterations': 'lots'

Expected: integer >= 1
Got: 'lots'

Example: /ralph-config set maxIterations 15

reset Subcommand

  1. Confirm with the user before proceeding
  2. Delete or overwrite .aiwg/ralph/config.json with built-in defaults
  3. Confirm the reset

Confirmation prompt:

This will reset all Al configuration to defaults.

Current non-default values:
  maxIterations: 5 (default: 10)
  timeout: 30 (default: 60)
  quiet: true (default: false)

Type 'yes' to confirm reset:

After reset:

Al configuration reset to defaults.

.aiwg/ralph/config.json has been cleared.
All future loops will use built-in default values.

preset Subcommand

  1. Validate the preset name is one of ci, thorough, or quick
  2. Confirm which keys will be overwritten
  3. Apply the preset values on top of current config
  4. Save and confirm

Preview before applying:

Applying preset: ci

Changes:
  maxIterations: 10  →  5
  timeout: 60  →  30
  quiet: false  →  true

Other keys are unchanged.

Apply? (yes/no):

After applying:

Preset 'ci' applied.

Al will now use:
  maxIterations: 5
  timeout: 30 minutes
  quiet: true

These values persist until changed or reset.
Saved to: .aiwg/ralph/config.json

Unknown preset:

Unknown preset: 'aggressive'

Available presets:
  ci        — max 5 iterations, 30m timeout, quiet (for pipelines)
  thorough  — max 20 iterations, 120m timeout (for complex tasks)
  quick     — max 3 iterations, 15m timeout (for exploration)

Usage: /ralph-config preset <name>

Config File Location

ContextPath
In-session loops (ralph).aiwg/ralph/config.json
External loops (ralph-external).aiwg/ralph-external/config.json

Each context has its own config file. show, set, reset, and preset operate on the in-session config by default. Use /ralph-config --external show to inspect external loop config (if the --external flag is available in your install).

Error Handling

Config file unreadable:

Cannot read Al config: .aiwg/ralph/config.json

Permission error or corrupted JSON. Options:
1. Delete the file to reset to defaults: rm .aiwg/ralph/config.json
2. Inspect manually: cat .aiwg/ralph/config.json

Read-only directory:

Cannot write to .aiwg/ralph/config.json.

Check that the .aiwg/ralph/ directory is writable:
  chmod 755 .aiwg/ralph/

Examples

Example 1: Show current config

/ralph-config show

Response: Table of all current values with defaults and modified markers.

Example 2: Set a single value

/ralph-config set maxIterations 15

Response: Confirms maxIterations: 10 → 15, writes file.

Example 3: Apply the CI preset

/ralph-config preset ci

Response: Previews 3 changes, prompts confirmation, applies and saves.

Example 4: Apply the thorough preset for a complex refactor

/ralph-config preset thorough

Response: Sets max 20 iterations and 120-minute timeout.

Example 5: Disable auto-commit

/ralph-config set autoCommit false

Response: Loop iterations will no longer create git commits.

Example 6: Reset everything

/ralph-config reset

Response: Confirms which values will revert, then clears the config file.

Related

  • ralph — Start an in-session loop (uses this config as defaults)
  • ralph-external — Start a crash-resilient external loop
  • ralph-status — Check active loop state
  • ralph-memory — Manage accumulated loop memory

References

  • @$AIWG_ROOT/src/cli/handlers/ralph.ts — Al CLI handler (config parsing)
  • @$AIWG_ROOT/tools/ralph-external/README.md — External loop architecture
  • @$AIWG_ROOT/agentic/code/addons/agent-loop/README.md — Al documentation

Frequently asked questions

What does the Ralph Config AI skill do?

View and configure agent loop settings — show, set, reset, and apply named presets

Why use Ralph Config on TypingMind?

Because you install it once and use it with any model. Ralph Config 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 Ralph Config in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jmagly/aiwg/tree/main/agentic/code/addons/agent-loop/skills/ralph-config. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ralph Config?

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 Ralph Config?

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

Is the Ralph Config AI skill free?

Yes. It is published on GitHub by jmagly 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.

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