rlm-init

name	rlm-init
description	Interactive RLM cache initialization. Use when: setting up a new project's semantic cache for the first time, or adding a new cache profile. Walks the user through folder selection, extension config, manifest creation, and first distillation pass.

RLM Init: Cache Bootstrap

Initialize a new RLM semantic cache for any project. This is the first-run workflow — run it once per cache, then use rlm-distill for ongoing updates.

When to Use

• First time using RLM Factory in a project
• Adding a new cache profile (e.g., separate cache for API docs vs scripts)
• Rebuilding a cache from scratch after major restructuring

Interactive Setup Protocol

Step 1: Ask the User

Before creating anything, gather requirements:

1. "What do you want cached?" — What kind of files? (docs, scripts, configs, etc.)
2. "Which folders should be included?" — (e.g., docs/, src/, plugins/)
3. "Which file extensions?" — (e.g., .md, .py, .ts)
4. "Where should the cache live?" — Default: .agent/learning/ or config/rlm/
5. "What should we name this cache?" — (e.g., plugins, project, tools)

Step 2: Configure rlm_profiles.json

Each cache is defined as a profile in rlm_profiles.json. This file is located at RLM_PROFILES_PATH or defaults to .agent/learning/rlm_profiles.json. If it doesn't exist, create it:

mkdir -p <profiles_dir>

Create or append to <profiles_dir>/rlm_profiles.json:

{
    "version": 1,
    "default_profile": "<NAME>",
    "profiles": {
        "<NAME>": {
            "description": "<What this cache contains>",
            "manifest": "<profiles_dir>/<name>_manifest.json",
            "cache": "<profiles_dir>/rlm_<name>_cache.json",
            "extensions": [
                ".md",
                ".py",
                ".ts"
            ]
        }
    }
}

Key	Purpose
description	Human-readable explanation of the profile's purpose
manifest	Path to the manifest JSON (what folders/files to index)
cache	Path to the cache JSON (where summaries are stored)
extensions	List of string file extensions to include

Step 3: Create the Manifest

The manifest defines which folders, files, and globs to index. Extensions come from the profile config.

Create <manifest_path>:

{
  "description": "<What this cache contains>",
  "include": [
    "<folder_or_glob_1>",
    "<folder_or_glob_2>"
  ],
  "exclude": [
    ".git/",
    "node_modules/",
    ".venv/",
    "__pycache__/"
  ],
  "recursive": true
}

Step 4: Initialize Empty Cache

echo "{}" > <cache_path>

Step 5: Audit (Show What Needs Caching)

Scan the manifest against the cache to find uncached files:

python3 plugins/skills/rlm-curator/scripts/inventory.py --profile <NAME>

Report: "N files in manifest, M already cached, K remaining."

Step 6: Serial Agent Distillation

For each uncached file:

1. Read the file
2. Summarize — Generate a concise, information-dense summary
3. Write the summary into the cache JSON with this schema:

{
  "<relative_path>": {
    "hash": "agent_distilled_<YYYY_MM_DD>",
    "summary": "<your summary>",
    "summarized_at": "<ISO timestamp>"
  }
}

4. Log: "✅ Cached: <path>"
5. Repeat for next file

Step 7: Verify

Run audit again:

python3 plugins/skills/rlm-curator/scripts/inventory.py --profile <NAME>

Target: 100% coverage. If gaps remain, repeat Step 6 for missing files.

Quality Guidelines

Every summary should answer: "Why does this file exist and what does it do?"

❌ Bad	✅ Good
"This is a README file"	"Plugin providing 5 composable agent loop patterns for learning, red team review, dual-loop delegation, and parallel swarm execution"
"Contains a SKILL definition"	"Orchestrator skill that routes tasks to the correct loop pattern using a 4-question decision tree, manages shared closure sequence"

After Init

• Use rlm-distill for ongoing cache updates
• Use rlm-curator for querying, auditing, and cleanup
• Cache files should be .gitignored if they contain project-specific summaries