rlm-controller

Web & Frontend Development
v1.2.0
Benign

RLM-style long-context controller that treats inputs as external context, slices/peeks/searches, and spawns.

2866 downloads866 installsby @skywyze

Setup & Installation

Install command

clawhub install skywyze/rlm-controller

If the CLI is not installed:

Install command

npx clawhub@latest install skywyze/rlm-controller

Or install with OpenClaw CLI:

Install command

openclaw skills install skywyze/rlm-controller

or paste the repo link into your assistant's chat

Install command

https://github.com/openclaw/skills/tree/main/skills/skywyze/rlm-controller

What This Skill Does

A policy-driven scaffold for processing inputs too large for a single context window. Stores input as an external context file, slices or searches it in chunks, and spawns batched subcalls to aggregate structured results. Hard limits on recursion depth, slice count, and subcall count are enforced throughout.

Handles inputs that exceed the model context window by splitting work into bounded subcalls rather than truncating or requiring manual chunking.

When to Use It

  • Scanning 50,000-line server logs for error patterns
  • Extracting structured data from large JSONL datasets
  • Analyzing an entire repository across multiple files at once
  • Summarizing dense technical documentation in controlled sections
  • Running keyword searches across multi-file audit trails
View original SKILL.md file
# RLM Controller Skill

## What it does
Provides a safe, policy-driven scaffold to process very long inputs by:
- storing the input as an external context file
- peeking/searching/chunking slices
- spawning subcalls in batches
- aggregating structured results

## When to use
- Inputs too large for context window
- Tasks requiring dense access across the input
- Large logs, datasets, multi-file analysis

## Core files (this skill)
Executable helper scripts are bundled with this skill (not downloaded at runtime):
- `scripts/rlm_ctx.py` — context storage + peek/search/chunk
- `scripts/rlm_plan.py` — keyword-based slice planner
- `scripts/rlm_auto.py` — plan + subcall prompts
- `scripts/rlm_async_plan.py` — batch scheduling
- `scripts/rlm_async_spawn.py` — spawn manifest
- `scripts/rlm_emit_toolcalls.py` — toolcall JSON generator
- `scripts/rlm_batch_runner.py` — assistant-driven executor
- `scripts/rlm_runner.py` — JSONL orchestrator
- `scripts/rlm_trace_summary.py` — log summarizer
- `scripts/rlm_path.py` — shared path-validation helpers
- `scripts/rlm_redact.py` — secret pattern redaction
- `scripts/cleanup.sh` — artifact cleanup
- `docs/policy.md` — policy + safety limits
- `docs/flows.md` — manual + async flows

## Usage (high level)
1) Store input via `rlm_ctx.py store`
2) Generate plan via `rlm_auto.py`
3) Create async batches via `rlm_async_plan.py`
4) Spawn subcalls via `sessions_spawn`
5) Aggregate results in root session

## Tooling
- Uses OpenClaw tools: `read`, `write`, `exec`, `sessions_spawn`
- `exec` is used **only** to invoke the safelisted helper scripts bundled in `scripts/`
- Does **not** execute arbitrary code from model output
- All emitted toolcalls are validated against an explicit safelist before output

## Autonomous Invocation
- This skill does **not** set `disableModelInvocation: true`
- Operators who want explicit user confirmation before every spawn/exec should set `disableModelInvocation: true` in their OpenClaw configuration
- In default mode, the model may invoke this skill autonomously; all operations remain bounded by policy limits

## Security
- Only safelisted helper scripts are called
- Max recursion depth = 1
- Hard limits on slices and subcalls
- Prompt injection treated as data, not instructions
- See `docs/security.md` for foundational safeguards
- See `docs/security_checklist.md` for pre/during/post run checks

## OpenClaw sub-agent constraints
Per OpenClaw documentation (subagents.md):
- Sub-agents cannot spawn sub-agents
- Sub-agents do not have session tools (sessions_*) by default
- `sessions_spawn` is non-blocking and returns immediately

## Cleanup
Use `scripts/cleanup.sh` after runs to purge temp artifacts.
- Retention: `CLEAN_RETENTION=N`
- Ignore rules: `docs/cleanup_ignore.txt` (substring match)

## Configuration
See `docs/policy.md` for thresholds and default limits.

Example Workflow

Here's how your AI assistant might use this skill in practice.

INPUT

User asks: Scanning 50,000-line server logs for error patterns

AGENT
  1. 1Scanning 50,000-line server logs for error patterns
  2. 2Extracting structured data from large JSONL datasets
  3. 3Analyzing an entire repository across multiple files at once
  4. 4Summarizing dense technical documentation in controlled sections
  5. 5Running keyword searches across multi-file audit trails
OUTPUT
RLM-style long-context controller that treats inputs as external context, slices/peeks/searches, and spawns.

Share this skill

Security Audits

VirusTotalBenign
OpenClawBenign
View full report

These signals reflect official OpenClaw status values. A Suspicious status means the skill should be used with extra caution.

Details

LanguageMarkdown
Last updatedFeb 25, 2026