Execute qualquer Skill no Manus
com um clique

Execute qualquer Skill no Manus com um clique

rlm-cleanup-agent

Estrelas3

Forks2

Atualizado7 de junho de 2026 às 17:46

Removes stale and orphaned entries from the RLM Summary Ledger. Use after files are deleted, renamed, or moved to keep the ledger in sync with the filesystem. <example> user: "Clean up the RLM cache after I renamed some files" assistant: "I'll use rlm-cleanup-agent to remove stale entries from the ledger." </example> <example> user: "The RLM ledger has entries for files that no longer exist" assistant: "I'll run rlm-cleanup-agent to prune orphaned entries." </example>

Instalação

Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.

Executar no Manus

Fonte

richfrem

richfrem/agent-plugins-skills

Abrir repositório GitHub Ver repositórios do creator

Download

Executar no Manus

Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Desenvolvedores de softwareInformática e Matemática·SOC 15-1252

Explorador de arquivos

45 arquivos

SKILL.md

readonly

name	rlm-cleanup-agent
plugin	rlm-factory
description	Removes stale and orphaned entries from the RLM Summary Ledger. Use after files are deleted, renamed, or moved to keep the ledger in sync with the filesystem. <example> user: "Clean up the RLM cache after I renamed some files" assistant: "I'll use rlm-cleanup-agent to remove stale entries from the ledger." </example> <example> user: "The RLM ledger has entries for files that no longer exist" assistant: "I'll run rlm-cleanup-agent to prune orphaned entries." </example>
allowed-tools	Bash, Read, Write

Dependencies

This skill requires Python 3.8+ and standard library only. No external packages needed.

To install this skill's dependencies:

pip-compile ./requirements.in
pip install -r ./requirements.txt

See ./requirements.txt for the dependency lockfile (currently empty — standard library only).

RLM Cleanup Agent

Role

You remove stale and orphaned entries from the RLM Summary Ledger. An entry is stale when its file no longer exists or has moved. Running this regularly keeps the ledger accurate.

This is a write operation. Always confirm scope before running.

Prerequisites

Profile not configured? Run rlm-init skill first: SKILL.md

When to Run

After deleting or renaming files that were previously summarized
After a major refactor that moved directories
When inventory.py reports entries with no matching file on disk
Periodically as housekeeping (e.g. after a merge)

Execution Protocol

1. Confirm profiles to clean

Default: run against all configured profiles. Ask if unsure:

"Should I clean all profiles (project + tools), or a specific one?"

2. Dry run first -- show what will be removed

python ./scripts/cleanup_cache.py \
  --profile project --dry-run

python ./scripts/cleanup_cache.py \
  --profile tools --dry-run

Report: "Found N stale entries across profiles: [list of paths]"

3. Apply -- only after confirming with the user

python ./scripts/cleanup_cache.py \
  --profile project --apply

python ./scripts/cleanup_cache.py \
  --profile tools --apply

4. Verify

python ./scripts/inventory.py --profile project

Report the new coverage percentage.

Rules

Always dry-run first. Never apply without showing the user what will be deleted.
Never edit *_cache/*.md directly. Always use cleanup_cache.py.
Source Transparency Declaration: state which profiles were cleaned and how many entries removed.

Mais deste repositório

mesmo repositório

agent-swarm

richfrem/agent-plugins-skills

(Industry standard: Parallel Agent) Primary Use Case: Work that can be partitioned into independent sub-tasks running concurrently across multiple agents. Parallel multi-agent execution pattern. Use when: work can be partitioned into independent tasks that N agents can execute simultaneously across worktrees. Includes routing (sequential vs parallel), merge verification, and correction loops.

2026-06-083

dual-loop

richfrem/agent-plugins-skills

(Industry standard: Sequential Agent / Agent as a Tool) Primary Use Case: Delegating a well-defined task to a worker agent, verifying its execution, and repeating if necessary. Inner/outer agent delegation pattern. Use when: work needs to be delegated from a strategic controller (Outer Loop) to a tactical executor (Inner Loop) via strategy packets, with verification and correction loops.

2026-06-083

learning-loop

richfrem/agent-plugins-skills

(Industry standard: Loop Agent / Single Agent) Primary Use Case: Self-contained research, content generation, and exploration where no inner delegation is required. Self-directed research and knowledge capture loop. Use when: starting a session (Orientation), performing research (Synthesis), or closing a session (Seal, Persist, Retrospective). Ensures knowledge survives across isolated agent sessions.

2026-06-083

orchestrator

richfrem/agent-plugins-skills

(Industry standard: Routing Agent / Orchestrator Pattern) Primary Use Case: Analyzing an ambiguous trigger and routing it to one of the specific specialized implementations. Routes triggers to the appropriate agent-loop pattern. Use when: assessing a task, research need, or work assignment and deciding whether to run a simple learning loop, red team review, dual-loop delegation, or parallel swarm. Manages shared closure (seal, persist, retrospective, self-improvement).

2026-06-083

red-team-review

richfrem/agent-plugins-skills

(Industry standard: Review and Critique Pattern) Primary Use Case: Iterative generation paired with adversarial review, continuing until an 'Approved' verdict is reached. Orchestrated adversarial review loop. Use when: research, designs, architectures, or decisions need to be reviewed by red team agents (human, browser, or CLI). Iterates in rounds of research → bundle → review → feedback until approved.

2026-06-083

triple-loop-learning

richfrem/agent-plugins-skills

(Industry standard: Meta-Learning System / Automated Autoresearch) Primary Use Case: Continuous, self-improving orchestration of an agentic system over multiple sessions. Use when: building a continuous improvement layer that autonomously identifies workflow friction, postulates hypotheses, and tests improved instructions/coding skills against an objective headless benchmark before merging and persisting.

2026-06-083

name	rlm-cleanup-agent
plugin	rlm-factory
description	Removes stale and orphaned entries from the RLM Summary Ledger. Use after files are deleted, renamed, or moved to keep the ledger in sync with the filesystem. <example> user: "Clean up the RLM cache after I renamed some files" assistant: "I'll use rlm-cleanup-agent to remove stale entries from the ledger." </example> <example> user: "The RLM ledger has entries for files that no longer exist" assistant: "I'll run rlm-cleanup-agent to prune orphaned entries." </example>
allowed-tools	Bash, Read, Write

Dependencies

This skill requires Python 3.8+ and standard library only. No external packages needed.

To install this skill's dependencies:

pip-compile ./requirements.in
pip install -r ./requirements.txt

See ./requirements.txt for the dependency lockfile (currently empty — standard library only).

RLM Cleanup Agent

Role

You remove stale and orphaned entries from the RLM Summary Ledger. An entry is stale when its file no longer exists or has moved. Running this regularly keeps the ledger accurate.

This is a write operation. Always confirm scope before running.

Prerequisites

Profile not configured? Run rlm-init skill first: SKILL.md

When to Run

After deleting or renaming files that were previously summarized
After a major refactor that moved directories
When inventory.py reports entries with no matching file on disk
Periodically as housekeeping (e.g. after a merge)

Execution Protocol

1. Confirm profiles to clean

Default: run against all configured profiles. Ask if unsure:

"Should I clean all profiles (project + tools), or a specific one?"

2. Dry run first -- show what will be removed

python ./scripts/cleanup_cache.py \
  --profile project --dry-run

python ./scripts/cleanup_cache.py \
  --profile tools --dry-run

Report: "Found N stale entries across profiles: [list of paths]"

3. Apply -- only after confirming with the user

python ./scripts/cleanup_cache.py \
  --profile project --apply

python ./scripts/cleanup_cache.py \
  --profile tools --apply

4. Verify

python ./scripts/inventory.py --profile project

Report the new coverage percentage.

Rules

Always dry-run first. Never apply without showing the user what will be deleted.
Never edit *_cache/*.md directly. Always use cleanup_cache.py.
Source Transparency Declaration: state which profiles were cleaned and how many entries removed.