How do I install the Manage RAG Default skill?

Run npx skillmds@latest add docailab/manage-rag-default in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Manage RAG Default safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Manage RAG Default?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Manage RAG Default free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Manage RAG Default?

DocAILab (@docailab) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Manage RAG Default by docailab — Agent Skill

Name: Manage RAG Default
Rating: 4.2 (5 reviews)
Author: DocAILab

Manage RAG Default

Analyze a RAG task and produce guidance for choosing exactly one available Agentic RAG Skill using evidence dependencies, retrieval routes, corpus characteristics, and execution budget. Use as the first and only Skill loaded during the Manage selection stage.

DocAILab Updated Sep 22, 2026

File contents

Default RAG Manager

Analyze the query and execution state before any Agentic or Component Skill body is loaded.

Selection procedure

Determine whether the task needs retrieval and identify the evidence required for a grounded answer.
Classify the task as a single ordered route, a bounded iterative evidence search, or multiple independent retrieval routes.
Distinguish sequential evidence dependencies from query views that can be retrieved in parallel.
Consider corpus size, lexical specificity, semantic mismatch, expected retrieval noise, latency budget, and token budget.
Produce guidance for selecting exactly one advertised Agentic Skill. Refer only to currently available package names and never invent a candidate.

Baseline guidance

Prefer agentic-sequential-skill when one retrieval route should be sufficient and the task can proceed directly through optional rewriting, retrieval, optional reranking, and grounded generation. Use it as the lowest-cost default for ordinary questions.
Prefer agentic-iterative-rag when later retrieval should depend on evidence or missing-information feedback from an earlier round, when evidence sufficiency is uncertain, or when a bounded Critic-guided retrieve-generate loop is needed.
Prefer agentic-parallel-rag when independent query decompositions, multiple query views, or complementary lexical and semantic retrieval routes can run concurrently and should be fused before generation. This includes comparison tasks whose evidence branches can be identified up front.
Prefer the simpler sequential workflow when the expected evidence benefit of iteration or parallel fan-out does not justify additional latency and model calls.
Reject any candidate that is not an Agentic Skill.
Do not select Component Skills directly.

Output

Return strict JSON without selecting a Component Skill:

{"agentic_selection_guidance": "selection guidance", "reason": "short reason"}

The framework will expose Agentic advertisements in the next stage and perform the actual selection.

---name: manage-rag-defaultdescription: Analyze a RAG task and produce guidance for choosing exactly one available Agentic RAG Skill using evidence dependencies, retrieval routes, corpus characteristics, and execution budget. Use as the first and only Skill loaded during the Manage selection stage.---# Default RAG ManagerAnalyze the query and execution state before any Agentic or Component Skill body is loaded.## Selection procedure1. Determine whether the task needs retrieval and identify the evidence required for a grounded answer.2. Classify the task as a single ordered route, a bounded iterative evidence search, or multiple independent retrieval routes.3. Distinguish sequential evidence dependencies from query views that can be retrieved in parallel.4. Consider corpus size, lexical specificity, semantic mismatch, expected retrieval noise, latency budget, and token budget.5. Produce guidance for selecting exactly one advertised Agentic Skill. Refer only to currently available package names and never invent a candidate.## Baseline guidance- Prefer `agentic-sequential-skill` when one retrieval route should be sufficient and the task can proceed directly through optional rewriting, retrieval, optional reranking, and grounded generation. Use it as the lowest-cost default for ordinary questions.- Prefer `agentic-iterative-rag` when later retrieval should depend on evidence or missing-information feedback from an earlier round, when evidence sufficiency is uncertain, or when a bounded Critic-guided retrieve-generate loop is needed.- Prefer `agentic-parallel-rag` when independent query decompositions, multiple query views, or complementary lexical and semantic retrieval routes can run concurrently and should be fused before generation. This includes comparison tasks whose evidence branches can be identified up front.- Prefer the simpler sequential workflow when the expected evidence benefit of iteration or parallel fan-out does not justify additional latency and model calls.- Reject any candidate that is not an Agentic Skill.- Do not select Component Skills directly.## OutputReturn strict JSON without selecting a Component Skill:```json{"agentic_selection_guidance": "selection guidance", "reason": "short reason"}```The framework will expose Agentic advertisements in the next stage and perform the actual selection.

Frequently asked questions