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Agent skill

Enrich

by sharpdeveye in sharpdeveye/maestro

A skill your agent uses when the agent needs access to information beyond its training data — knowledge sources, RAG pipelines, or grounding data.

MITAuto-check passedAI & LLM Engineering

Install Enrich

skills CLI
$ npx skills add sharpdeveye/maestro --skill enrich -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sharpdeveye/maestro enrich --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sharpdeveye/maestro.git skills-src && mkdir -p .claude/skills && cp -r skills-src/source/skills/enrich .claude/skills/enrich && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
enrich
GitHub stars
592
Token cost
~827 tokens
SKILL.md length
375 words
Files
1
Skills in repo
25
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when the agent needs access to information beyond its training data — knowledge sources, RAG pipelines, or grounding data.

  • Works in 6 steps: Select documents: Identify the… → Chunk strategy: Choose chunking based on… → Embed: Use appropriate embedding model… → …
  • The agent needs access to information beyond its training data — knowledge sources
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Tasks that involve Retrieval-augmented generation

What it does

Enrich is an agent skill from sharpdeveye/maestro. Use when the agent needs access to information beyond its training data — knowledge sources, RAG pipelines, or grounding data.

Its SKILL.md is about 830 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Retrieval-augmented generation. The repository describes itself as: Workflow fluency for AI coding agents. 1 core skill · 25 commands · 7 domain references · memory layer · audit trail — works across Cursor, Claude Code, Gemini CLI, Copilot, and… The licence is MIT.

When your agent uses it

  • The agent needs access to information beyond its training data — knowledge sources
  • Tasks that involve Retrieval-augmented generation

Example prompts

  • “/enrich”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Select documents: Identify the authoritative source documents
  2. Chunk strategy: Choose chunking based on document type (semantic > token-based)
  3. Embed: Use appropriate embedding model for the domain
  4. Index: Store in vector database with metadata
  5. Retrieve: Implement hybrid search (semantic + keyword)
  6. Inject: Add retrieved context to the prompt with source attribution

What it can do on your machine

Read from SKILL.md and the folder at commit 00f9115. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Enrich loads about 827 tokens when it runs. Until then it costs about 33 tokens; SKILL.md has 375 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~33
When it runs · the whole SKILL.md, loaded when a task matches
~827

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sharpdeveye/maestro at commit 00f9115, republished under its MIT licence (© sharpdeveye). 375 words, ~827 tokens.

Download SKILL.mdSave it as .claude/skills/enrich/SKILL.md (or your agent's skills folder).
name
enrich
description
Use when the agent needs access to information beyond its training data — knowledge sources, RAG pipelines, or grounding data.
argument-hint
[knowledge domain or source]
category
enhancement
version
2.0.0
user-invocable
true

MANDATORY PREPARATION

Invoke /agent-workflow — it contains workflow principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding — if no workflow context exists yet, you MUST run /teach-maestro first. Consult the knowledge-systems reference in the agent-workflow skill for RAG architecture, chunking strategies, and retrieval patterns.

Add knowledge sources to ground the workflow in facts. Without grounding, agents hallucinate. With grounding, they cite sources.

Knowledge Source Assessment

Identify what knowledge the workflow needs:

Knowledge TypeSourceUpdate FrequencyAccess Pattern
Domain docsInternal docs, specsMonthlySemantic search
Code contextCodebaseReal-timeCode search
User dataDatabase, CRMReal-timeStructured query
External dataAPIs, webReal-timeAPI call
HistoricalLogs, past interactionsDailyTime-range query
Add RAG Pipeline

For document-based knowledge (consult the knowledge-systems reference in the agent-workflow skill):

  1. Select documents: Identify the authoritative source documents
  2. Chunk strategy: Choose chunking based on document type (semantic > token-based)
  3. Embed: Use appropriate embedding model for the domain
  4. Index: Store in vector database with metadata
  5. Retrieve: Implement hybrid search (semantic + keyword)
  6. Inject: Add retrieved context to the prompt with source attribution
Add Structured Data

For database-backed knowledge:

  1. Define the query interface: Natural language → structured query
  2. Add guardrails: Read-only access, query complexity limits
  3. Format results: Transform raw data into context the model can use
  4. Attribute: Include data source and freshness in the context
Show full SKILL.md (147 more words)Show less
Add Real-Time Data

For live information:

  1. Identify APIs: What external services provide the needed data
  2. Cache strategy: How often does the data change? Cache accordingly
  3. Fallback: What happens when the API is down?
  4. Attribution: Include data timestamp and source
Enrichment Checklist
  • Every knowledge source has attribution (source, date, confidence)
  • Retrieval quality tested independently of generation quality
  • Chunk sizes tested and optimized for the document types
  • Fallbacks exist for all external knowledge sources
  • Knowledge base has a refresh/update strategy
  • PII is handled appropriately in knowledge sources

After enrichment, run /evaluate to test retrieval quality, or /iterate to set up continuous monitoring of knowledge freshness.

NEVER:

  • Index everything without curation (garbage in = garbage out)
  • Skip source attribution (hallucination without attribution is undetectable)
  • Build RAG without testing retrieval quality first
  • Use fixed chunk sizes for all document types
  • Assume embedding similarity equals relevance

© sharpdeveye, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in source/skills/enrich of sharpdeveye/maestro.

Open the folder on GitHubat commit 00f9115

Compare with similar skills

Enrich next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Enrich compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Enrich this skillsharpdeveye/maestro592—~827Automated safety check: PassMIT
Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs13k8 repos~2.3kAutomated safety check: PassMIT
LLM Application DevMoizIbnYousaf/ai-agent-skills1.1k2 repos~1.3kAutomated safety check: PassMIT
Senior Prompt Engineermaslennikov-ig/claude-code-orchestrator-kit2604 repos~1.4kAutomated safety check: PassCustom licence
MCP Local RAGshinpr/mcp-local-rag407—~4.4kAutomated safety check: PassMIT
Ms Agent Framework RAGshuyu-labs/WebCode278—~1.1kAutomated safety check: PassCustom licence

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  • Chroma Vector Database

    Orchestra-Research/AI-Research-SKILLs

    Shows how to store documents and embeddings in Chroma, query them by similarity with metadata filters, and persist them to disk for RAG and semantic search projects.

    13k GitHub starsUsed in 8 repos~2.3k tokens
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  • LLM Application Dev

    MoizIbnYousaf/ai-agent-skills

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  • MCP Local RAG

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  • Compose

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  • Diagnose

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  • Extract Pattern

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Categories

Questions about Enrich

What does Enrich do?

A skill your agent uses when the agent needs access to information beyond its training data — knowledge sources, RAG pipelines, or grounding data. Enrich is an agent skill from sharpdeveye/maestro. Use when the agent needs access to information beyond its training data — knowledge sources, RAG pipelines, or grounding data.

When should I use Enrich?

Enrich fits situations like: the agent needs access to information beyond its training data — knowledge sources; tasks that involve Retrieval-augmented generation.

How do I install Enrich in Claude Code?

Run `npx skills add sharpdeveye/maestro --skill enrich -a claude-code`. Or copy the skill folder (source/skills/enrich in sharpdeveye/maestro) into .claude/skills/enrich in your project. Claude Code loads it when a task matches its description.

How do I install Enrich in Codex?

Run `npx skills add sharpdeveye/maestro --skill enrich -a codex`. Or copy the skill folder (source/skills/enrich in sharpdeveye/maestro) into .agents/skills/enrich in your project. Codex loads it when a task matches its description.

Can I use Enrich in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sharpdeveye/maestro --skill enrich -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/enrich, .gemini/skills/enrich, .github/skills/enrich and .opencode/skills/enrich in your project.

What does Enrich need to run?

SKILL.md names no scripts, command-line tools or credentials: Enrich is instructions for the agent only.

Does Enrich access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Enrich safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Enrich use?

Enrich is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Enrich use?

About 827 tokens (SKILL.md is roughly 3.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Enrich?

Skills that share tags, products or a category with Enrich: Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars), LLM Application Dev (MoizIbnYousaf/ai-agent-skills, 1.1k stars), Senior Prompt Engineer (maslennikov-ig/claude-code-orchestrator-kit, 260 stars) and MCP Local RAG (shinpr/mcp-local-rag, 407 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Enrich?

sharpdeveye (a GitHub user) maintains it in sharpdeveye/maestro, which has 592 GitHub stars. The repository holds 25 skills in this directory. The repository was last updated on April 29, 2026.

Source: sharpdeveye/maestro on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.