Most developers install one agent skill and call it done. Code Reviewer for PR feedback. SQL Generator for queries. Research Assistant for summaries. Each skill works fine in isolation. But AI agents get genuinely useful when skills work together — where one skill’s output becomes the next skill’s input, and a single prompt kicks off a multi-step workflow you don’t have to manage manually.

This guide walks through how to chain agent skills effectively, with practical examples using skills available in the AgentNDX directory.

How skill chaining works

Agent skills are CLAUDE.md-based instruction sets that tell your agent how to behave in a specific domain. When you install multiple skills, they coexist in your agent’s context. You can invoke them in sequence within a single session, or design your workflow so each skill hands off cleanly to the next.

Three patterns cover most use cases:

Sequential — Skill A completes, then Skill B runs on the output. Example: Research Assistant gathers information, Long-Form Writer turns it into a draft.

Conditional — Skill B only runs if Skill A’s output meets a condition. Example: Test Generator creates tests, then AI Detect checks the documentation for AI patterns before shipping.

Parallel — Multiple skills run on the same input simultaneously. Example: Security Auditor and Accessibility Auditor both review a new component at the same time.

Example 1: Research to publishable draft

Skills used: Research Assistant + Long-Form Writer + SEO Content Optimizer

Content teams use this chain more than any other. Research Assistant finds sources, statistics, and relevant examples on a topic. Long-Form Writer structures them into a full draft. SEO Content Optimizer tunes the title, description, and opening for search. Each skill does one job. None of them overlap.

In practice:

1. Research Assistant: "Find the top 5 studies on enterprise AI adoption failure rates from 2025-2026. Extract key statistics, authors, and publication dates."

2. Long-Form Writer: "Using this research, write a 1,200-word article on why enterprise AI adoption fails. Stat-first opening. No em dashes. First-person where it fits."

3. SEO Content Optimizer: "Optimize this article for the query 'why enterprise AI adoption fails.' Rewrite the title, meta description, and opening paragraph."

Each prompt builds on the last. The Research Assistant output feeds the writer. The writer’s draft feeds the optimizer. Total time: 10-15 minutes for a publish-ready article.

Example 2: Code review pipeline

Skills used: Code Reviewer + Security Auditor + Changelog Writer

This chain covers a PR from review to documentation. Code Reviewer handles logic, patterns, and quality. Security Auditor flags vulnerabilities and auth issues. Changelog Writer generates the release notes entry.

1. Code Reviewer: "Review this PR diff for code quality, anti-patterns, and anything that could cause issues in production."

2. Security Auditor: "Review the same diff for security issues: injection vectors, authentication gaps, secrets exposure, broken access control."

3. Changelog Writer: "Based on the PR description and these review notes, write the CHANGELOG.md entry for this release."

Running all three on a PR takes two minutes and catches what a single reviewer would likely miss.

Example 3: Data pipeline with documentation

Skills used: SQL Generator + Schema Documenter + Data Cleaner

Data teams usually write the query, skip the docs, and deal with edge cases later — often after something breaks. This chain handles all three while the context is still in one place.

1. SQL Generator: "Write a query to find all customers who haven't placed an order in 90 days, grouped by region."

2. Schema Documenter: "Document the tables this query touches — customers, orders — with column descriptions, data types, and relationships."

3. Data Cleaner: "What edge cases could cause this query to return incorrect results? What data quality checks should run before relying on this output?"

Three outputs from one run. More usefully, you get them while the query is still fresh — not six weeks later when nobody remembers what the customers table join was for.

Tips for reliable skill chains

Be explicit about handoffs. Don’t assume the agent knows the output from step 1 is the input for step 2. Reference it directly: “Using the research from the previous step…” or “Based on the review notes above…”

Keep each skill prompt focused. Each step should have one clear job. If you’re asking the Code Reviewer to also check security and write the changelog, you’re collapsing three skills into one prompt and losing the specialization.

Run quality gates between steps. If you’re chaining writing skills, run AI Detect between the writer and the optimizer. Don’t let a high-AI-pattern draft get SEO-optimized and shipped. Catch it in the middle.

Start with two skills before building longer chains. Get the two-step handoff clean before adding a third. Most failures in skill chains are handoff problems — the context from step 1 didn’t transfer clearly enough for step 2 to pick up. Not a skill problem. A prompt problem.

Finding skills to chain

All 95 skills in the AgentNDX directory are tagged by category and compatible agent. When you’re building a workflow, filter by category to find skills that cover adjacent steps in your process.

Good starting combinations by category:

  • Content workflows: Research Assistant + Long-Form Writer + SEO Content Optimizer
  • Engineering pipelines: Code Reviewer + Security Auditor + Test Generator
  • Data work: SQL Generator + Schema Documenter + Data Cleaner
  • Finance and compliance: Financial Analyst + Compliance Checker + Audit Trail Designer
  • Product and documentation: Product Spec Writer + Changelog Writer + Document Summarizer

Browse the full skills directory to find what fits your stack.

FAQ

Q: Do skills need to be installed separately for each one to work? A: Yes. Each skill is its own CLAUDE.md instruction set. Install each one for your agent using its install command, then they’re all available in the same session.

Q: Can I chain skills across different compatible agents? A: Skills are agent-specific. A Claude Code skill and a Cursor skill won’t chain in the same session. Match your skills to the agent you’re running. Skills tagged “universal” work across most environments.

Q: How many skills can I chain before things get unwieldy? A: Three to four is the practical limit for most workflows. Beyond that, the handoffs get harder to manage and the context window fills up. For longer pipelines, consider breaking the workflow into two sessions.