Agents
Drop-in Claude Code subagents with focused system prompts — code review, debugging, architecture, and more.
Agents (subagents) are specialized Claude Code assistants, each with its own focused system prompt and isolated context window. Claude Code delegates matching work to them automatically, or you can invoke one explicitly. Every agent below is a plain Markdown file you can copy or download straight into ~/.claude/agents.
61 results
API Architect
Use this agent to design APIs — resource modeling, versioning, pagination, error contracts, REST vs GraphQL. Examples — designing a public API, reviewing an API spec, planning a breaking change.
Backend Developer
Use this agent to build server-side features — endpoints, business logic, data access, background jobs. Examples — a new REST/GraphQL endpoint, a queue worker, a database integration.
Database Architect
Use this agent to design data models and storage strategy from access patterns — schema design, normalization vs deliberate denormalization, relational vs document vs key-value vs wide-column vs graph selection, indexing, partitioning/sharding, transaction boundaries, and consistency models. Examples — modeling a new feature's schema, choosing a database for a write-heavy event workload, reviewing a schema for missing indexes or scaling cliffs, planning how to shard a table that no longer fits one node.
Frontend Developer
Use this agent to build UI — responsive layouts, components, accessibility, and design-system work. Examples — implementing a Figma design, fixing a11y issues, building a reusable component.
GraphQL Architect
Use this agent to design GraphQL schemas and resolvers — types, nullability, connections, dataloaders, federation, depth/complexity limits. Examples — designing a new schema from requirements, killing N+1 queries in resolvers, planning a deprecation, hardening a public graph.
Mobile Developer
Use this agent to build cross-platform mobile apps with React Native + Expo — screens, navigation, native modules, and shipping via EAS. Examples — adding a tab-based navigation flow, fixing a janky FlatList, shipping a build to TestFlight with EAS.
System Architect
Use this agent for high-level system design — service boundaries, data flow, scaling, trade-offs. Examples — designing a new system, evaluating a monolith-to-services split, a scalability review.
Agent Tool Integration Engineer
Use this agent to wire tools and function-calling into an agent loop reliably — clean tool schemas, errors fed back as observations, retries with limits, idempotency, and parallel calls. Examples — "connect our APIs as agent tools", "our agent calls tools wrong / ignores tool errors", "add function-calling with proper error recovery to our agent".
Browser Agent Engineer
Use this agent to build, harden, or debug browser-automation agents — web tasks via Browser Use, Stagehand, Skyvern, or Playwright-based stacks. Examples: automate a portal workflow, make a flaky browser agent reliable, add verification and guardrails to web automation, choose between vision and DOM grounding.
Data Engineer
Use this agent to build and maintain data pipelines — ingestion, ELT/ETL, warehouse modeling, orchestration, and data-quality tests. Examples — building an idempotent ingestion job, modeling a fact/dimension table in dbt, writing a safe backfill for a changed schema.
Data Scientist
Use this agent for data analysis — exploration, statistics, SQL, and clear findings. Examples — analyzing a dataset, writing an analytical SQL query, summarizing experiment results.
Finetuning Engineer
Use this agent to fine-tune an open-weight model end to end — confirming fine-tuning is the right tool, preparing the dataset, choosing the method (LoRA/QLoRA vs. full), running training, and proving the result beats the prompted baseline on a held-out eval set. Examples — "fine-tune a small model to match our support tone and answer format", "we have 800 labeled examples — LoRA-tune and show it beats prompting", "our fine-tune overfits and forgot general ability — fix the data and run".
LLM Cost Optimizer
Use this agent to cut the cost and latency of an application's LLM API usage without losing quality — audit where the tokens and dollars go, then apply caching, model right-sizing, prompt trimming, batching, and budgets, proven against an eval bar. Examples — "our OpenAI bill tripled, find where the spend is and cut it", "this endpoint's p95 is 8s, bring it down", "right-size models per task and add prompt caching to our chat feature".
LLM Evaluation Engineer
Use this agent to make an LLM feature's quality measurable — building the dataset, choosing metrics, setting a baseline, and turning evals into a CI gate so prompt and model changes are scored, not guessed. Examples — "we changed the prompt and don't know if it's better, set up evals", "add a regression gate for our extraction feature", "our RAG quality is drifting, build an eval suite".
LLM Inference Engineer
Use this agent to serve and optimize self-hosted LLM inference — sizing GPUs, configuring a serving engine like vLLM (continuous batching, PagedAttention, tensor parallelism), applying quantization, and tuning throughput and tail latency against a cost and p95 budget. Examples — "serve Llama-3-70B at p95 under 2s on our GPUs", "our self-hosted model is slow and the GPUs sit half-idle — raise throughput", "quantize this model to fit one GPU without wrecking quality".
LLM Integration Engineer
Use this agent to add an LLM feature to an application and make it production-grade — typed/structured output, streaming, provider fallback and retries, caching, and cost/latency controls. Examples — "add an AI summary endpoint to our app", "our LLM calls return unparseable JSON and break, make them reliable", "add streaming and a fallback provider to our chat feature".
LLM Observability Engineer
Use this agent to make a production LLM app observable — tracing every step, scoring live traffic with online evals, and monitoring quality, cost, and latency — so you can debug agent runs and catch regressions in production. Examples — "add tracing to our RAG/agent so we can debug bad answers", "set up online evals and cost/latency dashboards", "production quality is slipping and we're flying blind".
ML Engineer
Use this agent for production ML — pipelines, training, serving, evaluation, and MLOps. Examples — building a training pipeline, deploying a model, setting up evaluation.
Postgres Migration Engineer
Use this agent to plan and execute a zero-downtime Postgres schema migration — decomposing a breaking change into expand-contract steps, writing batched backfills, building indexes CONCURRENTLY, validating constraints online, and keeping every step reversible with the project's migration tooling. Examples — "add a NOT NULL column to a 200M-row table without downtime", "rename a column safely across a rolling deploy", "split this risky migration into reversible expand/contract steps".
Prompt Engineer
Use this agent to design and iterate the prompts behind an LLM-powered product feature — instructions, few-shot examples, tool schemas, and the evals that prove a change actually helped. Examples — "this classification prompt is flaky, make it reliable", "design the system prompt and function schema for our support agent", "our extraction prompt regressed after I tweaked it, set up evals so this stops happening".
Rag Pipeline Engineer
Use this agent to design, build, and harden a production retrieval-augmented generation (RAG) pipeline end to end — ingestion, chunking, embeddings, indexing, retrieval, reranking, and grounded generation — with evals that prove each stage works. Examples — "stand up RAG over our docs", "our RAG hallucinates and misses obvious answers, fix the pipeline", "take our prototype RAG to production with evals and citations".
Retrieval Engineer
Use this agent to raise the retrieval quality of a search or RAG system — recall and precision, hybrid (dense + sparse) search, reranking, query transformation, and metadata filtering — measured against a labeled eval set. Examples — "our RAG retrieves irrelevant chunks, fix recall", "add hybrid search and reranking and prove it helps", "queries with acronyms/IDs return nothing, fix it".
Vector Search Engineer
Use this agent to design, build, and tune the vector-database layer of a search or RAG system — schema and index design (HNSW/IVF + quantization), metadata/payload filtering, hybrid (dense + sparse) search, and ingestion/upsert pipelines — sized to a real latency, recall, and cost budget. Examples — "set up pgvector for our docs with HNSW and filtered search", "our Qdrant queries are slow and recall dropped after quantization", "add metadata filtering so search only returns the current tenant's documents".
Voice Agent Engineer
Use this agent to build or fix a real-time voice agent — the streaming STT → LLM → TTS pipeline, conversational (mouth-to-ear) latency, turn-taking, barge-in/interruptions, and per-stage provider selection. Examples — "our voice bot feels laggy and talks over people, fix the turn-taking and latency", "build a phone agent that transcribes, answers with our LLM, and speaks back", "get our voice agent's response time under a second".
CLI Tooling Engineer
Use this agent to design or build a command-line tool — subcommand and flag layout, --help and error UX, exit codes, --json/machine output, config precedence, stdin/stdout/stderr and pipe behavior, TTY/color/NO_COLOR detection, and CLI testing. Examples — "design the command and flag surface for our new deploy CLI", "this tool prints errors to stdout and returns 0 on failure — fix its ergonomics", "make our command pipe-friendly and add a --json mode for CI".
Dependency Manager
Use this agent to upgrade project dependencies safely — batching low-risk bumps apart from breaking majors and verifying each step. Examples — clearing months of stale packages, taking a single major version with migration notes, resolving a peer-dependency conflict.
Documentation Engineer
Use this agent to write and maintain technical docs that stay true to the code — READMEs, how-to guides, API references, and runbooks. Examples — updating a stale README after a refactor, documenting a new public API from its signatures, writing an on-call runbook for a service.
Git Github Expert
Use this agent for Git and GitHub workflows — rebases, conflict resolution, history surgery, PRs, and Actions. Examples — resolving a messy merge, rewriting history safely, fixing a workflow file.
MCP Server Engineer
Use this agent to build, harden, or productionize a Model Context Protocol (MCP) server — designing tools/resources/prompts, choosing stdio vs. Streamable HTTP, taking a server remote with OAuth and stateless scaling, and testing it with the MCP Inspector. Examples — "wrap our internal API as an MCP server with three tools", "take our stdio server remote so the team can share it", "our tools confuse the model — fix the names, schemas, and descriptions".
Refactoring Specialist
Use this agent to safely restructure code without changing behavior — extracting, renaming, decoupling. Examples — breaking up a god object, removing duplication, improving testability.
CI/CD Engineer
Use this agent to design, speed up, and harden CI/CD pipelines on any provider (GitHub Actions, GitLab CI, CircleCI, Buildkite). Examples — setting up a build→test→deploy pipeline from scratch, cutting a 25-minute CI run down with caching and matrix parallelism, adding a canary or blue-green deploy with automatic rollback, or reviewing a workflow for leaked secrets, over-broad tokens, and unpinned third-party actions.
Cloud Architect
Use this agent to design a cloud architecture on AWS, GCP, or Azure — compute, networking, data stores, IAM, and cost trade-offs. Examples — choosing serverless vs containers for a new service, designing a multi-account network boundary, picking a database and estimating its monthly cost.
DevOps Engineer
Use this agent for CI/CD, infrastructure, and automation. Examples — writing a CI pipeline, containerizing an app, infrastructure-as-code changes.
Incident Responder
Use this agent during a live production incident to restore service fast and learn from it — triage and severity, mitigation-first action (roll back, fail over, shed load), change correlation, status updates, and the blameless postmortem. Examples — an alert just fired and the API is 5xx-ing, a deploy broke checkout and you need to decide rollback vs. forward-fix, latency is climbing and the pager is going off, or you're writing the postmortem the morning after.
Kubernetes Specialist
Use this agent for Kubernetes — manifests, Helm, troubleshooting, scaling, and resource tuning. Examples — debugging a CrashLoopBackOff, writing a Deployment, tuning requests/limits.
SRE Engineer
Use this agent to make reliability measurable: SLIs/SLOs and error budgets, observability, symptom-based alerting, incident response, and capacity. Examples — defining an SLO for a checkout API, fixing a noisy pager, writing a blameless postmortem.
Terraform Specialist
Use this agent for Terraform and infrastructure-as-code — module design, remote state, plan/apply safety, drift, and provider pinning. Examples — reviewing a plan for destroys before apply, designing a reusable module, resolving state drift after a console change.
C# Pro
Use this agent for modern C#/.NET 8+ — records, pattern matching, nullable reference types, correct async/await, LINQ, Span<T>, and source generators — plus ASP.NET Core and EF Core. Examples — building a minimal-API service, fixing an EF Core N+1 or tracking leak, hunting a deadlock from sync-over-async, or turning on nullable reference types across a project.
Golang Pro
Use this agent for idiomatic Go — concurrency, errors, small interfaces, stdlib-first design, and profiling. Examples — fixing a goroutine leak, designing a context-aware API, profiling a hot path with pprof.
Java Pro
Use this agent for idiomatic, modern Java (17/21+) — records, sealed types, pattern matching, virtual threads and structured concurrency, the Streams API, and JVM/GC performance. Examples — modernizing a legacy POJO-and-thread-pool service to records and virtual threads, diagnosing a GC pause or allocation hotspot, reviewing concurrency correctness, or fixing a Spring Boot service that blocks the wrong threads.
Kotlin Pro
Use this agent for idiomatic Kotlin — null safety, coroutines and structured concurrency, Flow, sealed classes with exhaustive when, data classes, and extension functions — on Android and the JVM. Examples — fixing a coroutine leak, replacing callbacks with Flow, removing !! null-safety holes.
Php Pro
Use this agent for idiomatic, modern PHP 8.3+ — strict types, enums, readonly and promoted properties, Composer/PSR-4 autoloading, and safe PDO data access. Examples — modernizing a PHP 5-era class, killing an ORM N+1, hardening a query against SQL injection.
Python Pro
Use this agent for idiomatic, performant Python — typing, async, packaging, and stdlib mastery. Examples — refactoring to idiomatic Python, async I/O, packaging a library.
React Specialist
Use this agent for React architecture — hooks, state, performance, Server Components, and patterns. Examples — fixing re-render issues, designing component state, adopting RSC.
Rust Pro
Use this agent for idiomatic Rust — ownership, lifetimes, error handling, traits, async with tokio, and the cargo toolchain. Examples — fixing borrow-checker errors, designing a trait API, making async code compile cleanly under tokio.
SQL Pro
Use this agent for SQL itself — correct joins and window functions, indexing, EXPLAIN plans, schema design, and safe migrations on Postgres/MySQL. Examples — making a slow query fast, designing a normalized schema, writing a reversible migration.
Swift Pro
Use this agent for modern Swift 6 — value semantics, optionals done right, async/await and actors, Sendable/data-race safety, and idiomatic SwiftUI. Examples — fixing a data race under strict concurrency, untangling force-unwrap crashes, making a SwiftUI list scroll smoothly.
Typescript Pro
Use this agent for advanced TypeScript — generics, type-level programming, strictness, and inference. Examples — typing a tricky API, fixing type errors, designing a type-safe library surface.
Agent Architect
Use this agent to design a new Claude Code subagent or review an existing one — scoping, description, toolset, model, and output contract. Examples — "design an agent that triages flaky tests", "review my code-reviewer agent for scope creep", "why won't Claude auto-delegate to my agent?".
Agent Reliability Reviewer
Use this agent to make an AI agent production-ready — reviewing its loops, cost controls, error handling, tool use, human-in-the-loop gates, checkpointing, and observability, then reporting concrete failure modes and fixes. Examples — "is our agent safe to ship?", "our agent loops forever / burns tokens, harden it", "add guardrails and recovery before we put this agent in front of users".
Context Engineer
Use this agent to engineer what an LLM agent carries in its context window — deciding what to include vs exclude vs retrieve on demand, designing project/agent memory (CLAUDE.md), compacting growing history, and allocating the token budget across system prompt, memory, retrieved docs, tool results, and conversation. Examples — "my agent forgets the schema we agreed on three turns ago", "the agent gets dumber and more inconsistent as the chat grows", "we're burning 60k tokens of tool output every turn", "what should this support agent always know vs look up?".
Eval Driven Developer
Use this agent to drive AI feature development with evals the way TDD drives code with tests — define success criteria and a representative eval set BEFORE iterating on prompts/models, then optimize against measured scores instead of vibes. Examples — "make the summarizer better" (turn it into measurable criteria first), "our prompt change keeps regressing quality, set up a loop that catches it", "add an eval gate to CI so a model swap can't silently degrade output", "we tweak prompts and pray — give us a baseline and a change-by-change scoreboard".
Workflow Orchestrator
Use this agent to break large tasks into coordinated multi-step plans and delegate to other agents. Examples — planning a multi-file refactor, orchestrating a migration, decomposing an epic.
Accessibility Auditor
Use this agent to audit web UI against WCAG 2.2 AA — semantics, keyboard, ARIA, contrast, forms, and motion. Examples — auditing a new component for keyboard traps, checking a form for accessible errors, running a pre-ship a11y pass on a page.
Code Reviewer
Use this agent to review code changes for correctness, security, and maintainability before merging. Examples — reviewing a PR diff, auditing a new module, checking a refactor for regressions.
Debugger
Use this agent to diagnose failing tests, runtime errors, or unexpected behavior by forming and testing hypotheses. Examples — a stack trace to root-cause, a flaky test, a "works locally but not in CI" bug.
Performance Engineer
Use this agent to profile and optimize performance — latency, throughput, memory, bundle size. Examples — a slow endpoint, an N+1 query, a heavy render, a large JS bundle.
Prompt Injection Auditor
Use this agent to audit an LLM app or agent for prompt-injection exposure — mapping where untrusted content enters the model's context (user, RAG, tools, web), assessing the blast radius if an injection succeeds, probing with adversarial inputs, and recommending architectural mitigations. Examples — "audit our RAG agent for indirect prompt injection", "what's the blast radius if our agent gets injected — which tools and credentials are exposed?", "review our LLM app's trust boundaries and tell us what to fix".
QA Automation Engineer
Use this agent for end-to-end and UI test automation — building flake-resistant Playwright/Cypress suites, stabilizing flaky browser tests, structuring page objects and fixtures, and reviewing E2E suites. Examples — adding E2E coverage for a checkout or signup flow, killing a test that fails 1-in-5 in CI, choosing a framework and folder structure, replacing sleeps with web-first waits, or auditing a suite that's slow and brittle.
Security Auditor
Use this agent to find security vulnerabilities — injection, auth flaws, secrets, unsafe deserialization, dependency risks. Examples — auditing an API surface, reviewing auth code, pre-release security pass.
Test Engineer
Use this agent to write and improve automated tests — unit, integration, and edge cases. Examples — adding coverage to an untested module, writing regression tests for a bug, designing a test plan.
Frequently asked questions
- What is a Claude Code agent?
- A Claude Code agent (or subagent) is a specialized assistant defined by a Markdown file with a focused system prompt and its own isolated context window. Claude Code delegates matching tasks to it automatically, or you can invoke it by name.
- How do I install an agent?
- Copy or download the agent's Markdown file to ~/.claude/agents/<name>.md for all your projects, or .claude/agents/<name>.md inside a single project. Claude Code picks it up automatically — no restart required.
- Are these agents free?
- Yes. Every agent on AgentsCamp is free to copy, download, and use. They are open Markdown files with no signup or API key required.
- When should I use an agent instead of a skill or slash command?
- Use an agent when you want delegated, multi-step work to run in its own context window, such as a code review or a debugging pass. Use a skill for on-demand expertise loaded into the main conversation, and a slash command for a quick reusable prompt you trigger with /name.
- Do these agents work outside Claude Code?
- Agents follow Claude Code's subagent file format, so they install directly into Claude Code. The system prompts inside them can also be adapted for other agent frameworks.