adk-dev-guide
google/adk-docs
ADK development lifecycle, spec-driven workflow, and mandatory coding guidelines for agent implementation.
What is adk-dev-guide?
This skill provides the core development workflow for ADK agents, covering the four phases: understanding specs, building, evaluating, and deploying. It also establishes mandatory coding guidelines including code preservation, model selection rules, and troubleshooting practices. Read this at the start of any ADK agent development session.
- Defines the four-phase ADK development lifecycle: spec understanding, implementation, evaluation, and deployment
- Enforces code preservation and surgical precision when modifying existing code
- Establishes model selection rules (use Gemini 3 series for new agents, never change models unless explicitly requested)
- Provides mandatory evaluation workflow using evalsets and scoring metrics before deployment
- Includes troubleshooting guidance for common issues like location misconfigurations and infinite loops
- References related skills for specific phases: adk-cheatsheet, adk-eval-guide, adk-deploy-guide, adk-scaffold
How to install adk-dev-guide
npx skills add https://github.com/google/adk-docs --skill adk-dev-guideHow to use adk-dev-guide
- 1.Read DESIGN_SPEC.md first to understand functional requirements, success criteria, and constraints
- 2.Implement agent logic using the appropriate framework guidance file (GEMINI.md or CLAUDE.md)
- 3.Use make playground or adk web for interactive testing during development
- 4.Activate adk-eval-guide and run adk eval with 1-2 sample cases to validate behavior
- 5.Iterate on core evaluation cases until quality thresholds are met
- 6.Consult adk-deploy-guide and obtain explicit human approval before deploying
Use cases
- Starting a new ADK agent development session to understand the required workflow
- Implementing agent logic while preserving existing code and configuration
- Running evaluations to validate agent behavior against success criteria before deployment
- Troubleshooting common development issues like model 404 errors or infinite loops
- Deciding when to use Gemini 3 models versus older model versions
- ADK agent developers building new agents or modifying existing ones
- Teams implementing spec-driven agent development workflows
- Developers deploying agents to production who need approval checkpoints
adk-dev-guide FAQ
Never change the model unless explicitly asked by the user. If you encounter a 404 error, it's almost always a GOOGLE_CLOUD_LOCATION issue, not a model problem. Fix the location instead.
Use Gemini 3 series models: gemini-3-flash-preview or gemini-3-pro-preview. Do not use older models like gemini-2.0-flash or gemini-1.5-flash unless explicitly requested.
Tests (pytest) validate code correctness but say nothing about agent behavior. Evaluation (adk eval) validates agent behavior end-to-end using evalsets and scoring metrics. Always run adk eval before deployment.
Import the tool instance directly: from google.adk.tools.load_web_page import load_web_page, then pass it as tools=[load_web_page]. Do not import the module and then access the tool through it.
Stop immediately and fix the root cause instead of retrying. Check for state conflicts (Error 409), tool bugs in source code, or location/configuration issues. Run underlying commands directly if the tool is problematic.
Full instructions (SKILL.md)
Source of truth, from google/adk-docs.
name: adk-dev-guide description: > ALWAYS ACTIVE — read at the start of any ADK agent development session. ADK development lifecycle and mandatory coding guidelines — spec-driven workflow, code preservation rules, model selection, and troubleshooting. metadata: license: Apache-2.0 author: Google
ADK Development Workflow & Guidelines
Session Continuity
If this is a long session, re-read the relevant skill before each phase —
/adk-cheatsheet before writing code, /adk-eval-guide before running evals,
/adk-deploy-guide before deploying, /adk-scaffold before scaffolding.
Context compaction may have dropped earlier skill content.
DESIGN_SPEC.md — Your Primary Reference
IMPORTANT: If DESIGN_SPEC.md exists in this project, it is your primary source of truth.
Read it FIRST to understand:
- Functional requirements and capabilities
- Success criteria and quality thresholds
- Agent behavior constraints
- Expected tools and integrations
The spec is your contract. All implementation decisions should align with it. When in doubt, refer back to DESIGN_SPEC.md.
Phase 1: Understand the Spec
Before writing any code:
- Read
DESIGN_SPEC.mdthoroughly - Identify the core capabilities required
- Note any constraints or things the agent should NOT do
- Understand success criteria for evaluation
Phase 2: Build and Implement
Implement the agent logic:
- Write/modify code in the agent directory (check the agent guidance file, e.g. GEMINI.md or CLAUDE.md, for directory name)
- Use
make playground(oradk web .) for interactive testing during development - Iterate on the implementation based on user feedback
For ADK API patterns and code examples, use /adk-cheatsheet.
Phase 3: Evaluate
This is the most important phase. Evaluation validates agent behavior end-to-end using evalsets and scoring metrics.
MANDATORY: Activate /adk-eval-guide before running evaluation. It contains the evalset schema, config format, and critical gotchas. Do NOT skip this.
Tests (pytest) are NOT evaluation. They test code correctness but say nothing about whether the agent behaves correctly. Always run adk eval.
- Start small: Begin with 1-2 sample eval cases, not a full suite
- Run evaluations:
adk eval(ormake evalif the project has a Makefile) - Discuss results with the user
- Fix issues and iterate on the core cases first
- Only after core cases pass, add edge cases and new scenarios
- Repeat until quality thresholds are met
Expect 5-10+ iterations here.
Phase 4: Deploy
Once evaluation thresholds are met:
- Deploy when ready — see
/adk-deploy-guidefor deployment options
IMPORTANT: Never deploy without explicit human approval.
Operational Guidelines for Coding Agents
Principle 1: Code Preservation & Isolation
When executing code modifications, your paramount objective is surgical precision. You must alter only the code segments directly targeted by the user's request, while strictly preserving all surrounding and unrelated code.
Mandatory Pre-Execution Verification:
Before finalizing any code replacement, verify:
- Target Identification: Clearly define the exact lines or expressions to be changed, based solely on the user's explicit instructions.
- Preservation Check: Ensure all code, configuration values (e.g.,
model,version,api_key), comments, and formatting outside the identified target remain identical.
Example:
- User Request: "Change the agent's instruction to be a recipe suggester."
- Incorrect (VIOLATION):
root_agent = Agent( name="recipe_suggester", model="gemini-1.5-flash", # UNINTENDED - model was not requested to change instruction="You are a recipe suggester." ) - Correct (COMPLIANT):
root_agent = Agent( name="recipe_suggester", # OK, related to new purpose model="gemini-3-flash-preview", # PRESERVED instruction="You are a recipe suggester." # OK, the direct target )
Principle 2: Execution Best Practices
-
Model Selection — CRITICAL:
- NEVER change the model unless explicitly asked. If the code uses
gemini-3-flash-preview, keep it asgemini-3-flash-preview. Do NOT "upgrade" or "fix" model names. - When creating NEW agents (not modifying existing), use Gemini 3 series:
gemini-3-flash-preview,gemini-3-pro-preview. - Do NOT use older models (
gemini-2.0-flash,gemini-1.5-flash, etc.) unless the user explicitly requests them.
- NEVER change the model unless explicitly asked. If the code uses
-
Location Matters More Than Model:
- If a model returns a 404, it's almost always a
GOOGLE_CLOUD_LOCATIONissue (e.g., needingglobalinstead ofus-central1). - Changing the model name to "fix" a 404 is a violation — fix the location instead.
- Some models (like
gemini-3-flash-preview) require specific locations. Check the error message for hints.
- If a model returns a 404, it's almost always a
-
ADK Built-in Tool Imports (Precision Required):
# CORRECT - imports the tool instance from google.adk.tools.load_web_page import load_web_page # WRONG - imports the module, not the tool from google.adk.tools import load_web_pagePass the imported tool directly to
tools=[load_web_page], nottools=[load_web_page.load_web_page]. -
Running Python Commands:
- Always use
uvto execute Python commands (e.g.,uv run python script.py) - Run
make install(oruv sync) before executing scripts - Consult
MakefileandREADME.mdfor available commands (if present)
- Always use
-
Breaking Infinite Loops:
- Stop immediately if you see the same error 3+ times in a row
- Don't retry failed operations — fix the root cause first
- RED FLAGS: Lock IDs incrementing, names appending v5->v6->v7, "I'll try one more time" repeatedly
- State conflicts (Error 409: Resource already exists): Import existing resources with
terraform importinstead of retrying creation - Tool bugs: Fix source code bugs before continuing — don't work around them
- When stuck: Run underlying commands directly (e.g.,
terraformCLI) instead of calling problematic tools
-
Troubleshooting:
- Check
/adk-cheatsheetfirst — it covers most common patterns - Search the installed ADK package with Glob/Grep/Read (find it with
python -c "import google.adk; print(google.adk.__path__[0])"— useuv run pythonif using uv) - For ADK docs index, use
curl https://adk.dev/llms.txt - For framework questions or GCP products, check official documentation
- When encountering persistent errors, a targeted Google Search often finds solutions faster
- Check
Scaffold as Reference
When you need specific infrastructure files but don't want to scaffold the current project directly, use the /adk-scaffold skill to create a temporary reference project in /tmp/ and copy over what you need.
See /adk-scaffold for full CLI commands and options.
Development Commands
Projects created via the Agent Starter Pack CLI include a Makefile with these commands.
To create or enhance a project, see the /adk-scaffold skill for full instructions.
For non-scaffolded projects, use the ADK CLI equivalents.
| Make (scaffolded) | ADK CLI equivalent | Purpose |
|---|---|---|
make playground | adk web . | Interactive local testing |
make test | pytest | Run unit and integration tests |
make eval | adk eval <agent_dir> <evalset> | Run evaluation against evalsets |
make lint | ruff check . | Check code quality |
make setup-dev-env | — (scaffolded only) | Set up dev infrastructure (Terraform) |
make deploy | — (scaffolded only) | Deploy to dev (requires human approval) |
Skills Version
Troubleshooting hint: If you encounter repeated errors, unexpected tool failures, or instructions in these skills that seem incomplete or reference features you can't find, the skills may be outdated. Run:
npx skills check -gIf it reports outdated skills, tell the user to update:
npx skills update -gDo NOT run this check on every session — only when you suspect stale skills are causing problems.
Related skills
More from google/adk-docs and the wider catalog.

adk-eval-guide
ADK evaluation methodology guide — metrics, evalsets, LLM-as-judge, and debugging eval failures.

adk-observability-guide
Configure monitoring, tracing, and logging for ADK agents—Cloud Trace, prompt-response logging, BigQuery analytics, and third-party integrations.

adk-scaffold
Scaffold new ADK agent projects or enhance existing ones with deployment, CI/CD, and infrastructure templates.

adk-cheatsheet
Python ADK API quick reference for agent types, tools, orchestration, and state management.

google-agents-cli-adk-code
Quick reference for ADK Python API patterns, tools, callbacks, and agent code examples.

google-agents-cli-deploy
Deploy ADK agents to Agent Runtime, Cloud Run, or GKE with managed infrastructure and CI/CD.