Agent Development Kit is a Python framework for building, evaluating, deploying, and coordinating AI agents as software applications. Developers use it to compose workflows, delegate tasks between specialized agents, manage state, and connect agents to tools while choosing supported models and deployment environments. The catalogue add-ons provide workflows for using the toolkit.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add google/adk-python --skill adk-debuggit clone --depth 1 https://github.com/google/adk-pythonWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/google/adk-python/adk-debug)<a href="https://agentmods.dev/skills/google/adk-python/adk-debug"><img src="https://agentmods.dev/badge/skills/google/adk-python/adk-debug/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/google/adk-python/adk-debug"><img src="https://agentmods.dev/badge/skills/google/adk-python/adk-debug.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00191 | $0.00875 |
| Opus 5.5 | $0.00076 | $0.00350 |
| Sonnet 5.5 | $0.00038 | $0.00175 |
| Haiku 4.5 | $0.00019 | $0.00088 |
Grade A, and why
adk-debug scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugging ADK agents
Two entry points. Default to adk run: one process, no server, and --jsonl
output that pipes straight into grep or python3. Switch to adk web when
you need the browser UI, a persisted session you can click through, or the
trace endpoints that expose the exact LLM request.
First moves
- Reproduce headlessly:
adk run --jsonl {agent_dir} "{query}". Without--jsonl,adk runprints only text parts — tool calls and tool errors are invisible. - Read the log file.
adk runwrites to/tmp/agents_log/agent.latest.logand nothing to the terminal;adk webdoes the opposite. See logs-and-traces.md. - Match the symptom in failure-modes.md before reading source — most reports are one of a handful of known shapes.
- If the text is fine but the routing is not, dump the events and read
author,branch,nodeInfo.path, andactions— event-flow.md. - If the model itself misbehaved, read what it actually received from the
call_llmspan rather than guessing from the agent definition — logs-and-traces.md.
References
- cli-run.md —
adk runflags, the JSONL event shape, multi-turn and human-in-the-loop resume, exit codes, driving aRunnerfrom Python. - web-api.md — starting
adk web, listing and reading sessions over HTTP, posting test messages to/run_sse. - logs-and-traces.md — log levels and where each command writes them, the trace endpoints, span attributes, and the env vars that control whether prompts appear in spans.
- failure-modes.md — ADK-specific symptoms with the cause and a concrete check for each.
- event-flow.md — how an invocation becomes events, callback order, the event fields that matter, and where each stage lives in the source.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 67 lines · 191 tokens per session scan A 141e9442ee9f
adk-debug is a skill published in the GitHub repository google/adk-python (21,864 stars, last pushed today), licensed Apache-2.0. It adds 191 tokens to every session and 875 once invoked, about $0.0008 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
rubber-duck
Adversarial "rubber duck" review that turns explaining-out-loud into a hallucination check. The main session is the PRESENTER (it did the work — a design doc, investigation, or analysis — and holds the real reasoning) and reconstructs the topic to a LISTENER — a spawned subagent pinned to a DIFFERENT-vendor model that…
debug-gateway
Load this before concluding a change did not land, and to read what the kirocrew-debug tools actually answered - which question to ask first, the fields that mean the opposite of how they look, and what a 400 / 403 / 404 / 501 from these tools means.
ai-discover
Read-only discovery step of the auto-improvement loop. One bounded agent turn per cycle reads the focus files (Read/Grep/Glob only, no shell, no subagents) and replies with a JSON array of testable surfaces {file, line, symbol, rule, message, hypothesis}; the spine authors fixes and verifies them. Discovery only — no…
debugging-websocket-issues
Use when seeing WebSocket errors like "Invalid frame header", "RSV1 must be clear", or "WSERRUNEXPECTEDRSV1" - covers multiple WebSocketServer conflicts, compression issues, and raw frame debugging techniques.
api-debug
Debug REST, GraphQL, webhook, and OAuth/API-key integrations with layered request, auth, schema, and response checks.
rn-debugging
This skill should be used when the user asks to "debug the app", "fix a crash", "diagnose a blank screen", "read error logs", "troubleshoot CDP connection", "check Metro status", "find native crashes", "inspect network failures", "the app crashed", "I see a blank screen", "the screen is white", "something broke", "app…