adk-samples

Performs a strict evaluation of a video asset using Google's official 'ABCD' framework (Attract, Brand, Connect, Direct) based on transcript and metadata.

Aligns a Python recipe's pyproject.toml with the repo's standards enforced by .github/workflows/python-validate-recipe.yml, plus one critical [build-system] presence check. Scope is pyproject.toml only — standalone ruff.toml / .ruff.toml files (also forbidden in recipes) are caught by the CI workflow instead, not by this skill. Runs in two modes: a read-only --dry-run that reports what needs alignment, and an apply mode that rewrites pyproject.toml (and optionally manifest.yaml) using comment-preserving TOML/YAML editors. Use when the user wants to "align the recipe's pyproject.toml", "fix pyproject to match the repo standard", "check what needs changing in a recipe's pyproject", or clean up a recipe before submitting a PR.

Blog post writing skill with structure templates and style guidelines. Guides the agent through writing well-structured, engaging technical blog posts with proper formatting, section flow, and reader engagement techniques.

Install/auth CLIs the sandbox lacks - `gws` (Drive, Gmail, Sheets, Calendar), `gcloud`, `agents-cli`, `mcp-cli` (MCP servers). Use on "command not found" or before GCP/Workspace/MCP work.

Content research and SEO writing methodology. Guides the agent through topic research, keyword identification, competitive analysis, and writing SEO-optimized content that ranks well and provides genuine value to readers.

Deconstructs high-performing or viral videos to extract actionable creative insights from metadata and transcript.

Provides a high-signal briefing on events in a specific location and timeframe, backed by primary video sources and transcripts.

Extracts the strongest arguments from heated YouTube comment threads, identifying key battlegrounds and community consensus.

Saves time by autonomously reading transcripts, synthesizing arguments, and generating direct jump-links to key moments.

Scans a Python recipe to find every place an environment variable is accessed — including `os.environ.setdefault("V", "d")`, whose "d" would otherwise be a hidden default a user editing .env.example has no way to discover — then ensures all variables are declared in `.env.example`, that `load_dotenv()` is bootstrapped in the package `__init__.py`, and that `python-dotenv>=1.0.0` is listed in `pyproject.toml`. When a new entry is added to `.env.example`, the extracted default from source is written as the value (with a `# extracted-by:extract-env-vars` marker and provenance comment); values that look like stubs (`"my-project-id"`, `"changeme"`, `"<...>"`) are downgraded to the TODO placeholder but the source string is preserved in the marker comment. Also detects hardcoded model-name string literals (e.g. `"gemini-3.5-flash"` in `agent.py`) and rewrites them to an `os.getenv(...)` call. The variable name is derived from the assignment target when it names a model (`DEFAULT_EMBEDDING_MODEL` → `EMBEDDING_MODEL`), else `MODEL_NAME` (single model) or `MODEL_NAME_GENERATED_1` / `MODEL_NAME_GENERATED_2`, … (multiple models). Normally no fallback default is written into the Python source; the one exception is when no `load_dotenv()` bootstrap could be installed (no package `__init__.py`), where the original literal is kept as the fallback so the lookup cannot evaluate to `None` at runtime. The model string is written as the value in `.env.example` with a comment prompting a rename. When re-run against a recipe whose `.env.example` already has entries, the writer classifies each entry (skill-authored vs. user-authored, TODO vs. real value) and only rewrites lines it can prove it authored; user-authored lines are always preserved. A stale TODO (either skill-authored or a bare v1-era `<TODO: update-this-value>` with no inline comment) is upgraded in place when source can supply a real default. IMPORTANT — two hard rules the skill NEVER breaks: (1) USER-EDIT SAFETY. Any `.env.example` line the skill cannot prove it authored is USER_OWNED and is never modified. The rewriter fails closed on any structural ambiguity (quoted values, backslash continuation, duplicate declarations) — a stale TODO left in place is cheap; a clobbered user edit is not. (2) ADDITIVE-ONLY FOR PYTHON FILES. The skill never writes new `os.environ.setdefault(...)` bootstrap lines into any Python file. Pre-existing `os.environ.setdefault(...)` or `os.getenv("VAR", "default")` calls that the recipe author wrote by hand are LEFT UNTOUCHED — the skill's only writes to Python files are (a) the `load_dotenv()` bootstrap; (b) `# noqa: E402` on trailing relative imports that would otherwise trip Ruff; (c) hardcoded model-literal replacement. Use when the user wants to "extract env vars", "update .env.example", "add load_dotenv", "surface setdefault defaults", "upgrade stale TODOs in .env.example", "replace hardcoded model names", or "fix environment variables" in a Python recipe.

Discover/install NEW agent skills from external sources (skills.sh). Use for capabilities you lack, e.g. "find a skill for X". Do NOT use to list skills you already have; see `<available_skills>`.

Scan an ADK recipe directory and generate a manifest.yaml for it based on the schema at .github/schemas/manifest-schema.json. Use when the user wants to create or generate a manifest.yaml for a recipe under core/, contrib/, or skills/.

Generates a lightweight `tests/test_runnability.py` for a Python recipe. The test just imports the recipe's agent module and asserts that `root_agent is not None` (and `app is not None` if the module defines one). The skill parses agent.py with `ast` to figure out which import-time side effects need mocking (`vertexai.init`, `google.auth.default`) and which env vars need setting (`GOOGLE_CLOUD_PROJECT`, `INTEGRATION_TEST`), and only emits the boilerplate the recipe actually needs. Runs in dry-run (report + preview) and apply (write to disk) modes. Use when the user wants to "add a runnability test", "generate test_runnability.py", "create a smoke test for the recipe", or fix the missing-required-file failure from `python-validate-recipe.yml`.

Read/write Google Drive, Docs, Sheets, Gmail, Calendar, Chat, Tasks, Slides via the `gws` CLI. Needs an OAuth client or service account configured first. Use for any Google Workspace task.

Equips sellers with macro industry trends by analyzing trending data and specific analyst/competitor channels.

Identifies and ranks Key Opinion Leaders (KOLs) based on engagement metrics, active rate, and sentiment rather than just views.

Makes an existing Python recipe deployable: generates the serving files a container needs (Dockerfile, .dockerignore, fast_api_app.py, app_utils/a2a.py, app_utils/services.py, app_utils/reasoning_engine_adapter.py) and configures the recipe to match (required serving dependencies, the App object in agent.py, the hatch wheel package, manifest.deployable). Interactive by design — it asks the recipe owner about runtime data directories and stops for a human decision when a recipe needs an ADK migration or carries a legacy app_utils generation. When docker is available it offers to PROVE the claim: it builds the generated Dockerfile, runs it, probes it, and refuses to flag a recipe deployable if the container does not come up. Does NOT deploy or write terraform. Use when the user wants to "make this recipe deployable", "add a Dockerfile to a recipe", "add the serving files", "containerize a recipe", "verify the container builds", or prepare a recipe for Cloud Build / Artifact Registry.

Performs high-density targeted extraction across 10+ videos to map semantic landscapes, consensus, and controversies.

Extracts specific local activity recommendations and sentiment from travel vlogs into a shareable HTML BD report.

Inspect the per-workspace tool-policy overlay (.lha/policies.jsonl) that gates destructive or sensitive tool calls, and tell the user what to change if they want a rule added or removed.

End-to-end orchestration to prepare or update a Python recipe under core/python/, contrib/python/, or skills/<vertical>/<solution>/ so it passes every check in .github/workflows/python-validate-recipe.yml. Runs eight phases in order on an already-in-place recipe: manifest.yaml generation, environment-variable extraction, pyproject.toml alignment, ruff format+check, per-recipe `uv lock`, runnability-test generation, compile-and-run verification of the generated test file, and a final pass through the repo's own `validate manifest` / `validate structure` validators. Assumes the user has already done the manual prep (deactivated any venv, `git pull` and `uv sync` from the repo root, placed the recipe at its target path, renamed if needed). Delegates to the existing sub-skills (generate-manifest, extract-python-environment-variables, align-recipe-pyproject, generate-python-runnability-test) so the master never duplicates their logic. Pauses at fixed decision points (description mismatch, existing test regeneration) AND is free to interrupt for clarification any time a phase's output looks ambiguous, unexpected, or would benefit from a human judgment call — this is an interactive skill by design. Use when the user wants to "prepare a recipe", "update a recipe end to end", "run all the checks and fixes", "make this recipe PR-ready", or invokes it by name.

Provides an executive dashboard comparing Creator vs Audience verdicts for a recent client product launch.

Answer questions about how the adk-samples repo itself is governed — CI and workflow behavior, the limits and thresholds in .github/policy.yml and the reasoning behind them, CODEOWNERS routing, what the bots (stale sweep, Dependabot, recipe canary, AI review) do, how the repo is organised (core vs contrib vs skills verticals), what the repo skills and validators cover, when a rule last changed and which PR changed it, what a label means, and the admin runbooks for changing any of it. Also answers generic contribution-process questions from docs/ — how a recipe is prepared and validated, what a field in manifest.yaml means, what a runnability test is, what a README must contain, what a named CI error means. On explicit request it also traces which files consume a config key, and audits whether the repo still obeys its own policy. STRICTLY READ-ONLY — it never edits, commits, comments, labels, or performs a task on the caller's behalf, even when asked directly; it cites the source file and describes the change for a human to make. A request to do the work is answered rather than obeyed — it says it is read-only, names the skill that does the work, and gives the guidance. Use when someone says "oracle", or asks how/why/who about this repo's own configuration, CI, governance, or layout. Don't use for writing or preparing a recipe (use prepare-python-recipe, generate-manifest, align-recipe-pyproject and friends), for ADK API questions (use the google-agents-cli-* skills), or for anything needing the caller's screen — a failing check on their PR, their working tree, their branch.

Creates product search agents with semantic search and RAG on Google Cloud (Vertex AI Vector Search, BigQuery, embeddings). Use when the user wants to "build a product search agent", "create an e-commerce search", "make a shopping assistant", "set up semantic catalog discovery", "ingest products into Vector Search", or "deploy a retail RAG agent". Handles the full pipeline: catalog data ingestion to BigQuery, Vertex AI Vector Search collection setup, ADK agent scaffolding, evaluation, and Cloud Run deployment.

Creates virtual try-on agents supporting image and video (catwalk animation) try-ons on Google Cloud (Gemini image models and Veo on Gemini Enterprise Agent Platform). Handles resource setup, user photo uploading, image/video generation pipelines, local testing, and evaluation.