adk

Name: adk
Author: GoogleCloudPlatform

78stars64forks

How to install adk?

googlecloudplatform/cxas-scrapi/adk

$npx -y skills add GoogleCloudPlatform/cxas-scrapi --skill adk

Installs into the current project.

Summary

Parsing and ingestion directives for the Python-based Agent Development Kit (ADK) conversational agent framework.

Files · 1

View on GitHub

SKILL.md

1	# ADK Framework Ingestor Skill
2
3	This skill standardizes how agents ingest, parse, and extract conversational
4	behaviors and Critical User Journeys (CUJs) from Python workspaces built on the
5	Agent Development Kit (ADK) framework.
6
7	--------------------------------------------------------------------------------
8
9	## 1. ADK Workspace Layout
10
11	An ADK workspace typically consists of multiple decoupled microservices, each
12	containing its own python project. The structure below is illustrative; actual
13	directory and file names may vary:
14
15	```
16	<workspace_root>/
17	├── <service_name>/ # Individual project directory (e.g., router, auth, useraccount)
18	│ ├── main.py # Fast API or WebSocket entry point
19	│ ├── ReadMe.md # Setup and configuration details
20	│ ├── pyproject.toml # Dependency list
21	│ ├── vitals.yaml # Health check parameters
22	│ └── app/ # Core application module
23	│ ├── agents/ # Individual conversational agent definitions
24	│ │ └── <agent_name>/
25	│ │ ├── agent.py # Configures the agent, lists tools, and declares child agents
26	│ │ ├── prompt.py # Defines raw prompt strings and formatting logic
27	│ │ └── tools.py # Implements agent-specific tool methods
28	│ ├── config/ # Environment configuration module
29	│ │ ├── app.py # General settings and global prompts
30	│ │ └── state.py # Defines state machine keys and initializer dictionaries
31	│ └── services/ # Back-end service integrations
32	```
33
34	--------------------------------------------------------------------------------
35
36	## 2. Ingestion & Parsing Directives
37
38	Unlike declarative frameworks, ADK agent behaviors are defined procedurally in
39	Python. The parser MUST dynamically discover and extract conversational behaviors
40	following these directives (do not assume the specific names in the examples below
41	are present in the target codebase):
42
43	### A. Agent Registry Parsing
44
45	1. Root Agent Discovery: Identify the primary entry agent by inspecting the
46	application entry points (e.g., `main.py` or the main router service). Locate
47	its definition file (typically under `app/agents/<root_agent_name>/agent.py`)
48	and extract the root agent class declaration.
49	2. Sub-Agent Mapping: Trace how child agents are registered. Look for
50	dictionaries or lists mapping states to agents (common patterns include
51	variables like `state_agents`, `sub_agents`, or transition mappings) to
52	establish the agent hierarchy.
53	3. Registered Tools Identification: Map python tool functions passed to the
54	agent constructor (typically via a `tools=[...]` argument or decorator). Trace
55	their parameter structures in the corresponding `tools.py` or imported modules.
56	* Rule: If a python tool function invokes helper methods from an external
57	toolset (e.g., `tools.<toolset_name>_<operation>`), classify this tool as
58	a Webhook. Recursively extract its parameter/response schemas from the
59	associated OpenAPI specification (typically found in
60	`toolsets/<toolset_name>/open_api_toolset/open_api_schema.yaml` or similar).
61
62	### B. Prompt & Constraint Extraction
63
64	Read the prompt definition files (typically `prompt.py` or `prompts.py`) associated
65	with each discovered agent:
66
67	1. Primary Prompt Text: Locate the core prompt string variables containing
68	system instructions (e.g., variables ending in `_PROMPT`).
69	2. Custom Verbalization Rules: Extract programmatic formatting blocks or
70	string concatenations that append mandatory verbal instructions (e.g., rules
71	forcing the agent to relay messages verbatim or format specific outputs).
72
73	### C. State Machine & Variable Mapping
74
75	Because state transitions are written in Python, you MUST map the context
76	variables used for flow control (typically defined in `app/config/state.py` or
77	equivalent state configuration files):
78
79	1. Context Variables: Catalog all state keys or context variables (e.g.,
80	session variables, flags, or status codes) that act as triggers for branching.
81	2. Transition Conditions: Analyze the agent's decision logic (e.g., in
82	`callbacks.py` or transition handler methods) to map conditional checks
83	directing flows to other agents (e.g., checking if a user is authenticated
84	before transferring to a secure agent).
85
86	--------------------------------------------------------------------------------
87
88	## 3. Dialogue Simulation Guidelines
89
90	When simulating natural dialogue transcripts from parsed ADK models, follow
91	these guidelines:
92
93	1. Dialogue Entry Triggers: Start the dialogue with a `User` turn that
94	naturally triggers the entry conditions for the target state or agent being
95	t

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adk

78stars64forks

View on GitHub

How to install adk?

googlecloudplatform/cxas-scrapi/adk

$npx -y skills add GoogleCloudPlatform/cxas-scrapi --skill adk

Installs into the current project.

Summary

Parsing and ingestion directives for the Python-based Agent Development Kit (ADK) conversational agent framework.

Files · 1

View on GitHub

SKILL.md

1	# ADK Framework Ingestor Skill
2
3	This skill standardizes how agents ingest, parse, and extract conversational
4	behaviors and Critical User Journeys (CUJs) from Python workspaces built on the
5	Agent Development Kit (ADK) framework.
6
7	--------------------------------------------------------------------------------
8
9	## 1. ADK Workspace Layout
10
11	An ADK workspace typically consists of multiple decoupled microservices, each
12	containing its own python project. The structure below is illustrative; actual
13	directory and file names may vary:
14
15	```
16	<workspace_root>/
17	├── <service_name>/ # Individual project directory (e.g., router, auth, useraccount)
18	│ ├── main.py # Fast API or WebSocket entry point
19	│ ├── ReadMe.md # Setup and configuration details
20	│ ├── pyproject.toml # Dependency list
21	│ ├── vitals.yaml # Health check parameters
22	│ └── app/ # Core application module
23	│ ├── agents/ # Individual conversational agent definitions
24	│ │ └── <agent_name>/
25	│ │ ├── agent.py # Configures the agent, lists tools, and declares child agents
26	│ │ ├── prompt.py # Defines raw prompt strings and formatting logic
27	│ │ └── tools.py # Implements agent-specific tool methods
28	│ ├── config/ # Environment configuration module
29	│ │ ├── app.py # General settings and global prompts
30	│ │ └── state.py # Defines state machine keys and initializer dictionaries
31	│ └── services/ # Back-end service integrations
32	```
33
34	--------------------------------------------------------------------------------
35
36	## 2. Ingestion & Parsing Directives
37
38	Unlike declarative frameworks, ADK agent behaviors are defined procedurally in
39	Python. The parser MUST dynamically discover and extract conversational behaviors
40	following these directives (do not assume the specific names in the examples below
41	are present in the target codebase):
42
43	### A. Agent Registry Parsing
44
45	1. Root Agent Discovery: Identify the primary entry agent by inspecting the
46	application entry points (e.g., `main.py` or the main router service). Locate
47	its definition file (typically under `app/agents/<root_agent_name>/agent.py`)
48	and extract the root agent class declaration.
49	2. Sub-Agent Mapping: Trace how child agents are registered. Look for
50	dictionaries or lists mapping states to agents (common patterns include
51	variables like `state_agents`, `sub_agents`, or transition mappings) to
52	establish the agent hierarchy.
53	3. Registered Tools Identification: Map python tool functions passed to the
54	agent constructor (typically via a `tools=[...]` argument or decorator). Trace
55	their parameter structures in the corresponding `tools.py` or imported modules.
56	* Rule: If a python tool function invokes helper methods from an external
57	toolset (e.g., `tools.<toolset_name>_<operation>`), classify this tool as
58	a Webhook. Recursively extract its parameter/response schemas from the
59	associated OpenAPI specification (typically found in
60	`toolsets/<toolset_name>/open_api_toolset/open_api_schema.yaml` or similar).
61
62	### B. Prompt & Constraint Extraction
63
64	Read the prompt definition files (typically `prompt.py` or `prompts.py`) associated
65	with each discovered agent:
66
67	1. Primary Prompt Text: Locate the core prompt string variables containing
68	system instructions (e.g., variables ending in `_PROMPT`).
69	2. Custom Verbalization Rules: Extract programmatic formatting blocks or
70	string concatenations that append mandatory verbal instructions (e.g., rules
71	forcing the agent to relay messages verbatim or format specific outputs).
72
73	### C. State Machine & Variable Mapping
74
75	Because state transitions are written in Python, you MUST map the context
76	variables used for flow control (typically defined in `app/config/state.py` or
77	equivalent state configuration files):
78
79	1. Context Variables: Catalog all state keys or context variables (e.g.,
80	session variables, flags, or status codes) that act as triggers for branching.
81	2. Transition Conditions: Analyze the agent's decision logic (e.g., in
82	`callbacks.py` or transition handler methods) to map conditional checks
83	directing flows to other agents (e.g., checking if a user is authenticated
84	before transferring to a secure agent).
85
86	--------------------------------------------------------------------------------
87
88	## 3. Dialogue Simulation Guidelines
89
90	When simulating natural dialogue transcripts from parsed ADK models, follow
91	these guidelines:
92
93	1. Dialogue Entry Triggers: Start the dialogue with a `User` turn that
94	naturally triggers the entry conditions for the target state or agent being
95	t

People who liked this also viewed

6 picks

Type