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cxas-drawio-ingestor
Extracts conversational transcripts from .drawio XML files.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Extracts conversational transcripts from .drawio XML files.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
End-to-end GECX/CXAS/CES conversational agent lifecycle -- build agents from requirements (PRD-to-agent), create and run evals (goldens, simulations, tool tests, callback tests), debug failures, and iterate to production quality. Use this skill whenever the user mentions GECX, CXAS, CES, SCRAPI, conversational agents, voice agents, audio agents, agent evals, pushing/pulling/linting agents, or agent instructions/callbacks/tools on the Google Customer Engagement Suite platform.
Automates the ingestion of customer requirement documents such as diagrams, BRDs, code etc., synthesizes high-fidelity natural transcripts, and compiles them into highly interactive, responsive Critical User Journey (CUJ) reports.
Migrate Dialogflow CX (DFCX) agents to CXAS (Customer Experience Agent Studio) agents. Use this skill when the user mentions DFCX migration, migrating agents, converting DFCX to CXAS, porting agents, agent migration, or post-migration optimization/consolidation. Four independently runnable scripts: migrate.py (1:1), stage_1.py (variable dedup + consolidation), stage_2.py (instruction state machines + tool mocks + lint + report), stage_3.py (rewires consolidated topology from source dep graph; only needed when stage_1 ran consolidation). State persists between scripts via <target>_ir.json so each can run / re-run / resume independently.
Parsing and ingestion directives for the Python-based Agent Development Kit (ADK) conversational agent framework.
Parsing and Ingestion directives for the Dialogflow CX (DFCX) agent structure.
Converts CXAS golden evaluations to SCRAPI SimulationEvals test cases. Use when generating high-level, goal-oriented test cases from turn-by-turn evaluation JSONs, and when enriching test expectations with inferred tool calls.
| name | cxas-drawio-ingestor |
| description | Extracts conversational transcripts from .drawio XML files. |
Use this skill when you need to extract dialogue turns and conversation flows
from .drawio diagram files.
When processing .drawio flowchart designs, you must parse them as complete
directed graphs to construct dialogue paths, rather than extracting flat text
turns.
<mxCell> tags where vertex="1". Extract their id and value
(text label).<mxCell> tags where edge="1". Match source and target vertex
IDs to identify directed pathways.<div>, <font>, CSS styles). Unescape HTML entities
(e.g., " or <) to obtain clean text.Ensure that you map all domain concepts consistently to the target brand or theme requested by the user or specified in the requirements. Do not mix multiple industrial domains in a single report.
Compile the traversed paths into a structured CXAS YAML format complying with
the root-level turn structures (turns containing speaker: Agent|User,
text, and optional actions). Ensure every scenario starts with the
standardized Agent welcome greeting, is voice-realistic, and ends with the
standardized sign-off calling the end_session tool.
subintent_id: check_loyalty_status
subintent_name: "Check Loyalty VIP Status"
parent_cuj: "Loyalty Rewards"
turns:
- speaker: Agent
text: "Hello! Thanks for calling [Brand]. How can I help you today?"
- speaker: User
text: "I need to check my loyalty rewards point balance."
- speaker: Agent
text: "I'd be happy to help you check your loyalty rewards point balance. May I have your membership phone number?"
- speaker: User
text: "Yes, it is 555-0199."
- speaker: Agent
text: "Thank you. Let me verify your rewards balance."
webhook_call:
name: check_rewards_balance
payload:
phone_number: "5550199"
response:
vip_status: "ELITE"
points_balance: 1500
- speaker: Agent
text: "I've found your account. You are an Elite member with a balance of 1,500 points. Is there anything else I can help you with today?"
- speaker: User
text: "No, that's all. Thank you."
- speaker: Agent
text: "Thank you for calling [Brand]! Goodbye."
tool_call:
name: end_session
payload:
session_escalated: false
reason: "Conversation completed successfully"
All generated spoken dialogue turns (Agent voice turns) MUST strictly adhere to high-fidelity spoken voice standards. Subagents must ensure:
"450", "$909", "555-0199"). Instead, numbers must
be explicitly spelled out phonetically:
"four hundred fifty points", "nine hundred nine dollars"."five five five, zero, one, nine, nine".please, thank you, thanks, certainly, happy to help, welcome, goodbye, great day).