Creator · GoogleCloudPlatform
Last updated · Sep 7, 2026
Extracts conversational transcripts from .drawio XML files.
Do not auto-install
Install targets
Codex install prompt
Install the "cxas-drawio-ingestor" agent skill from https://github.com/GoogleCloudPlatform/cxas-scrapi/tree/main/.agents/skills/cxas-cuj-report-generator/ingestors/files/drawio. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Extracts conversational transcripts from .drawio XML files. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"googlecloudplatform-cxas-drawio-ingestor","task":"Install cxas-drawio-ingestor","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Document processing
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add GoogleCloudPlatform/cxas-scrapi --skill cxas-drawio-ingestor
Maintenance
fresh
4d since push
Risk
Needs review
Dependency or permission surface needs review
GitHub quality
95
67/100 Quality · 65/100 Trust
Coverage tags
Review notes
Dependency or permission surface needs review · Permission surface may require sandboxing
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Do not auto-installTrust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Choose a stronger alternative or inspect the source manually before any install attempt.
Stars
95 GitHub stars
Repo activity
95 stars, 82 forks
Maintenance
4d since push
License
Apache-2.0
Install
npx skills add GoogleCloudPlatform/cxas-scrapi --skill cxas-drawio-ingestor
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add GoogleCloudPlatform/cxas-scrapi --skill cxas-drawio-ingestorDo not use when
Agent safety v2
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
medium
Skill may inspect schemas, query databases, or work with persistent stores.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20cxas-drawio-ingestor%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20cxas-drawio-ingestor%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/googlecloudplatform-cxas-drawio-ingestor/install
Agent should check
Copy prompt
Task: Use cxas-drawio-ingestor in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20cxas-drawio-ingestor%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/googlecloudplatform-cxas-drawio-ingestor/install
Install command: npx skills add GoogleCloudPlatform/cxas-scrapi --skill cxas-drawio-ingestor
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/googlecloudplatform-cxas-drawio-ingestor/install
LLM text format
/api/skills/googlecloudplatform-cxas-drawio-ingestor/install?format=text
Find alternatives
/api/skills/search?q=cxas-drawio-ingestor&limit=3
Agent prompt
Use cxas-drawio-ingestor for this task. Review https://www.openagentskill.com/api/skills/googlecloudplatform-cxas-drawio-ingestor/install, then install with: npx skills add GoogleCloudPlatform/cxas-scrapi --skill cxas-drawio-ingestorRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/googlecloudplatform-cxas-drawio-ingestor
LLM text
/api/registry/manifest/googlecloudplatform-cxas-drawio-ingestor?format=text
Install alias
/api/registry/install/googlecloudplatform-cxas-drawio-ingestor
Recommend
/api/registry/recommend?task=Use%20cxas-drawio-ingestor%20in%20an%20agent%20workflow&limit=3
Agent fit
Local desktop
Use-case tags
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Prototype with this skill first; keep a fallback candidate ready.
Role in stack
Fallback candidate
Primary fit
Local desktop
Trust label
Prototype first
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
GitHub adoption
CHECK95 GitHub stars
Stars/forks activity
CHECK95 stars, 82 forks; issue activity unavailable in current metadata
Recent maintenance
PASS4d since push
License clarity
PASSApache-2.0
Good signals
Review before install
Recommended action
Choose a stronger alternative or inspect the source manually before any install attempt.
Quality profile
Useful candidate, but compare it with alternatives before adopting.
Workflow fit
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Operate web apps
I need my agent to control a browser, fill forms, and verify web app workflows.
Parse messy files
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Workflow fit
Scrape, clean, and reuse web data
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Alternative shortlist
Similar skills that may fit this task.
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--- name: cxas-drawio-ingestor description: "Extracts conversational transcripts from .drawio XML files." ---
# Draw.io Ingestor Skill
Use this skill when you need to extract dialogue turns and conversation flows from `.drawio` diagram files.
## Protocol for Flowchart & Diagram Traversal
When processing `.drawio` flowchart designs, you must parse them as **complete directed graphs** to construct dialogue paths, rather than extracting flat text turns.
### 1. Parse Graph Structural Topology
* Scan `<mxCell>` tags where `vertex="1"`. Extract their `id` and `value` (text label). * Scan `<mxCell>` tags where `edge="1"`. Match `source` and `target` vertex IDs to identify directed pathways. * Clean cell text by stripping internal HTML styling, tags, and formatting metadata (e.g., `<div>`, `<font>`, CSS styles). Unescape HTML entities (e.g., `"` or `<`) to obtain clean text.
### 2. Dialogue State & Path Traversal
* Locate the start node (typically a top-level cell labeled "Start", "Welcome", or "Entry"). * Perform a step-by-step traversal (DFS/BFS) along the directed edges. Each unique path from start to a terminal node constitutes a distinct conversation flow. * **State Mapping**: * **Prompt Node**: Maps to an **Agent Turn** (using the node's cleaned label). * **Directed Edge Label**: Maps to a **User Input** (representing speech utterances or DTMF keys that trigger that transition). * **Computational Node**: Maps to a **System Action / Webhook** (e.g., database lookups or validation calls). * **Decision Diamond**: Represents logical branching.
### 3. Brand and Domain Consistency Mapping
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.
* If the target brand has specific concepts, map requirements to fit that brand's services. * If no brand is specified, use clean, generic customer service phrasing suitable for the context. * Avoid using technical or backend-specific terminology in spoken Agent turns.
### 4. Format the Resulting CXAS Transcript
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.
## Example Target Schema
```yaml 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" ```
## Linguistic & Voice Naturalness Standards
All generated spoken dialogue turns (Agent voice turns) MUST strictly adhere to high-fidelity spoken voice standards. Subagents must ensure:
1. **Numeric Voice Normalization**: Spoken Agent turns MUST NOT contain raw digits, formatted currencies, or punctuation symbols representing numbers (e.g., do NOT write `"450"`, `"$909"`, `"555-0199"`). Instead, numbers must be explicitly spelled out phonetically: * *Correct*: `"four hundred fifty points"`, `"nine hundred nine dollars"`. * *IDs and Phone Numbers*: Must be written digit-by-digit separated by spaces or commas: `"five five five, zero, one, nine, nine"`. 2. **Spoken Breath Span Limit**: Agent turns must remain concise, natural, and conversational. Individual spoken text blocks MUST NOT exceed **300 characters** inside a single turn. 3. **Vocabulary Smoothness**: Avoid robotic repetitions of the same long words (do not repeat the same word of length 5+ more than 4 times in a single turn). 4. **Conversational Politeness**: Agent turns must maintain standard polite voice markers (`please`, `thank you`, `thanks`, `certainly`, `happy to help`, `welcome`, `goodbye`, `great day`).
Source provenance
Decision snapshot
recent repository activity
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for cxas-drawio-ingestor, ready for a manual X post.
Before you hand an agent a web workflow, give it a repeatable starting point. cxas-drawio-ingestor: Extracts conversational transcripts from .drawio XML files. 95 stars https://www.openagentskill.com/skills/googlecloudplatform-cxas-drawio-ingestor?ref=x
Listing + install path for cxas-drawio-ingestor: https://www.openagentskill.com/skills/googlecloudplatform-cxas-drawio-ingestor?ref=x Install: npx skills add GoogleCloudPlatform/cxas-scrapi --skill cxas-drawio-ingestor
Listing source
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Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
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Do not auto-install
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shell or command execution, network or browser access
Agent outcomes
No agent outcome data yet
Docs
Usable metadata, review docs
Risk summary
Install readiness