Agent skill

Cxas Autolabel Rules

by GoogleCloudPlatform in GoogleCloudPlatform/cxas-scrapi

Author, validate, and manage Contact Center AI (CCAI) Insights Autolabeling Rules.

Apache-2.0Auto-check passed

Install Cxas Autolabel Rules

skills CLI
$ npx skills add GoogleCloudPlatform/cxas-scrapi --skill cxas-autolabel-rules -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install GoogleCloudPlatform/cxas-scrapi cxas-autolabel-rules --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/GoogleCloudPlatform/cxas-scrapi.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/cxas-autolabel-rules .claude/skills/cxas-autolabel-rules && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
cxas-autolabel-rules
GitHub stars
107
Token cost
~1.1k tokens
SKILL.md length
312 words
Files
6 (incl. scripts, references)
Skills in repo
14
Repo updated
First seen
Licence
Apache-2.0

At a glance

Author, validate, and manage Contact Center AI (CCAI) Insights Autolabeling Rules.

  • Works in 4 steps: Overview & Declarative YAML Schema → Common Expression Language (CEL)… → Step-by-Step Workflow → …
  • Users want to translate business labeling logic into CEL condition expressions
  • SKILL.md covers 1. Overview & Declarative YAML…, 2. Common Expression Language…, 3. Step-by-Step Workflow and 4. References & Tooling
  • Runs Python scripts from its folder; calls uv

What it does

Cxas Autolabel Rules is an agent skill from GoogleCloudPlatform/cxas-scrapi. Author, validate, and manage Contact Center AI (CCAI) Insights Autolabeling Rules. Use when users want to translate business labeling logic into CEL condition expressions, maintain declarative autolabelrules.yaml configurations, or synchronize rules to GCP projects.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `references/cel_cookbook.md`, `references/design.md` and `references/implementation_plan.md`).

It works with Google Cloud. The repository describes itself as: A powerful Python API, CLI, and set of Agent Skills for CX Agent Studio to automate, evaluate, and scale your agents with ease. The licence is Apache-2.0.

When your agent uses it

  • Users want to translate business labeling logic into CEL condition expressions
  • Maintain declarative autolabelrules.yaml configurations
  • Synchronize rules to GCP projects

Example prompts

  • “/cxas-autolabel-rules”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Overview & Declarative YAML Schema
  2. Common Expression Language (CEL) Authoring Rules
  3. Step-by-Step Workflow
  4. References & Tooling

What it can do on your machine

Read from SKILL.md and the folder at commit ffba639. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Cxas Autolabel Rules loads about 1.1k tokens when it runs, and up to ~11k if it reads all its reference files. Until then it costs about 72 tokens; SKILL.md has 312 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~72
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~11k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from GoogleCloudPlatform/cxas-scrapi at commit ffba639, republished under its Apache-2.0 licence (© GoogleCloudPlatform). 312 words, ~1,108 tokens.

Download SKILL.mdSave it as .claude/skills/cxas-autolabel-rules/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
cxas-autolabel-rules
description
Author, validate, and manage Contact Center AI (CCAI) Insights Autolabeling Rules. Use when users want to translate business labeling logic into CEL condition expressions, maintain declarative autolabel_rules.yaml configurations, or synchronize rules to GCP projects.

CCAI Insights Autolabeling Rules Skill

This skill guides you in authoring, refining, validating, and synchronizing Contact Center AI (CCAI) Insights Autolabeling Rules.

Autolabeling rules enrich ingested conversations with custom key-value metadata evaluated via Common Expression Language (CEL).


1. Overview & Declarative YAML Schema

Rules are defined declaratively in autolabel_rules.yaml:

yaml
version: "1.0"
project_id: "your-gcp-project-id"
location: "us-central1"

autolabeling_rules:
  - rule_id: "agent_domain"
    display_name: "Agent Domain Classifier"
    label_key: "agent_domain"
    label_key_type: "LABEL_KEY_TYPE_CUSTOM"
    active: true
    conditions:
      - condition: "containsSubAgent(conversation, 'billing_specialist')"
        value: "'billing'"
      - condition: "containsSubAgent(conversation, 'tech_support')"
        value: "'tech_support'"
      - condition: ""
        value: "'general'"
Core Schema Requirements
  1. rule_id: Unique alphanumeric identifier (snake_case or kebab-case).
  2. label_key: The metadata key name to attach to conversation.labels.
  3. conditions: Ordered array of conditions evaluated top-to-bottom.
    • condition: CEL boolean expression (e.g. conversation.duration > 300).
    • value: CEL expression or quoted string literal (e.g. 'vip', 'escalated').
    • Mandatory Fallback: The final condition in every rule MUST be condition: "" to act as the default fallback value.

2. Common Expression Language (CEL) Authoring Rules

Refer to the CEL Cookbook for full syntax and function references.

Built-in Helper Functions
  • Sub-Agent / Flow Detection:
    cel
    containsSubAgent(conversation, "billing_subagent")
  • Session Parameter Matching:
    cel
    hasSessionParam(conversation, "authenticated", "true")
  • Sentiment Analysis:
    cel
    hasCallerSentiment(conversation, "NEGATIVE")
  • Turn & Duration Checks:
    cel
    conversation.duration > 300 && conversation.turnCount >= 10

3. Step-by-Step Workflow

Follow these steps when helping a user build or update autolabeling rules:

Step 1: Ingest Requirements
  • Ask the user what conversation properties or behaviors they want to classify (e.g., specific sub-agents, customer sentiment, authentication status, duration thresholds).
  • Determine target GCP project ID and location.
Step 2: Draft or Edit Declarative YAML
  • Create or update autolabel_rules.yaml in the user's workspace.
  • Translate business logic into clear, prioritized CEL expressions.
  • Ensure every rule ends with a fallback (condition: "").
Step 3: Compare with Active Remote Rules (diff)

Run diff to preview additions, modifications, and deletions:

bash
uv run cxas insights diff-autolabel-rules --file autolabel_rules.yaml
Step 4: Dry-Run Deploy

Run push with --dry-run to verify API compatibility:

bash
uv run cxas insights push-autolabel-rules --file autolabel_rules.yaml --dry-run
Step 5: Deploy to GCP Project (push)

Deploy the changes:

bash
uv run cxas insights push-autolabel-rules --file autolabel_rules.yaml

(To delete remote rules that are no longer in the local YAML, append --force.)

Step 6: Export Existing Rules (pull)

To export existing active rules from an environment into YAML:

bash
uv run cxas insights pull-autolabel-rules --parent projects/<PROJECT>/locations/<LOCATION> --out autolabel_rules.yaml

4. References & Tooling

© GoogleCloudPlatform, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (scripts, references) in .agents/skills/cxas-autolabel-rules of GoogleCloudPlatform/cxas-scrapi.

  • SKILL.md
  • references/cel_cookbook.md
  • references/design.md
  • references/implementation_plan.md
  • references/schema.json
  • scripts/sync_rules.py

Open the folder on GitHubat commit ffba639

Compare with similar skills

Cxas Autolabel Rules next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Cxas Autolabel Rules compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Cxas Autolabel Rules this skillGoogleCloudPlatform/cxas-scrapi107—~1.1kAutomated safety check: PassApache-2.0
Gmail Inbox Watchergoogleworkspace/cli31k1 repos~476Automated safety check: PassApache-2.0
Cloud Cost Optimizationwshobson/agents40k14 repos~1.7kAutomated safety check: PassMIT
Senior DevOps Toolkitmaslennikov-ig/claude-code-orchestrator-kit2606 repos~1.1kAutomated safety check: NotesCustom licence
Terravision Cloud Diagramspatrickchugh/terravision1.6k—~5.6kAutomated safety check: NotesAGPL-3.0-only
Thesvgglincker/thesvg2.8k—~1.5kAutomated safety check: PassMIT

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Works with

Questions about Cxas Autolabel Rules

What does Cxas Autolabel Rules do?

Author, validate, and manage Contact Center AI (CCAI) Insights Autolabeling Rules. Cxas Autolabel Rules is an agent skill from GoogleCloudPlatform/cxas-scrapi. Author, validate, and manage Contact Center AI (CCAI) Insights Autolabeling Rules.

When should I use Cxas Autolabel Rules?

Cxas Autolabel Rules fits situations like: users want to translate business labeling logic into CEL condition expressions; maintain declarative autolabelrules.yaml configurations; synchronize rules to GCP projects.

How do I install Cxas Autolabel Rules in Claude Code?

Run `npx skills add GoogleCloudPlatform/cxas-scrapi --skill cxas-autolabel-rules -a claude-code`. Or copy the skill folder (.agents/skills/cxas-autolabel-rules in GoogleCloudPlatform/cxas-scrapi) into .claude/skills/cxas-autolabel-rules in your project. Claude Code loads it when a task matches its description.

How do I install Cxas Autolabel Rules in Codex?

Run `npx skills add GoogleCloudPlatform/cxas-scrapi --skill cxas-autolabel-rules -a codex`. Or copy the skill folder (.agents/skills/cxas-autolabel-rules in GoogleCloudPlatform/cxas-scrapi) into .agents/skills/cxas-autolabel-rules in your project. Codex loads it when a task matches its description.

Can I use Cxas Autolabel Rules in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add GoogleCloudPlatform/cxas-scrapi --skill cxas-autolabel-rules -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cxas-autolabel-rules, .gemini/skills/cxas-autolabel-rules, .github/skills/cxas-autolabel-rules and .opencode/skills/cxas-autolabel-rules in your project.

What does Cxas Autolabel Rules need to run?

Going by SKILL.md and its folder, Cxas Autolabel Rules needs Python for the scripts in its folder and the command-line tools its instructions call (uv). Our summary lists: Python 3.

Does Cxas Autolabel Rules access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Cxas Autolabel Rules safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Cxas Autolabel Rules use?

Cxas Autolabel Rules is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Cxas Autolabel Rules use?

About 1.1k tokens (SKILL.md is roughly 4.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 9.9k tokens, read only when the agent opens those files.

What are the alternatives to Cxas Autolabel Rules?

Skills that share tags, products or a category with Cxas Autolabel Rules: Gmail Inbox Watcher (googleworkspace/cli, 31k stars), Cloud Cost Optimization (wshobson/agents, 40k stars), Senior DevOps Toolkit (maslennikov-ig/claude-code-orchestrator-kit, 260 stars) and Terravision Cloud Diagrams (patrickchugh/terravision, 1.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Cxas Autolabel Rules?

GoogleCloudPlatform (a GitHub organization) maintains it in GoogleCloudPlatform/cxas-scrapi, which has 107 GitHub stars. The repository holds 14 skills in this directory. The repository was last updated on October 8, 2026.

Source: GoogleCloudPlatform/cxas-scrapi on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.