New: 97,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Cxas Protocol Robust Extraction

by GoogleCloudPlatform in GoogleCloudPlatform/cxas-scrapi

A robust methodology for LLM-based requirements gathering and high-fidelity artifact generation.

Apache-2.0Auto-check passedAgent Workflows

Install Cxas Protocol Robust Extraction

skills CLI
$ npx skills add GoogleCloudPlatform/cxas-scrapi --skill cxas-protocol-robust-extraction -a claude-code

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

GitHub CLI
$ gh skill install GoogleCloudPlatform/cxas-scrapi cxas-protocol-robust-extraction --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-cuj-report-generator/protocols/cxas-protocol-robust-extraction .claude/skills/cxas-protocol-robust-extraction && 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-protocol-robust-extraction
GitHub stars
106
Token cost
~1.1k tokens
SKILL.md length
585 words
Files
1
Skills in repo
14
Repo updated
First seen
Licence
Apache-2.0

At a glance

A robust methodology for LLM-based requirements gathering and high-fidelity artifact generation.

  • Works in 4 steps: Parallel Expert Discovery → The Iterative Exhaustion Loop → Logical Clustering → …
  • Tasks that involve Requirements gathering
  • SKILL.md covers General Principles &… and Core Directives
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Cxas Protocol Robust Extraction is an agent skill from GoogleCloudPlatform/cxas-scrapi. A robust methodology for LLM-based requirements gathering and high-fidelity artifact generation. Employs 'Divide, Conquer, and Verify' tactics using specialized subagents, iterative exhaustion loops, and batched execution to ensure zero data loss.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering Requirements gathering and Subagents. 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

  • Tasks that involve Requirements gathering
  • Tasks that involve Subagents

Example prompts

  • “Divide, Conquer, and Verify”
  • “/cxas-protocol-robust-extraction”

Workflow steps

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

  1. Parallel Expert Discovery
  2. The Iterative Exhaustion Loop
  3. Logical Clustering
  4. Batched Execution & Verification

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

    No scripts in the folder and no shell commands in SKILL.md.

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

  • Network

    No URLs in SKILL.md.

    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 Protocol Robust Extraction loads about 1.1k tokens when it runs. Until then it costs about 70 tokens; SKILL.md has 585 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~70
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from GoogleCloudPlatform/cxas-scrapi at commit ffba639, republished under its Apache-2.0 licence (© GoogleCloudPlatform). 585 words, ~1,090 tokens.

Download SKILL.mdSave it as .claude/skills/cxas-protocol-robust-extraction/SKILL.md (or your agent's skills folder).
name
cxas-protocol-robust-extraction
description
A robust methodology for LLM-based requirements gathering and high-fidelity artifact generation. Employs 'Divide, Conquer, and Verify' tactics using specialized subagents, iterative exhaustion loops, and batched execution to ensure zero data loss.

Robust Extraction Protocol

This protocol defines the standard operating procedure for extracting exhaustive requirements (like subintents, CUJs, or logic rules) from large, complex, or fragmented customer artifacts.

It prevents the common LLM pitfalls of "context drift" and "truncation" by enforcing a strict "Divide, Conquer, and Verify" methodology.

General Principles & Anti-Hallucination Guardrails

To ensure 100% coverage and prevent data loss due to tool limits or implicit filtering, follow these principles across all phases:

  • Quantify the Scope: Before spawning any subagents or starting extraction, determine the exact total count of target items (files, directories, database rows). Record this number as your "Success Target." You must verify that the sum of items processed equals this target before proceeding to consolidation.
  • Coverage over Curation: Default to 100% extraction coverage. Never assume the user only wants the "top" or "most interesting" items unless explicitly instructed to apply a quality filter. A standard or repetitive item is still data that must be reported.
  • Circumvent Tool Caps: Be aware that search and listing tools often have display limits (e.g., capped at 50 or 1000 results). If the expected scale (from the Quantify step) exceeds the tool's limit, you must partition the work (e.g., by alphabet or ID range) to ensure no items are hidden by the tool's cap.
  • Maintain Traceability: For every extracted requirement or item, record the source file or location it was extracted from. This allows for easy verification and provides context when reviewing the consolidated results.

Core Directives

When tasked with comprehensive extraction or generation from a large corpus, you MUST follow this four-phase methodology:

Phase 1: Parallel Expert Discovery

Never use a single generalist agent or a single prompt to read all files.

  1. Categorize the input artifacts (e.g., Code/ADK, Diagrams, Test Cases).
  2. Spawn specialized expert subagents (e.g., cxas-ingestor-adk) in parallel, providing each with only the context relevant to their expertise.
  3. Consolidate their initial findings into a centralized list.
Show full SKILL.md (267 more words)Show less
Phase 2: The Iterative Exhaustion Loop

LLMs often miss items in a single pass of a large document. You must force them to iterate.

  1. Provide the current consolidated list of findings back to the expert subagents.
  2. Ask a direct question: "Based on your artifacts, are there ANY MORE items missing from this list? If yes, list them. If no, reply EXACTLY 'NO'."
  3. The Loop Rule: You MUST continue this loop, updating the consolidated list each time, until ALL expert subagents unanimously reply with "NO".
Phase 3: Logical Clustering

Once the exhaustive list is finalized (e.g., 100+ subintents), organize it.

  1. Group the granular findings into high-level logical categories (Parent CUJs).
  2. Verify with the experts that the parent categories encompass all findings.
Phase 4: Batched Execution & Verification

Never ask an LLM to generate 100+ complex artifacts (like conversational transcripts) in a single prompt. It will hallucinate or truncate.

  1. Batching: Divide the exhaustive list into small, manageable batches (e.g., 10 batches of 10 items). Write these batches to temporary files.
  2. Delegated Execution: Spawn a new subagent for each batch. Instruct them to process only their assigned batch and write the output to a specific file.
  3. The Verification Gate: As the orchestrator, you MUST verify the output of each subagent. Did they generate an output for every single item in their batch?
  4. If YES: Accept the batch.
  5. If NO: Discard the output and respawn the subagent for that specific batch with stronger steering instructions.
  6. Consolidation: Only when all batches pass the Verification Gate, merge them into the final, exhaustive deliverable.

© 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

Just SKILL.md in .agents/skills/cxas-cuj-report-generator/protocols/cxas-protocol-robust-extraction of GoogleCloudPlatform/cxas-scrapi.

Open the folder on GitHubat commit ffba639

Compare with similar skills

Cxas Protocol Robust Extraction 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 Protocol Robust Extraction compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Cxas Protocol Robust Extraction this skillGoogleCloudPlatform/cxas-scrapi106—~1.1kAutomated safety check: PassApache-2.0
Extracting Requirementsprime-radiant-inc/iterative-development181—~2.7kAutomated safety check: PassApache-2.0
Conductor CcJinghao67/conductor102—~1.1kAutomated safety check: PassMIT
Grill Articleopen-cqrs/opencqrs118—~5kAutomated safety check: NotesApache-2.0
AutodevZhangShenao/harness9141—~606Automated safety check: NotesMIT
Using Superpowersfarm-fe/farm5.6k34 repos~1.4kAutomated safety check: PassMIT

Similar skills

  • Extracting Requirements

    prime-radiant-inc/iterative-development

    Reads human-written spec documents and produces per-epic requirement files with proof obligations plus behavior scenarios with stable IDs, using parallel chunked extraction.

    181 GitHub stars~2.7k tokensUpdated 4 mo ago
    Agent WorkflowsAuto-check passed

  • Conductor Cc

    Jinghao67/conductor

    A skill your agent uses when a complex, long-running task risks polluting one conversation — research, implementation, review, planning, or learning that should be split into dependency-aware…

    102 GitHub stars~1.1k tokensUpdated 3 mo ago
    Agent WorkflowsAuto-check passed

  • Grill Article

    open-cqrs/opencqrs

    Critically inspect a written article before enrichment — verify codebase claims with real tool lookups, find internal contradictions, and (for argumentative articles) sparr with the author about…

    118 GitHub stars~5k tokensUpdated 9 days ago
    Agent WorkflowsAuto-check: notes

  • Autodev

    ZhangShenao/harness9

    Feature auto-development — clarify requirements, generate spec, dispatch dev sub-agent to implement and merge into current branch

    141 GitHub stars~606 tokensUpdated today
    Agent WorkflowsAuto-check: notes

  • Using Superpowers

    farm-fe/farm

    A skill your agent uses when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions

    5.6k GitHub starsUsed in 34 repos~1.4k tokens
    Agent WorkflowsAuto-check passed

  • Claude Code Agent Development

    anthropics/claude-plugins-official

    Official

    Explains how to write agents for Claude Code plugins: the markdown file with YAML frontmatter, trigger descriptions, model and color settings, and system prompt design.

    37k GitHub starsUsed in 8 repos~2.8k tokens
    Agent WorkflowsAuto-check passed

More from GoogleCloudPlatform/cxas-scrapi

All 14 skills in this repo

  • Cxas Agent Foundry

    GoogleCloudPlatform/cxas-scrapi

    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…

    106 GitHub stars~2.4k tokensUpdated yesterday
    Auto-check passed

  • Cxas Autolabel Rules

    GoogleCloudPlatform/cxas-scrapi

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

    106 GitHub stars~1.1k tokensUpdated yesterday
    Auto-check passed

  • Cxas Configurable Dashboards

    GoogleCloudPlatform/cxas-scrapi

    Author, validate, and manage Contact Center AI (CCAI) Insights Configurable Dashboards.

    106 GitHub stars~1.8k tokensUpdated yesterday
    Auto-check passed

  • Cxas Dfcx Migration

    GoogleCloudPlatform/cxas-scrapi

    Migrate Dialogflow CX (DFCX) agents to CXAS (Customer Experience Agent Studio) agents.

    106 GitHub stars~3.2k tokensUpdated yesterday
    Auto-check passed

  • Cxas Loss Analysis

    GoogleCloudPlatform/cxas-scrapi

    Retrieves non-contained CCAI Insights conversations (losses), uses agent intelligence to cluster them into common failure patterns, and generates a markdown report.

    106 GitHub stars~1.5k tokensUpdated yesterday
    Auto-check passed

  • Cxas Sim Eval

    GoogleCloudPlatform/cxas-scrapi

    Converts CXAS golden evaluations to SCRAPI SimulationEvals test cases.

    106 GitHub stars~1.2k tokensUpdated yesterday
    Auto-check passed

Categories

Questions about Cxas Protocol Robust Extraction

What does Cxas Protocol Robust Extraction do?

A robust methodology for LLM-based requirements gathering and high-fidelity artifact generation. Cxas Protocol Robust Extraction is an agent skill from GoogleCloudPlatform/cxas-scrapi. A robust methodology for LLM-based requirements gathering and high-fidelity artifact generation.

When should I use Cxas Protocol Robust Extraction?

Cxas Protocol Robust Extraction fits situations like: tasks that involve Requirements gathering; tasks that involve Subagents.

How do I install Cxas Protocol Robust Extraction in Claude Code?

Run `npx skills add GoogleCloudPlatform/cxas-scrapi --skill cxas-protocol-robust-extraction -a claude-code`. Or copy the skill folder (.agents/skills/cxas-cuj-report-generator/protocols/cxas-protocol-robust-extraction in GoogleCloudPlatform/cxas-scrapi) into .claude/skills/cxas-protocol-robust-extraction in your project. Claude Code loads it when a task matches its description.

How do I install Cxas Protocol Robust Extraction in Codex?

Run `npx skills add GoogleCloudPlatform/cxas-scrapi --skill cxas-protocol-robust-extraction -a codex`. Or copy the skill folder (.agents/skills/cxas-cuj-report-generator/protocols/cxas-protocol-robust-extraction in GoogleCloudPlatform/cxas-scrapi) into .agents/skills/cxas-protocol-robust-extraction in your project. Codex loads it when a task matches its description.

Can I use Cxas Protocol Robust Extraction 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-protocol-robust-extraction -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-protocol-robust-extraction, .gemini/skills/cxas-protocol-robust-extraction, .github/skills/cxas-protocol-robust-extraction and .opencode/skills/cxas-protocol-robust-extraction in your project.

What does Cxas Protocol Robust Extraction need to run?

SKILL.md names no scripts, command-line tools or credentials: Cxas Protocol Robust Extraction is instructions for the agent only.

Does Cxas Protocol Robust Extraction access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Cxas Protocol Robust Extraction 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. Review the folder before installing.

What licence does Cxas Protocol Robust Extraction use?

Cxas Protocol Robust Extraction 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 Protocol Robust Extraction 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.

What are the alternatives to Cxas Protocol Robust Extraction?

Skills that share tags, products or a category with Cxas Protocol Robust Extraction: Extracting Requirements (prime-radiant-inc/iterative-development, 181 stars), Conductor Cc (Jinghao67/conductor, 102 stars), Grill Article (open-cqrs/opencqrs, 118 stars) and Autodev (ZhangShenao/harness9, 141 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Cxas Protocol Robust Extraction?

GoogleCloudPlatform (a GitHub organization) maintains it in GoogleCloudPlatform/cxas-scrapi, which has 106 GitHub stars. The repository holds 14 skills in this directory. The repository was last updated on October 6, 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.