Task Coverage Protocol logo

Task Coverage Protocol

Organization
GoogleCloudPlatform
task-coverage-protocol

Enforces 100% task and file ingestion coverage via an asynchronous, supervisor-driven state polling mechanism.

Overview

PublisherGoogleCloudPlatform
Repositorycxas-scrapi
Skill nametask-coverage-protocol
Stars
113
Forks
106
Bundled files
49
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 49 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by GoogleCloudPlatform on GitHub. Read the source before you install it.

Installation

Install the Task Coverage Protocol AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/GoogleCloudPlatform/cxas-scrapi/tree/main/.agents/skills/cxas-cuj-report-generator/protocols/task-coverage-protocol
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/GoogleCloudPlatform/cxas-scrapi.git /tmp/cxas-scrapi
mkdir -p .claude/skills
cp -r /tmp/cxas-scrapi/.agents/skills/cxas-cuj-report-generator/protocols/task-coverage-protocol .claude/skills/task-coverage-protocol
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Task Coverage Protocol in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Task Coverage Protocol on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Task Coverage Protocol is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

PROTOCOL: Supervisor-Driven Task Coverage Checklist

This protocol governs how you must handle large, repetitive, or multi-step requirements ingestion tasks to prevent context drift, avoid memory exhaustion, and guarantee 100% file coverage. Workloads are partitioned into sliced batches dynamically monitored by a decoupled supervisor.

Architectural Overview

Instead of attempting to list and process hundreds of files in a single conversation turn, tasks are executed asynchronously:

  1. The Ingestor Supervisor scans the directory, partitions the files, and outputs a structured batch directive.
  2. You (or parallel subagents) consume this directive, process only the assigned slice of files, and write the output files to the designated folder.
  3. The supervisor verifies and grades the outputs before queueing the next slice.

Execution Workflow

1. Scoped Scan & Scoping Setup

When instructed to ingest a requirements directory (e.g. "Ingest and parse all files in this folder"), you MUST launch the Supervisor script to initialize the task state:

bash
python3 protocols/task-coverage-protocol/scripts/ingestor_supervisor.py \
  --source_dir="path/to/source" \
  --target_skill="path/to/cxas-cuj-report-generator/"

2. Consume the Handoff Directive

The supervisor will scan the target folder, slice the files into batch lists of size 10, and print a structured directive to standard output: SPAWN_BATCH_DIRECTIVE: {"spec_file": "/tmp/ingestor/batch_spec.json", "output_dir": "/tmp/ingestor/outputs/"}

Upon seeing this token, you MUST:

  1. Read the spec file /tmp/ingestor/batch_spec.json to retrieve the list of files, prompts, and expected output paths.
  2. Spawn fresh, parallel subagents (one per file or partition) using their respective prompt files (e.g., /tmp/ingestor/prompts/<file_name>_prompt.txt).
  3. Instruct the subagents to write their generated YAML transcripts directly to /tmp/ingestor/outputs/<file_name>_output.txt.

3. Yield Terminal & Polling Gate

Once parallel subagents have been spawned in the background, you MUST immediately stop calling tools and go idle (or report status) to release the terminal lock. The supervisor will poll the output directory until all files are completed.

4. Progress Verification & Next Slices

When all subagents have delivered their YAML transcripts, run the supervisor script again:

bash
python3 protocols/task-coverage-protocol/scripts/ingestor_supervisor.py \
  --source_dir="path/to/source" \
  --target_skill="path/to/cxas-cuj-report-generator/"
  • Verification Pass: If the supervisor grades all outputs as passing, it automatically advances the file pointer and writes the next batch spec. Consume the new directive and repeat the parallel spawning cycle!
  • Verification Fail: If the supervisor detects any syntax, schema, or linguistic violations, it automatically triggers recovery, rebasing, or load bisection. Re-spawn the corrective subagents as directed.

5. Final Compilation & Completion

When all batch slices are fully processed, the supervisor will declare All files successfully ingested! and compile the final interactive CUJ reports.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Task Coverage Protocol AI skill do?

Enforces 100% task and file ingestion coverage via an asynchronous, supervisor-driven state polling mechanism.

Why use Task Coverage Protocol on TypingMind?

Because you install it once and use it with any model. Task Coverage Protocol is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Task Coverage Protocol in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/GoogleCloudPlatform/cxas-scrapi/tree/main/.agents/skills/cxas-cuj-report-generator/protocols/task-coverage-protocol. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Task Coverage Protocol?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Task Coverage Protocol?

As many as you like. As long as a model supports skills, you can use Task Coverage Protocol with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Task Coverage Protocol AI skill free?

Yes. It is published on GitHub by GoogleCloudPlatform under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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