Fallow Cloud Setup logo

Fallow Cloud Setup

Organization
fallow-rs
fallow-cloud-setup

Connect a JavaScript or TypeScript app to Fallow Cloud production runtime coverage. Use when asked to set up Fallow Cloud, add the Fallow beacon, send runtime coverage to Fallow Cloud, or find out why Fallow Cloud shows no data for a repository. Do not use for local code-quality tooling; use fallow-setup for that. Do not use to read runtime data of a repository that is already set up; use the fallow skill for that.

Overview

Publisherfallow-rs
Repositoryfallow-skills
Skill namefallow-cloud-setup
Stars
137
Forks
9
Bundled files
2
LicenseMIT
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.

  • 2 bundled files

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

  • Open source

    Published by fallow-rs on GitHub. Read the source before you install it.

Installation

Install the Fallow Cloud Setup 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/fallow-rs/fallow-skills/tree/main/fallow/skills/fallow-cloud-setup
  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/fallow-rs/fallow-skills.git /tmp/fallow-skills
mkdir -p .claude/skills
cp -r /tmp/fallow-skills/fallow/skills/fallow-cloud-setup .claude/skills/fallow-cloud-setup
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fallow Cloud Setup 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 Fallow Cloud Setup 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 Fallow Cloud Setup 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.

Fallow Cloud setup: runtime coverage with one commit SHA

Fallow Cloud joins production runtime coverage to the code only when five things name the same commit SHA:

  1. The beacon in the app sends commitSha.
  2. CI uploads the static inventory for that SHA.
  3. CI uploads the source maps for that SHA, when the build bundles the code.
  4. CI registers the deployment of that SHA.
  5. The first production data for that SHA arrives.

fallow coverage setup --cloud --json writes a plan for all five steps. This skill applies the plan.

When to Use

  • Add the Fallow beacon to an app and send production coverage to Fallow Cloud.
  • Wire the CI steps that upload the inventory and the source maps and register the deployment.
  • Find the missing step when Fallow Cloud shows no data or no matched functions for a repository.

When NOT to Use

  • Local code-quality tooling, CI quality gates, or agent wiring. Use fallow-setup.
  • Reading runtime data of a repository that is already set up. Use fallow.

Rules

  1. Never write an API key value into a file, a command, or a message. Use the environment variable names that the plan gives (cloud.api_key_env, cloud.browser_key_env). The user stores the values as CI secrets and server environment variables. Never use the --api-key flag and never put a key before a command (FALLOW_API_KEY=... npx ...). The user exports the key in the shell.
  2. A browser bundle gets only an ingest-only key (prefix fallow_pub_k1_). A full key (prefix fallow_live_k1_) stays in CI and on the server.
  3. Use the same commit SHA for every step. Do not replace $GITHUB_SHA or the commitSha expression in the plan with another value.
  4. Resolve flags from fallow coverage setup --help. Do not use flags from memory.
  5. Show the edits to the user before you change a CI workflow. A deploy workflow can hold secrets and production access.

Sequence

  1. Check the flag. Run npx fallow coverage setup --help. When it does not list --cloud, use Manual setup instead of steps 2 to 8. It has the same five steps.

  2. Get the plan. Run npx fallow coverage setup --cloud --json at the repository root. When cloud.project_id is null, ask the user for the Fallow Cloud project id (owner/repo) and run the command again with --project-id <owner/repo>, so no snippet keeps the OWNER/REPO placeholder. When the command exits 2 and prints no JSON, read the error on stderr: an input such as the project id, --git-sha, or --api-endpoint is not valid.

  3. Read the plan.

    • members[] has one entry for each workspace member that runs code. Each entry has its own snippets, files_to_edit, dockerfile_snippet, and warnings. Apply the snippets from members[] only: the top-level snippets and files_to_edit repeat the first member.
    • commands installs the beacon.
    • cloud.ci.steps holds the GitHub Actions steps (cloud.ci.provider is github_actions). cloud.ci.workflows_detected names the workflow files that look like a deploy.
    • cloud.source_maps.dirs names the build output of each bundled member. cloud.source_maps.reason says what the build must do, for example emit source maps.
    • warnings names what the plan cannot do for you, for example the coverage source of a Node server.
  4. Install the beacon. Run the commands in commands with the package manager of the project.

  5. Add the snippets. For each member, add each snippet to the file in its path. Keep the projectId, commitSha, coverageOrigin, and runtimeSurface values of the snippet. When the file exists, add the snippet at the start of the entry module. Do not replace the file.

  6. Pass the commit SHA to the runtime.

    • Browser bundle: set VITE_FALLOW_COMMIT_SHA on the build step (the CI steps show it).
    • Server in a container: add the dockerfile_snippet lines to the Dockerfile, and build with --build-arg FALLOW_COMMIT_SHA=$GITHUB_SHA.
    • Server on a platform: the snippet reads the SHA that Vercel, Render, or Railway sets. On another platform, set FALLOW_COMMIT_SHA in the runtime environment.
  7. Add the CI steps. Add cloud.ci.steps to the job that builds and deploys, in the workflow from cloud.ci.workflows_detected. When that list is empty, or the project does not use GitHub Actions, ask the user which job deploys and adapt the steps to it. The checkout needs fetch-depth: 0. The upload step goes after the build step, and the build must emit source maps for the upload to send anything. The registration step goes after the deploy step. Follow cloud.ci.instructions.

  8. Tell the user what to do by hand. The user creates the keys in the Fallow Cloud dashboard, stores them as the secrets that the plan names, and deploys.

  9. Check the setup after the deploy. Ask the user to export FALLOW_API_KEY (a full key) in the shell. Run cloud.wait_command from the plan with --json added and $DEPLOYED_SHA set to the deployed commit SHA. It holds the project id. Then read the exit code:

    • 0: every step is done.
    • 1: a timeout. Read cloud.wait.activation.next. It names the step to do now and, where one exists, a command. activation is null when no read succeeded, and next.command can be null.
    • 2: an error. Read cloud.wait.message, or stderr when stdout has no JSON.

    The activation body can have step ids and states that this skill does not name. Report them as they are.

Common problems

  • beacon is problem: the beacon sends no commit SHA. Check step 5.
  • runtime stays waiting: the app has no production traffic yet, or the beacon commitSha is not the full SHA that CI used.
  • deployment is waiting: an older deployment still receives runtime. The new one becomes current on its first runtime data.
  • overlap is waiting: too few functions ran so far. Send more traffic.
  • source_maps is missing for an app that runs its source files without a bundle: it changes to not_needed when runtime joins the inventory.
  • overlap is problem: runtime paths do not match the inventory paths. A container that runs the code under another root, for example /app, needs --path-prefix on upload-inventory. A bundled app needs the source maps.
  • inventory fails in CI: the checkout is shallow. Set fetch-depth: 0.

References

  • Manual setup: the five steps by hand, for a fallow version without --cloud.

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 Fallow Cloud Setup AI skill do?

Connect a JavaScript or TypeScript app to Fallow Cloud production runtime coverage. Use when asked to set up Fallow Cloud, add the Fallow beacon, send runtime coverage to Fallow Cloud, or find out why Fallow Cloud shows no data for a repository. Do not use for local code-quality tooling; use fallow-setup for that. Do not use to read runtime data of a repository that is already set up; use the fallow skill for that.

Why use Fallow Cloud Setup on TypingMind?

Because you install it once and use it with any model. Fallow Cloud Setup 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 Fallow Cloud Setup in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/fallow-rs/fallow-skills/tree/main/fallow/skills/fallow-cloud-setup. 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 Fallow Cloud Setup?

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 Fallow Cloud Setup?

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

Is the Fallow Cloud Setup AI skill free?

Yes. It is published on GitHub by fallow-rs under the MIT 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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