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Replay User Flows

OrganizationPopular
reticlehq
replay-user-flows

Turn a user journey you just clicked through into a saved regression check that re-runs deterministically, with no model in the loop and no test code to write. Use when you have driven the same flow twice, when the user wants regression coverage without a Playwright suite, when a refactor needs proving against every existing journey, or when re-verifying by hand is costing a full drive every time.

Overview

Publisherreticlehq
Repositoryreticle
Skill namereplay-user-flows
Stars
1.2K
Forks
176
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Replay User Flows 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/reticlehq/reticle/tree/main/skills/replay-user-flows
  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/reticlehq/reticle.git /tmp/reticle
mkdir -p .claude/skills
cp -r /tmp/reticle/skills/replay-user-flows .claude/skills/replay-user-flows
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Replay User Flows 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 Replay User Flows 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 Replay User Flows 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.

Record a journey once, re-verify it forever

Exploring an app to find a journey is the expensive part, and re-driving it with a model pays that cost again on every change. Reticle flows pay it once: the journey is saved with semantic anchors and replayed deterministically afterwards.

Needs Reticle wired in the project. Not there? RETICLE_INSTALL_SOURCE=npx_skill npx @reticlehq/server@latest init, then the install-and-verify skill.

Record

You do not have to ask. A drive is saved as a flow automatically when the session ends, written to .reticle/flows/. Commit it: any agent on the repo can then replay it.

What decides whether it is worth committing is how you drove it. A step keeps a consequence only when you declared one, so drive the golden path like this:

reticle_act_and_wait({ sessionId, ref, action: "click", until: { kind: "signal", name: "task:created" } })

That step replays as a test. A bare reticle_act saves a click with nothing to prove, and replays green through any regression.

To name a flow deliberately rather than take the automatic one, reticle_record and reticle_flow_save do it through reticle_run { tool, args }. Neither is advertised, and neither needs to be: reticle_run is on the default surface and dispatches to any registered tool by name. RETICLE_ADVERTISE_ALL_TOOLS=1 advertises them outright instead, which suits a suite that calls by name rather than a running agent.

Annotate the business outcome, not just the clicks, so a replay proves the journey achieved something. Extended surface, like the two above:

reticle_run({ tool: "reticle_annotate", sessionId, args: { flow: "create-task", kind: "intent", text: "create a task and see it in the list" } })
reticle_run({ tool: "reticle_annotate", sessionId, args: { flow: "create-task", kind: "success-state", signal: "task:created" } })

You do not need to add data-testid first. A step whose element has no testid is anchored on its component and source location automatically, and a testid-preserving refactor still replays green.

Replay

reticle_verify({ sessionId, action: "change", files: ["src/tasks/TaskList.tsx"] })

That replays the flows covering those files. To replay one named flow directly, reticle_flow_replay is reached through reticle_run.

Three statuses, and the failures are legible rather than blind:

statusmeansnext
okevery anchor resolved, every expectation helddone
driftan anchor missed: a renamed testid, a signal that never firedread decision.nextAction; it names the file:line and the closest surviving anchor
errorthe flow file is missing or invalid, or a step failed at runtimefix from the error envelope's failed step

On drift, reticle_verify { action: "heal" } proposes the nearest-match rebind so flows do not rot. Apply it when the rename was intentional; treat it as a finding when it was not.

Re-verify the whole suite after any change

reticle_verify({ sessionId, action: "flows" })
// → { status, total, passed, failed, failures: [{ flow, verdict, whatChanged, whereInSource, nextAction }] }

One call, every saved flow, no model per flow. Only failures carry detail, so a green suite is cheap to check. Build → flow_verify → fix from each nextAction → repeat is the regression loop, and it is the point of recording in the first place.

Only the flows your change could have broken

On a large suite, replaying everything after a one-file edit is waste. Hand it the diff instead:

reticle_verify({ sessionId, action: "change", since: "HEAD~1" })

It works out which saved flows cover the files you edited and replays only those. Give it a git ref or the file list. Use this in the inner loop and flow_verify before you ship: the narrow one is fast, the whole one is the guarantee.

Which of your flows actually prove anything

reticle_run({ tool: "reticle_domain", sessionId })   // not advertised, one hop away
// → { flowCount, coverage: { asserted, presenceOnly, assertionFree }, gaps: { declaredUntestedSignals, … } }

A recorded flow that asserts nothing replays green through any regression: it proves the clicks still resolve, not that the app still works. Check this after a recording session: anything landing in assertionFree needs an annotate pass with a success-state, or it is decoration.

When NOT to record

A journey you will run once is cheaper to drive with reticle_act_and_wait and forget. Record the flows that define the product (the ones a regression in would be a bad day) and leave exploratory drives unsaved. A suite of forty half-meant flows costs more attention than it returns.

Honesty

A replay reports what happened. drift is not a pass, and healing a flow to make it green when the app genuinely broke is the one thing that makes the whole suite worthless. If the rename was not intentional, the drift is the finding: report it with the whereInSource pointer.


Full flow reference, one page: curl https://docs.reticle.sh/flows.md. Index of everything: curl https://docs.reticle.sh/llms.txt.

Frequently asked questions

What does the Replay User Flows AI skill do?

Turn a user journey you just clicked through into a saved regression check that re-runs deterministically, with no model in the loop and no test code to write. Use when you have driven the same flow twice, when the user wants regression coverage without a Playwright suite, when a refactor needs proving against every existing journey, or when re-verifying by hand is costing a full drive every time.

Why use Replay User Flows on TypingMind?

Because you install it once and use it with any model. Replay User Flows 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 Replay User Flows in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/reticlehq/reticle/tree/main/skills/replay-user-flows. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Replay User Flows?

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 Replay User Flows?

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

Is the Replay User Flows AI skill free?

Yes. It is published on GitHub by reticlehq 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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