Record a journey once, re-verify it forever

SkillWeb & browsing

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.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Record a journey once, re-verify it forever skill

What this skill tells your AI

The instructions your AI receives, as published by reticlehq/reticle in skills/replay-user-flows/SKILL.md and read by ahel’s review.

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

reticle_run({ tool: "reticle_record", sessionId, args: { action: "start", recordingName: "create-task" } })
   … drive the golden path with reticle_act / reticle_act_sequence …
reticle_run({ tool: "reticle_record", sessionId, args: { action: "stop", recordingName: "create-task" } })
reticle_run({ tool: "reticle_flow_save", sessionId, args: { flowName: "create-task" } })

That writes .reticle/flows/create-task.json. Commit it: any agent on the repo can then replay it.

Annotate the business outcome, not just the clicks, so a replay proves the journey achieved something:

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_run({ tool: "reticle_flow_replay", sessionId, args: { flowName: "create-task" } })

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_flow_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_run({ tool: "reticle_verify", sessionId, args: { 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_run({ tool: "reticle_verify", sessionId, args: { 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 })
// → { 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.

Signals

GitHub stars
479
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Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
replay-user-flows
Source
github.com/reticlehq/reticle