Compile a task once, replay it forever with zero model calls
A successful run compiles into a skill — an ordered, parameterized program of browser actions that replays the same way every time.
POST /api/v1/skills/{id}/run → x-twin-llm-calls: 0What deterministic replay does
When a planner solves a task, Twin captures the exact sequence of actions and turns it into a skill. Replaying that skill is deterministic execution — no planning, no model in the loop, no per-run token bill. The same inputs produce the same steps, which is what makes the work cheap and auditable.
Zero LLM calls on replay
A compiled skill runs as a program, not a prompt. Replays cost a flat credit and never touch a model.
Parameterized, not brittle
Skills take typed inputs, so one compiled flow handles every customer, date, or record — not just the example it was trained on.
Same steps, every time
Deterministic execution means a flow that passed in testing behaves identically in production, run after run.
Self-healing handoff
If a page genuinely changed, the replay can fall back to a single re-plan and recompile — so drift fixes itself instead of failing silently.
From a goal to deterministic action
- 1Solve onceThe planner completes the goal on a live page and records every action it took.
- 2Compile to a skillThe successful trace is parameterized and stored as a replayable skill with a stable id and version.
- 3Replay deterministicallyLater runs execute the skill directly against the DOM — no model, flat credit, identical steps.
- 4Recompile on real driftIf the target page changed enough to break a step, Twin re-plans that step once and bumps the skill version.
See it on a real call
Running a compiled skill is deterministic — four steps, zero model calls, one flat credit.
curl https://api.twin-browser.com/v1/skills/book-slot/run \
-H "Authorization: Bearer $TWIN_KEY" \
-d '{ "inputs": { "day": "Tuesday", "slot": "09:00" } }'
# < x-twin-skill: book-slot@v3
# < x-twin-llm-calls: 0
# < x-twin-steps: 4- Solve oncedone
- Compile to a skillrunning
- Replay deterministicallyqueued
- Recompile on real driftqueued
What deterministic replay is
The facts — how it works, what it costs, and the signal you get back on every call.
| Property | Twin Browser |
|---|---|
| Execution | Deterministic, no model in loop |
| Inputs | Typed, parameterized |
| Versioning | Stable skill id + version |
| Replay cost | Flat credit per run |
| Drift handling | Single re-plan + recompile |
| Audit | Step list + session video |
Deterministic replay — common questions
What is the difference between a run and a skill?
What if the website changes?
Is replay really zero model cost?
Go deeper
The rest of the platform
Semantic dispatch cache
Re-phrased goals fuzzy-match a skill you already compiled — so the second request and every one after it skips the LLM entirely.
Agent & skill library
Every compiled skill is stored, versioned, and reusable — browse them, run them directly, and share them across your agents.
Live view & session video
Stream the browser session in real time, then keep a durable video of every run for debugging, audit, and proof of what happened.
Make every run cheaper than the last.
Start free, compile your first skill, and watch the marginal cost per run trend toward zero as your agents reuse what they have already learned.