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# galdr
> _galdr_ — Old Norse for a chanted spell: a sequence performed once and sung again.
**Record & Replay for agent skills.** Your agent just did a multi-step task well. Rather
than re-explain it next time, galdr records the *tool calls* it made and distills them into
a reusable **skill** the agent can replay with judgment. It runs entirely on your machine.
<p align="center">
<img src="assets/demo.gif" alt="galdr: record a task, distill it into a reusable skill" width="100%">
</p>
## What galdr records
Most of what an agent does has no screen to record. Running tests, editing files, git,
deploys, API calls: each is already a structured tool call with its input and its result.
galdr records that trace and distills it into a skill the agent reads and applies, not a
verbatim re-run.
Some apps only live behind a GUI, with no API or CLI. There the agent uses Computer Use,
the same pixel-based control, and that is the right tool for the job. galdr captures those
sessions too: a click and a keystroke are tool calls, so it keeps the action and, by
default, drops the screenshot (you can opt in to keeping frames to help author the skill).
There is now a second human-observation lane for browser workflows:
`galdr observe browser start <name> --url <url>` launches an isolated Chrome/Chromium
profile, injects a local CDP sensor, and records navigation, clicks, form changes, and
submits as semantic human events. Typed text is redacted by default; screenshots are not
captured. Native macOS human demonstrations are still the next sensor, not a shipped
claim.
## Quickstart
```sh
cargo install galdr
galdr setup skill # teach your harness how to drive galdr (once)
galdr rec start demo # ● recording — now do the task with your agent
# ... a few tool calls ...
galdr rec stop # ■ stopped — 6 steps
galdr distill # a faithful draft + an authoring brief; finish it with --from
```
No 26-character ids to copy — galdr resolves a recording by the latest, a name, or a short
prefix. Run `galdr` with no arguments for a one-screen overview of where you are.
## The loop
1. **Record** — start a recording, do the task with your agent, stop. Its tool calls are
captured automatically; nothing to narrate.
2. **Distill** — a replay of the tool calls is not yet a skill. `galdr distill` renders a
faithful draft and hands the agent an authoring brief: supply the *why*, the inputs that
vary, each step's intent, the gotchas. Install your version with `--from`. `--fast` takes
the mechanical draft as-is; `--auto` lets a local model write it.
3. **Replay** — the skill is discoverable by name in every harness on the machine. Invoke it
later with new inputs; interpret it, don't replay it verbatim.
## What else it does
- **`galdr observe browser`** — records a human-demonstrated browser workflow through a
local CDP sensor, with typed values redacted and no screenshots by default.
- **`galdr suggest`** — finds repeated tasks (the same shape across recordings) worth a skill.
- **`galdr bench`** — how reliably your skills replay, aggregated from the outcomes you record.
- **`galdr tui`** — a terminal UI: an Overview dashboard, then tabs for recordings, skills,
and harnesses; read a `SKILL.md` or a recording's (noise-filtered) steps at a glance.
- **`galdr skills` / `galdr doctor`** — a small readiness-scored skill catalog, and a health
check for your setup.
- Every read command takes `--json`, so an agent consumes galdr without scraping a table.
## One skill, every harness
galdr distills a skill once and links it into **every harness it finds**: Claude Code, Codex,
and Cursor. A skill recorded in one is discoverable in all of them. `galdr setup <harness>`
wires the sensor and prints the per-harness step, like trusting the hook in Codex.
## Local and private
- Everything lives on your machine: recordings and the catalog under `~/.galdr`, distilled
skills in your local skill directories. galdr makes **no network egress**: it never phones
home. The single opt-in exception, local-model distillation, talks **only to loopback**.
- The distiller redacts and generalizes the text it writes into a skill, and an install-time
gate **refuses any skill that still contains secrets or personal paths**. Computer-Use
screenshots are dropped by default (the action is kept, the screenshot isn't, for privacy).
The raw recording itself is not redacted and can hold sensitive command output, so keep that
in mind before sharing one. See [SECURITY.md](SECURITY.md).
- The recorder never breaks your agent session: if it fails internally it records nothing and
exits cleanly.
## Install
```sh
cargo install galdr # from crates.io
cargo install --git https://github.com/Arakiss/galdr # from source
```
Or grab a prebuilt binary from a [release](https://github.com/Arakiss/galdr/releases/latest):
each ships signed and checksummed (Sigstore + SHA-256) with an SBOM, for macOS and Linux
(arm64 + x86_64).
## Roadmap
Shipped: the record → distill → replay loop with **author-by-default** distillation,
**`galdr suggest`** and **`galdr bench`**, an **Overview-led TUI**, multi-harness skills and
sensors (Claude Code, Codex, Cursor), **Human Browser Observe** for browser workflows,
optional **vision-assisted authoring** (keep screenshots ephemerally so the authoring pass
writes semantic GUI steps), and a safe, redacted export path.
Next: a live end-to-end recording verified in each harness, native macOS human observation
behind explicit permissions, and a multi-agent broker over the same model.
## Contributing
See [CONTRIBUTING.md](CONTRIBUTING.md). Commits follow
[Conventional Commits](https://www.conventionalcommits.org/); the project uses
[Semantic Versioning](https://semver.org/).
## License
[MIT](LICENSE) © Petru Arakiss. Simple and permissive: use it, fork it, ship it.