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.

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). The one thing out of scope is a human's manual gestures in an app the agent never touched.

Quickstart

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 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.
• The recorder never breaks your agent session: if it fails internally it records nothing and exits cleanly.

Install

cargo install galdr                                   # from crates.io
cargo install --git https://github.com/Arakiss/galdr  # from source

Or grab a prebuilt binary from a release: 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), optional vision-assisted authoring (keep the 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, capture of a human's GUI gestures (the deliberate scope gap above), and a multi-agent broker over the same model.

Contributing

See CONTRIBUTING.md. Commits follow Conventional Commits; the project uses Semantic Versioning.

License

MIT © Petru Arakiss. Simple and permissive: use it, fork it, ship it.