deepseekplugins

DeepSeek Harness plugin

dsh-record-replay

DeepSeek Harness plugin: model-facing orr_* tools plus the open-record-replay skill for the Open Record/Replay macOS workflow recorder.

Workflow & Automation
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Source facts

Repository
humblebanana/dsh-record-replay
Latest update
Aug 13, 2026
Category
Workflow & Automation
GitHub stars
11
Format
plugin
Catalog evidence
Upstream dsh.bundle evidence
Evidence path
package.json#dsh.bundle
Checked against
0.1.0-rc.8
Upstream check date
2026-08-20

This evidence comes from the upstream catalog. This site has not installed, run, or security-reviewed the plugin.

Install

Start with a prompt that asks an agent to review the GitHub repository and source. Switch to the command if you want to install it yourself.

Copy this prompt into DSH, Codex, or another agent and ask it to review the GitHub repository and source first.

Do not install or run any commands yet. Read this plugin's GitHub repository, README, and relevant source code. Then answer the questions below clearly and directly so I can decide whether it fits my needs:

1. What is this plugin, and what problem does it solve?
2. Who is it for, and what are its typical use cases?
3. How is it used after installation? Include one minimal example.
4. What known limitations or privacy, security, compatibility, or maintenance risks does it have?
5. Give a clear recommendation: recommend, conditionally recommend, or do not recommend, with reasons.

Distinguish statements documented by the repository, inferences from source code, and unknowns. If evidence is insufficient, say so explicitly. Do not guess or simply repeat the README.

GitHub: https://github.com/humblebanana/dsh-record-replay
Plugin: dsh-record-replay
Author: humblebanana

Check the source files

Read the README and other files from this plugin directory before installing.

File explorer3 files
README.mdSource · read only

dsh-record-replay

A DeepSeek Harness plugin that turns the Open Record/Replay macOS workflow recorder into first-class harness capabilities.

It registers the open-record-replay skill and **six model-facing orr_* tools**, so an agent can learn a user-demonstrated desktop workflow: record the user's real macOS actions, validate the evidence, and package it for the host agent's native skill creator.

user demonstrates a workflow
  -> orr_record_start            (capture session.json + events.jsonl)
  -> orr_record_stop             (finalize)
  -> orr_session_validate        (check the evidence against the contract)
  -> orr_session_events          (read what the user actually did)
  -> orr_skill_prepare           (package a skill-input directory)
  -> host skill creator

Requirements

  • macOS (the recorder's native backend is Swift; it needs Xcode Command Line Tools).
  • Node.js >= 22.19 (the Harness runtime).
  • A DeepSeek Harness installation.
  • An open-record-replay

checkout whose bin/orr.js the plugin invokes.

Install

Build the package and add it to a profile as a bundle:

git clone https://github.com/<you>/dsh-record-replay.git
cd dsh-record-replay
pnpm install
pnpm build
pnpm pack                      # produces dsh-record-replay-0.1.0.tgz
dsh plugin --profile web add ./dsh-record-replay-0.1.0.tgz

dsh plugin add records the package in the profile's package.json dependencies and dsh.profile.bundles, and the harness heals the profiles/node_modules fallback so the bundle resolves. The shipped cordis.patch.yml mounts a neutral row; point it at your checkout by overlaying the row from your profile's cordis.patch.yml:

- id: record-replay
  config:
    repoRoot: '/absolute/path/to/open-record-replay'
    runsOut: 'runs'
    skillInputsOut: 'skill-inputs'

The profile patch file is hot-reloaded, so the running GUI picks the plugin up without a restart. Restart the Harness if you are not on a live profile.

Configuration

KeyDefaultMeaning
cliPathenv ORR_CLI_PATHExplicit path to bin/orr.js. Overrides repoRoot.
repoRootenv ORR_REPO_ROOTPath to an open-record-replay checkout; the CLI is <repoRoot>/bin/orr.js.
runsOutrunsWorkspace-relative recordings directory.
skillInputsOutskill-inputsWorkspace-relative skill-input packages directory.

The CLI runs with the session workspace as its working directory, so recordings and skill packages land where the agent's filesystem tools can read them.

Tools

ToolCLI mappingPurpose
orr_permissions_checkpermissions checkVerify Accessibility / Input Monitoring before recording.
orr_record_startrecord startBegin capturing the user's demonstration.
orr_record_stoprecord stopFinalize the session after the user finishes.
orr_session_eventssession eventsRead the evidence stream (events.jsonl), capped at limit events.
orr_session_validatesession validate-recordingCheck the recording against the official contract.
orr_skill_prepareskill prepareBuild the evidence package for the host skill creator.
orr_skill_create— (built-in)Built-in skill creation fallback. Generates a SKILL.md skeleton from a recorded session following the Anthropic skills spec (kebab-case name + description frontmatter, progressive-disclosure body, evals/evals.json), validates agent-authored drafts, and installs to ~/.agents/skills/<name>/. Prefer a host-native Skill Creator when one exists; this is the fallback.

Development

pnpm install
node scripts/link-dsh.mjs    # links @deepseek-ai/* from a DSH checkout/harness
pnpm build                   # tsc
pnpm test                    # vitest
pnpm validate                # build + test

Tests assert the registration contract and exercise the CLI runner through a stubbed subprocess service; node scripts/link-dsh.mjs --path <dir> points the @deepseek-ai scope at an explicit DSH checkout.

License

MIT — see [LICENSE](./LICENSE).

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