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forkprobe

Jayden-X-L/forkprobe

Compare multiple skills on the same task and pick the winner.

725PythonMITSource

Install

Add forkprobe to DeepSeek Harness

via GitHub · GitHub source

Installing from GitHub runs the project's build script, which pnpm blocks until you allowlist it — run the command once and pnpm prints the exact key to add under `allowBuilds` in ~/.dsh/profiles/web/pnpm-workspace.yaml.

What happened when we ran it

Installed cleanly when we ran it

Every command here is run in a throwaway container against a clean profile, and the result is whatever the harness recorded — not a guess from the source. Last run 13d ago.

Show it in your README

install verified — dshmarketplace

For maintainers: the badge serves this listing's latest sandbox verdict, so a re-run updates it on its own — and it links readers to the full result here.

Due diligence

Before you install forkprobe

  • Source of record: Jayden-X-L/forkprobe — present in the community registry that DSH's own plugin market installs from.
  • Licensed under MIT.
  • Detected: terminal surface. Read the source before granting these.
  • A listing here is not a security review. Plugins run with your agent's permissions.

The AI take

What it is — It runs multiple skills in parallel on the same task, generates a local HTML report, and lets you choose the winner to continue execution.

Who it is for — In academic polishing, SCI writing, PPTX generation or image prompt design tasks, having the agent compare multiple skill outputs in parallel can help you pick the winner. For tasks where the answer or tool path is already clear, people who can execute directly can skip it because direct execution will be faster.

Watch out — Sandbox testing passed; it was installed in a fresh profile and registered into it. Static checks indicate it will execute shell commands, but no other issues found. Installation from source requires manually allowing the build script.

The verdict — I would install it because it turns skill selection into an observable parallel execution process, but only when deliverables need file previews and QA.

Generated by grok-4.6, and a starting point rather than a verdict. Where it says a plugin installs or does not, that is from a real run in a clean profile — everything else is read off the repository. Trust the source over this.

What forkprobe does

Parallel task paths are compared and converge into one selected result with a local report.

forkprobe is a DeepSeek Harness plugin that compares several skills on the same task and helps select a winner. It runs a baseline and multiple candidate skills with the same input, then opens a local HTML report containing the outputs, elapsed time, estimated token use, file previews and AI review suggestions. In DSH, the native forkprobe_compare tool starts candidate subagents in parallel. After selection, the report’s “Continue” action creates a local handoff and returns the chosen result so the current agent can continue with the winning skill.

Candidates can come from the local installed-skill set, EverMind Skill Hub, GitHub or a BYO path. The repository also documents direct runs with python3 scripts/compare.py, using options such as --input, repeated --skill, --judge and --output. The plugin scans local skills, removes duplicates and can optionally support anonymous winner sharing.

This is intended for tasks where output quality or artifact quality is uncertain, including writing, PPTX, figures, research reports, web pages and video. It is a poor fit for simple, deterministic work where the correct tool path is already clear. External GitHub candidates still require checks for licence, dependencies and output paths. The terminal surface reaches the environment to run comparison scripts and candidate pipelines, which may create reports and generated files with the agent’s permissions; the README does not describe an additional isolation layer. DSH users do not need a nested dsh process, and the plugin does not copy DSH credentials.

forkprobe documentation

How it behaves

forkprobe treats skill selection as a comparison run. It recommends a small candidate set, runs baseline and the selected skills with identical input, and writes a local HTML report. Depending on the task, the report can include text, previews, source files, QA results, media metadata, estimated token use, elapsed time and AI review comments. The documented workflow ends with a user-selected winner and a continuation handoff.

In DSH, the native forkprobe_compare tool starts candidate subagents in parallel. The README states that this native implementation does not launch a second dsh process or copy DSH credentials. It also retains installation confirmation, local Skill scanning, candidate de-duplication and optional anonymous winner sharing.

Commands

The README shows a direct comparison command:

python3 scripts/compare.py --input /tmp/forkprobe-input.txt --skill baseline --skill writing-anti-ai --skill humanizer-zh --skill remove-ai-flavor-writing-skill --judge --output /tmp/forkprobe-report.html

--input takes an input file, --skill can be supplied for each candidate, --judge enables the documented AI review step, and --output specifies the report path. The DSH-native tool is named forkprobe_compare; its full argument schema is not included in the supplied README.

Candidate sources and outputs

Candidates may be recommended from the curated directory, locally installed Skills, EverMind Skill Hub, GitHub or a BYO path. The documented report types include text outputs, PPTX files, PNG/SVG/PDF/TIFF figure assets, research evidence files, runnable web pages, screenshots, MP4 media, subtitles, transcripts and source code, depending on the comparison mode.

Known limits

Image-generation comparison is listed as planned rather than supported. For external GitHub candidates, the README recommends checking licence, dependencies and final output paths before execution. Simple deterministic tasks are explicitly outside the intended use case.

Written from the project's own documentation and kept in sync with it. Where the two disagree, the source is authoritative — read the README on GitHub

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