Skill Workshop for DeepSeek Harness

简体中文 · DeepSeek Harness · Security

A native DSH skill authoring and self-learning workshop. Import, edit, and manage SKILL.md-based skills, or let completed tasks produce candidate procedures that can be promoted automatically after evidence checks.

Features

• Browse, create, update, and import native DSH skills from Markdown, directories, and supported skill archives.
• Learn reusable workflows from completed tasks without granting a model direct write access to the published skill root.
• Unattended auto-promotion by default: the same skill needs evidence from at least two distinct successful task turns, each linked to genuine user requests and assistant results.
• Guard against uncontrolled growth: bounded candidates and managed skills, stale-candidate expiry, history pruning, and retirement of unused workshop-owned skills.
• Protect manual edits and externally managed skills from automated overwrite; recover interrupted publications using durable records and hash checks.

Install

Requires native DSH skill and session services; runtime and peer requirements are listed in package.json.

dsh plugin --profile desktop add github:Kerberos255/dsh-skill-workshop

Replace the profile name as needed, pin a commit if desired, and restart DSH after installing or upgrading code.

Quick start

1. Open Settings → Plugins → Skill Workshop. Choose the project skill root (.dsh/skills) or user skill root (DSH_HOME/skills).
2. Run regular DSH tasks. Eligible successful turns may generate skill candidates; short, failed and interrupted tasks are skipped.
3. Once a candidate has independent evidence from two distinct turns and passes checks, the workshop publishes it without asking for per-skill manual confirmation.
4. Inspect candidate/managed counts and customize limits, or use the editor/import screen for manual skills. Manual import and external edits retain their review flow.

Growth-control defaults

Bound	Default
Pending auto-learned candidates per workspace	32
Workshop-managed skills per workspace	64
Candidate expiry without fresh evidence	45 days
Automatic learning cooldown / daily attempts	60 minutes / 8
Managed skill retirement / grace	90 days idle / 7 days
Processed automatic-learning records	180 days

Candidate evidence is a source and repetition safeguard, not proof that a procedure was re-executed or externally validated. Automatic publication will not overwrite a manually edited skill, and reaching the managed cap blocks new automatic skills.

Data and testing

Candidate and publication state live in local DSH SQLite; published skills remain files in the chosen native skill root. Interrupted changes are checked and may require human review only for conflicts or unusual recovery states. No secret/session database belongs in the public repository.

Run npm test for portable tests. Live model learning and DSH registry/UI behavior require separate integration testing. See config.example.json and SECURITY.md.

Related: Dream & Memory · Instruction Files.

MIT licensed. See LICENSE.