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
- Open Settings → Plugins → Skill Workshop. Choose the project skill root (
.dsh/skills) or user skill root (DSH_HOME/skills).
- Run regular DSH tasks. Eligible successful turns may generate skill candidates; short, failed and interrupted tasks are skipped.
- Once a candidate has independent evidence from two distinct turns and passes checks, the workshop publishes it without asking for per-skill manual confirmation.
- 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.
Optional plugin diagnostics
The plugin optionally emits privacy-restricted operation and failure metadata to dsh-plugin-diagnostics. Diagnostics contain event type, status, fixed error code and elapsed time; conversation content and credentials are not logged. The diagnostics plugin is optional and does not change the host requirements.
