DeepSeek Harness Plugins
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dsh-continual-harness

Continual learning and self-improvement harness plugin providing persistent memory, automated review, and rollback.

Developer ToolsSecurity & PolicySkills & Workflows
9GitHub Stars1ForksUpdated2026-09-10

Install

$ dsh plugin --profile <name> add dsh-continual-harness

Plugin Overview & Capabilities

AI-assisted organization based on the public repository snapshot. The content must be grounded in source evidence and does not replace compatibility or security verification.

source-grounded
dsh-continual-harness is a DeepSeek Harness plugin designed for self-improving AI agents. It implements the Experience Solidification Protocol (ESP) to persist reusable prompt notes, memories, skill contracts, and subagent specifications across sessions. The plugin features state projection into agent context, automatic periodic refinement gates, manual refinement tools/commands, atomic rollbacks, and explicit A/B benchmarking.

Key Capabilities

  • Injects state and memory context incrementally into agent pre-step workflows based on digest changes
  • Executes automated LLM review, planning, and refinement on turn intervals or session compaction
  • Provides harness_refine tool and /refine slash command with atomic apply and rollback capabilities
  • Materializes applied skill contracts into real DSH SKILL.md bundles
  • Executes code-owned A/B benchmarks using frozen test cases and baseline reference snapshots
  • Supports session wrap-up recommendations and injection telemetry logging

Useful For

  • Persisting learnings, error patterns, and instructions across multiple agent sessions
  • Automatically refining agent prompt directives and skills during long-running tasks
  • Running controlled A/B benchmarks before adopting prompt or skill modifications
  • Safely rolling back problematic agent memory or contract updates

Who It Fits

  • Developers building autonomous or self-evolving agents in DeepSeek Harness
  • Engineers requiring cross-session knowledge persistence and structured agent memory
  • DSH power users optimizing agent prompt behaviors with regression testing

Documented Limitations

  • Real end-to-end evaluation requires configured LLM capability and API keys
  • Concurrent refinement writes follow a last-writer-wins model without multi-process serialization
  • Compaction triggers silently skip when the session compaction capability is absent
  • Content-shrink guard is not yet implemented; only per-commit entry growth is capped

DSH Compatibility

Version-specific runtime evidence collected by DSH Plugin. A missing result means we have not tested that combination yet.

Not tested yetNo runtime compatibility tests have been published yet.

Security Signals

Objective signals discovered from package metadata and source inspection. These are not a guarantee that a plugin is safe.

No automated security signals have been published yet.

Source & Registry Notes

Public provenance, Registry classification, and the latest source check for this entry, kept separate from runtime verification.

Source repository
jasen215/dsh-continual-harness
Registry source
GitHub · dsh-plugin topic
Registry classification
Plugin
Source checked
d4e336a · 2026-09-14

This project is independently indexed from public source information. DSH Plugin is not affiliated with DeepSeek or the plugin author. Always check the author repository before installation.

Repository Activity

GitHub stars
9GitHub stars
Forks
1
Open issues
0
Last commit
2026-09-10
Last release
2026-09-10
For maintainers

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Listed on DSHPlugin.app