DeepSeek Harness Plugins
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dsh-evolve

Self-evolving memory and skill lifecycle engine with deterministic zero-token recall, tiered approval, and anti-bloat pruning for DeepSeek Harness.

UI & ProductivityDeveloper ToolsSecurity & PolicySkills & Workflows
8GitHub Stars0ForksUpdated2026-09-20

Install

$ dsh plugin --profile web add github:chenzheshushi-commits/dsh-evolve

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-evolve is an installable DeepSeek Harness plugin that provides a durable, self-evolving memory and skill management subsystem. It enables agents to retain structured knowledge across sessions using zero-token deterministic recall (combining bigram-Jaccard and SQLite FTS5 BM25 via Reciprocal Rank Fusion). The plugin turns repeated procedures into crystallized, versioned SKILL.md documents, manages an active-stale-archived lifecycle with rollback safety, enforces tiered write approvals, and includes anti-bloat convergence algorithms alongside a dedicated web settings UI for pruning and review.

Key Capabilities

  • Durable cross-session structured memory (facts, preferences, decisions, lessons, todos) stored in JSON with readable Markdown mirrors
  • Deterministic zero-token hybrid recall fusing bigram-Jaccard similarity and SQLite FTS5 BM25 via RRF
  • Tiered approval gate to automatically confirm reversible, user-anchored memories while queuing uncertain writes for review
  • Skill crystallization from high-value lessons into versioned, editable SKILL.md documents with pre-operation backups and rollback
  • Anti-bloat convergence and controlled pruning (soft-delete, memory budget enforcement, coldness scoring, and duplicate skill merging)
  • Isolated background turn review without interrupting main loop context or prompt cache
  • Web settings panel integration for memory approval queues, controlled two-stage pruning, and lifecycle configuration

Useful For

  • Maintaining long-term user preferences and project-level context across multiple DeepSeek Harness sessions
  • Automatically extracting reusable procedural workflows into version-controlled skills based on repetitive agent tasks
  • Preventing memory and skill bloat through deterministic deduplication, power-law coldness ranking, and safe archiving

Who It Fits

  • Developers and power users building persistent workflows with DeepSeek Harness
  • Users needing deterministic, local-first agent memory without relying on vector database APIs or token-heavy embedding models

Documented Limitations

  • Requires Node.js >= 22.5.0 due to built-in node:sqlite FTS5 full-text search requirements
  • Requires external git and tar binaries on PATH for optional automated memory checkpoints and skill backup rollbacks
  • Primary testing performed on Linux and macOS; Windows is untested

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
chenzheshushi-commits/dsh-evolve
Registry source
GitHub · dsh-plugin topic
Registry classification
Plugin
Source checked
c2f2589 · 2026-09-21

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
8GitHub stars
Forks
0
Open issues
0
Last commit
2026-09-20
Last release
2026-09-20
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