VanadisGithub/dsh-skill-evolution

dsh-skill-evolution

English | 中文

A skill self-evolution plugin for DeepSeek Harness (DSH): it watches agent execution traces and, at the end of every successful turn, fires an LLM review when signals trip — distilling workflows worth reusing into crystallized skills registered in the skill catalog. Later runs of the same workflow fold fresh lessons into the existing skill — skills are alive and get better with use.

Highlights

• Event-driven crystallization — review fires at turn end, not on pure frequency counting; any of three signals (complex / recovered / repeated) sends the turn to review
• Strict review gate — LLM reviewer plus a hard do-not-capture list keeps junk out; NOTHING_TO_SAVE is a first-class outcome
• Skills keep evolving — new evidence is merged into existing skills (version bump); semantic dedup (FOLD_INTO) blocks near-duplicates reached through different tool sequences
• Full management UI in Settings — tabbed skills/settings views, master switch, skill cards with value/version/invocation chips, a tuning pipeline diagram with controls embedded in-place, and editable review prompts
• Persistent & self-contained — skills persist under generated/ and re-register on restart; host half depends only on node: builtins

Screenshots

Skills tab — skill cards with value (high/medium/low), version, invocation count and update-time chips; expand for crystallization signals, tool sequence and the full SKILL.md:

Settings tab — status overview, review model, generation language and the configuration panels:

Thresholds & controls — every parameter embedded in the pipeline diagram, editable exactly where it takes effect:

How it works

Signal detection

At every turn end (turn/end), successful traces meeting a minimum step count are checked against three signals:

Corroboration gate (complexRequiresCorroboration, on by default): a lone complex signal is weak evidence (any long turn qualifies), so by default it must be corroborated by recovered or repeated before review; withheld turns are logged and re-submitted once the sequence later earns a corroborating signal.

Review gate

The reviewer LLM receives full evidence (signals, user intent, tool sequence, per-step outcomes and summaries) plus the existing evolved-skill list, and answers one of: SAVE (crystallize a new skill), FOLD_INTO (merge into an existing skill), or NOTHING_TO_SAVE.

Hard do-not-capture list — the following is never crystallized:

• environment-dependent failures (missing binaries, unconfigured credentials, uninstalled packages)
• negative claims about tools ("tool X is broken" would freeze into a long-lived refusal)
• transient errors cured by a retry (the lesson is the retry pattern, not the original error)
• one-off task narratives, unresolved failures

Continuous evolution

• Progressive improvement — when new evidence lands on an existing skill's sequence (especially error-recovery experience), the reviewer merges the new lessons into the skill body and bumps the version instead of creating a duplicate
• Semantic dedup (FOLD_INTO) — the same class of experience can reappear via a different tool sequence, which pure sequence matching cannot catch. The crystallization reviewer sees the existing skill list (name + description) and may answer VERDICT: FOLD_INTO: <existing-skill-name>; the host then runs the improvement pipeline against that skill

Output format

Class-level, kebab-case skills with the standard SKILL.md shape (# Title / ## When to Use / ## Procedure / ## Pitfalls / ## Verification). NAME and section headings are always English; body language follows skillLanguage.

Install

Option 1: official CLI (once published to npm)

dsh plugin --profile web add dsh-skill-evolution

The bundled dsh.bundle.patch mounts automatically — no profile edits needed.

Option 2: git clone + link

git clone https://github.com/VanadisGithub/dsh-skill-evolution.git ~/Code/dsh-skill-evolution

Add to dependencies in ~/.dsh/profiles/web/package.json:

"dsh-skill-evolution": "link:/Users/<you>/Code/dsh-skill-evolution"

Then run pnpm install in ~/.dsh/profiles/web and hard-refresh the browser.

Option 3: file:// manual mount (prototype debugging)

Place the repo anywhere (e.g. ~/.dsh/plugins/skill-evolution/) and append to ~/.dsh/cordis.patch.yml:

- insert:
  - id: 'skill-evolution'
    name: file:///Users/<you>/.dsh/plugins/skill-evolution/plugin.mjs?v=1
    config:
      minToolCalls: 5
      minPatternOccurrences: 3
      autoRegister: true
      llmProvider: deepseek      # adjust to a provider available in your deployment
      llmModel: deepseek-chat

?v=N busts the Node module cache; bump it whenever plugin.mjs changes. Host-half changes need a loader patch replay; browser-half changes just need a hard refresh.

⚠️ Pick exactly one option — duplicate mounts cause a dual instance.

Configuration

Every key below works both via mount config and directly in the Settings panel (panel edits persist to runtime-config.json in the plugin directory, overriding mount config across restarts):

Management panel

The "Skill Evolution" section in Settings, a two-tab layout with a master switch in the top-right corner:

• Skills tab — skill cards: name, description, value chip (high/medium/low, rated from crystallization and improvement counts), version, invocation count (skill-tool calls + user-explicit injection), update time; expand for crystallization signals, tool sequence and the full SKILL.md; delete/clear use two-step confirmation
• Settings tab — status overview, review model (provider-cascaded picker), generation language, and four panels:
  • Thresholds & controls — all thresholds drawn as one pipeline (turn end → observation filter → signal detection → corroboration gate → LLM review → crystallization → continuous improvement), each parameter embedded in the step where it takes effect
  • Review prompts — edit the crystallization/improvement system prompts in place (draft–save workflow), resettable to built-ins at any time
  • Live pattern stats — observed tool-sequence patterns and trigger counts
  • Danger zone — clear all evolved skills (two-step confirmation)

Data & privacy

• Trace evidence lives in memory only (bounded cache), never on disk
• On disk: crystallization artifacts (generated/*.json: SKILL.md body, signals, sequences, stats) and runtime-config.json
• Review LLM calls go through providers already configured in the deployment — no external endpoints introduced
• Uninstalling the plugin removes all data (generated/ goes with the directory)

Layout & development

Two-sided plugin package (host + web client):

node --check plugin.mjs && node --check client.js   # syntax
node test/smoke.mjs                                  # smoke test
node test/screenshot.mjs <output-dir>                # re-capture README screenshots

Only node: builtins are imported — no @deepseek-ai packages — avoiding any dual-instance conflict with the host's own cordis.

License

MIT