V4 · Adaptive Skills

Skills That Learn From Experience.

Track observed skill use and explicit outcomes, then inspect whether a derived routing hint is supported. The statistical analysis path runs locally without an additional LLM call. Exposure alone is not evidence that a skill worked.

V4 AGPL v3 Local analysis Inspectable hints IDE-agnostic
THE PIPELINE

How skill evolution works.

Three stages — observe, analyze, evolve. The statistical path runs locally; optional providers and connectors retain their own network behavior.

01 · Observe

Observe

When installed and supported by the client, hooks can capture selected tool events and context. Verify event coverage, truncation, redaction, storage, and runtime cost for the specific client.

02 · Analyze

Analyze

SkillPerformanceMiner can build traces and outcome heuristics from available observations. Measure consolidation cost and distinguish explicit outcomes from inferred signals.

03 · Evolve

Evolve

Configured soft prompts can surface bounded routing hints. Treat derived assertions as untrusted context until their evidence base and effect are verified.

V4 Operator check

Treat derived skill hints as evidence, not authority.

Verify the installed V4 runtime with slm --version, inspect the configured observation paths, and review the evidence before relying on a derived routing hint.

INTEGRATIONS

IDE compatibility.

The backend is IDE-agnostic. Any client can POST tool events. The shipped hook currently supports Claude Code.

IDE Status Integration
Claude Code Supported Auto-registered via slm init
Any IDE API available POST to /api/v3/tool-event
Cursor Planned Adapter in development
Windsurf Planned Adapter in development
VS Code / JetBrains Planned Extension adapter
ENHANCED OBSERVATIONS

Claude Code workflow integration.

Everything Claude Code (ECC) is an external project whose observation files can be imported by the documented SLM ingest path. Its behavior, license, and compatibility remain independently versioned.

slm ingest --source ecc can import supported ECC observation files. Preview with --dry-run, inspect counts and omissions, and do not assume every ECC artifact is covered.

ECC is optional. The importer reads external data — review consent, provenance, redaction, and retention before writing it into SLM.

ecc ingest
# Import ECC observations into SLM $ slm ingest --source ecc Inspect ingested, skipped, and error counts
# Preview without writing $ slm ingest --source ecc --dry-run Review the dry-run result before writing ✓ review consent + provenance before enabling
FOUNDATIONS

Research design context.

These external references are design context, not proof that the current SLM package reproduces their results.

EvoSkills

External preprint; inspect its protocol and scope before transferring any result to SLM.

arXiv:2604.01687

OpenSpace

External repository; inspect its current code, license, and release evidence directly.

github.com/HKUDS/OpenSpace

SkillsBench

External preprint; its benchmark does not establish current SLM product performance.

arXiv:2602.12670

SoK: Agent Skills

External survey; use its taxonomy and security findings only within the paper's stated protocol.

arXiv:2602.12430
GET STARTED

Start tracking skill performance.

Install SLM V4, enable only the observation paths you consent to, and verify signal quality before changing routing.

Open source under AGPL v3 · A Qualixar Research Initiative