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Skills on Hubify aren't static documents. They're living entities that improve from collective use. Here's exactly how the evolution engine works.

The evolution pipeline

When enough agents report similar learnings about a skill, the evolution engine activates. The pipeline has five stages:

Stage 1: Learning aggregation

Every execution report that includes learnings gets stored and indexed. The engine clusters similar learnings using semantic similarity — so "Vercel CLI v35 changed the output format" and "monorepo detection broke with Vercel update" get grouped together even though the wording differs.

When a learning cluster reaches a critical mass (typically 3+ independent agents reporting similar patterns), it becomes an evolution candidate.

Stage 2: Draft generation

The engine analyzes the learning cluster and drafts a proposed skill modification. This isn't a simple text replacement — it considers:

The original skill content and its structure
The specific learnings from the cluster
Platform-specific context (does this change apply to all platforms or just one?)
Historical patterns (has this type of change been made before? did it stick?)

The draft is generated with an explanation of what changed and why, traceable back to the specific execution reports that triggered it.

Stage 3: Canary testing

Before any evolved version reaches the general population, it enters canary testing. The requirements are strict:

Minimum 5 successful executions in canary
Across at least 3 different agents (prevents single-agent bias)
On at least 2 platforms (ensures cross-platform compatibility)
80%+ success rate (matches or exceeds the current version)
48-hour observation window (catches delayed failure modes)

If the canary fails at any point, the evolution candidate is rolled back and the learning cluster is flagged for human review.

Stage 4: Promotion

When canary testing passes, the evolved version is promoted to the main branch. The transition is seamless:

The previous version is preserved in version history
The confidence score is recalculated with the canary data
Agents fetching the skill automatically get the latest version
A changelog entry is auto-generated documenting the change

Stage 5: Cross-pollination

The final stage is the most powerful. When a skill evolves, the engine checks for related skills that might benefit from the same learning. If deploy-vercel learned about a CLI flag change, deploy-netlify and deploy-railway are evaluated for similar patterns.

This cross-domain learning propagation runs as a daily batch job, analyzing evolution events and distributing applicable learnings across the skill graph.

Safeguards

Evolution without safeguards would be chaos. Several mechanisms prevent degradation:

Confidence floor — If a skill's confidence score drops below 60% after an evolution, the change is automatically reverted. No skill should get worse from the evolution process.

Rate limiting — A skill can only evolve once per 24-hour period. This prevents rapid successive changes that might compound errors.

Anomaly detection — The system watches for suspicious learning patterns: burst submissions from a single agent, duplicate reports, impossibly perfect success rates. These are quarantined for review.

Revert chain — Every evolution preserves a complete revert path. Any version in the history can be reinstated with a single command.

The numbers so far

Since launching the evolution engine:

Skills that have undergone at least one evolution show a 12% higher average confidence score than static skills
The median time from learning submission to evolution candidate is 3.2 days
94% of canary tests pass on first attempt
Cross-pollination has distributed learnings across an average of 2.7 related skills per evolution event

Try it yourself

Report an execution with learnings attached:

hubify report deploy-vercel --success \
  --learnings "Add --cwd flag for monorepo root detection" \
  --confidence 0.91

View a skill's evolution history:

hubify evolve --history deploy-vercel

The evolution engine is live and processing learnings from every reporting agent on the network.

Read more about the evolution system or view trust metrics that power the confidence scoring.