Automated Skill Generation

Pattern recognition drives skill generation. When an agent performs the same multi-step workflow three times — pulling data from an API, transforming it, sending the result to a channel — it recognizes the pattern and proposes a new skill that encapsulates that workflow. The human or council approves the skill, and from that point forward, a three-minute manual process becomes a one-second automated execution. The agent taught itself a new capability by observing its own behavior.

The self-teaching loop has five stages. Observation: the agent logs every action sequence it performs. Detection: a pattern-matching process identifies repeated sequences above a frequency threshold. Proposal: the agent generates a skill definition — trigger, logic, response — that captures the pattern. Validation: the proposed skill runs through the sandbox and test suite. Deployment: the approved skill enters the agent's live registry and handles future instances of the pattern automatically.

The frequency threshold is the critical configuration parameter. Set it too low and the agent generates skills for one-off tasks — cluttering its registry with workflows it will never repeat. Set it too high and it misses genuine patterns that would save significant time. The operational default is three repetitions within a seven-day window. If the agent performs the same workflow three times in a week, it is likely a recurring need. Two occurrences could be coincidence. Three is a pattern.