continuous-learning-v2
Review repeated user corrections and successful workflows, then propose small evidence-backed reusable instincts. Use only when the user explicitly asks to.
SKILL.mdskills/common/continuous-learning-v2/SKILL.md
GitHubConservative Continuous Learning
Convert repeated, verified patterns into reviewable proposals without silently changing the assistant's behavior.
Non-negotiable boundaries
- Advisory by default. Do not start background observation, install hooks, read unrelated conversations, or scan email/calendar/chat history.
- Do not create or update skills, agents, commands, plugins, hooks, memory stores, or configuration without a separate explicit approval for the exact diff and path.
- Do not select or switch models/providers. If an optional reviewer is available, use the active host's current capabilities; independence matters more than a model name.
- Store no secrets, raw private conversations, credentials, or sensitive code in learned artifacts.
- A confidence score ranks evidence; it never grants write permission.
Evidence threshold
Propose an instinct only when either:
- the same pattern appears in at least five distinct observations and no recent correction contradicts it; or
- one deterministic failure and its verified fix establish a narrow technical invariant.
Every proposal records the trigger, action, scope, evidence summaries, counter-evidence, confidence, expiry/review date, and a way to test or revoke it.
id: prefer-existing-project-pattern
trigger: when adding a new module to this repository
action: follow the nearest existing module structure before introducing a new abstraction
scope: project
confidence: 0.7
evidence:
- five reviewed changes followed the same structure
counter_evidence: []
review_after: 2026-10-01
status: proposed
Workflow
- Confirm the user requested learning or pattern review.
- Define the observation scope and exclude sensitive sources.
- Normalize observations into short claims; keep pointers to local evidence rather than copying raw content.
- Cluster only semantically equivalent claims. Keep exceptions and contradictions visible.
- Apply the evidence threshold and produce proposals, not writes.
- Show the exact proposed artifact/diff, target path, benefit, regression risk, validation, and rollback.
- Write only after approval. Re-read the destination immediately before editing and preserve unrelated changes.
- Validate the learned behavior against positive, negative, and boundary examples. Roll back if it broadens beyond its evidence.
Evolution into a skill
Evolve an instinct only when a repeatable workflow needs multiple steps, references, or tooling. Before replacing an existing skill, compare behavior and trigger coverage, run preservation tests, and keep a timestamped backup. If the proposal is not measurably better, leave the existing skill unchanged.
Output
Report observations reviewed, proposals accepted/rejected, contradictions, files changed, validation results, and rollback location. If no pattern meets the threshold, say so and make no change.