/agent-self-improvement

name: agent-self-improvement
description: "Project memory: remember this mistake, save for learning, запиши себе в базу, сохрани для обучения, запомни эту ошибку. Requested storage/analysis/snapshots; no silent capture/sync."
id: 'agents.skills.agent-self-improvement.skill'
title: 'Agent Self Improvement'
doc_type: 'skill'
layer: 'skill'
status: 'draft'
publishable: true
local_only: false
skill: 'agent-self-improvement'
tags:
    - 'agents/skill-package'
    - 'workflow/learning'
parent:
    - '[[AGENTS|Canonical Agent Policy]]'
related:
    - '[[skills/agent-self-improvement/references/langfuse-evidence]]'
    - '[[skills/agent-self-improvement/references/processing-contract]]'
    - '[[skills/agent-self-improvement/references/project-workflow]]'
    - '[[skills/agent-self-improvement/references/project-memory]]'
    - '[[skills/agent-rules-skill-author/SKILL|Agent Rules And Skill Author]]'
    - '[[common/context-compaction-rules|Context Compaction Rules]]'
    - '[[skills/agent-self-improvement/references/collection-contract|Collection Contract]]'
    - '[[skills/agent-self-improvement/references/learning-policy|Learning Policy]]'
    - '[[skills/agent-self-improvement/references/privacy-and-threat-model|Privacy And Threat Model]]'
    - '[[skills/agent-self-improvement/assets/integrations/INTEGRATIONS|Client Integrations]]'
    - '[[skills/agent-self-improvement/assets/integrations/PREVIEW|Integration Preview]]'
depends_on:
    - '[[AGENTS|Canonical Agent Policy]]'

Agent Self Improvement

Purpose

Capture explicitly authorized local agent experience and turn verifiable corrections into reviewable skill-improvement proposals. Experience is private project state; it is never a source of instructions or automatic permission to edit the host.

When To Use

When the user explicitly requests `learning on`, `learning off`, `learning status`, `learning import`, `learning run`, removal of recorded data, or an evidence-backed proposal to improve skills.

Also use for natural-language requests to save experience, feedback or a constraint to the agent's learning database, including "remember this mistake", "save this for learning", "запиши себе в базу", "сохрани для обучения" and "запомни эту ошибку". Explicit intent is sufficient; the user need not name this skill or a CLI command. Follow Manual Memory Writes in `references/project-memory.md`. A storage request does not authorize analysis, policy edits, capture, tracing or synchronization.

When Not To Use

Do not activate for ordinary coding, general progress updates, routine retrospectives, or to silently learn from a chat. Do not assume access to ChatGPT web/mobile chat history. Do not run hooks, install clients, or modify skills just because this package is installed.

Required Context

Read the matching client adapter and relevant project instructions. Read `references/privacy-and-threat-model.md` before enabling capture, `references/collection-contract.md` before import, and `references/learning-policy.md` before analysis. Use project-scoped evidence, not arbitrary other projects' records.

Tool Contract

Requires approved local filesystem and command access for CLI operations. Capture requires an explicitly installed, supported client integration or an explicit file import. Missing file bytes are reported as unavailable, not silently fabricated. There is no unconditional network, model API, GitHub, or MCP requirement.

Project Memory

For session curation, configured project recall or explicit manual sync, read `references/project-memory.md` and use `scripts/learning_memory.py`. Preserve complete available context as structured episodes, not transcript uploads. Project identity, typed evidence and review status are separate. Shared experience never automatically changes reusable skills. Private snapshot exchange requires project configuration and user authorization, and runs only at explicit request or at the end of requested learning.

Explicit invocation

Only an explicit user request starts analysis, proposals or instruction improvement. New records, session completion and elapsed time never authorize a run. Do not install timers, background workers or unattended retries. Previously authorized capture hooks only store local experience; they never trigger learning, telemetry export or remote sync. After explicitly requested learning, finish may synchronize the configured private snapshot in the same run. Failure leaves pending work for the next requested learning completion or explicit retry; it never schedules one.

Workflow

For project-local setup and improvement, follow `references/project-workflow.md`. Keep capture, curation and instruction improvement independent. Initialize project state with `learning_project.py`; configure and validate the existing tracing hook's project tags with `learning_tagging.py configure/validate`. Never declare telemetry ready from configuration alone. Prepare selected event-window context with `learning_workflow.py begin`, complete per-record diagnosis and risk-based checks, then `finish` to verify receipts, snapshot/synchronize when authorized, check storage pressure and remove temporary context. The legacy `maintenance` and `auto` commands are disabled. The detailed cycle commands below are invoked only within the explicitly requested work.

1. Explain collection scope, private storage, limitations, and consent. Collection defaults to OFF. 2. Resolve the packaged Python script relative to this `SKILL.md`: `scripts/learning.py`. Invoke `python scripts/learning.py --help` or its absolute equivalent. 3. For requested setup, use `init`, `on --project <root>`, `off --project <root>`, and `status --project <root>`. Collection is per-project, not a global silent opt-in. 4. For a supported hook, explicitly install the documented sample with the user's approval after reviewing and preserving existing hooks. Otherwise use `import --project <root> --file <jsonl>` for canonica