为多智能体的分层记忆统一契约,避免路由漂移、越级写入与作用域扩张。
记忆
Ber Clawhub V060
试用Better Every Run: capture explicit /ber corrections, review them, and promote only the lessons that deserve durable memory, skill rules, or evals.
它能做什么
Better Every Run: capture explicit /ber corrections, review them, and promote only the lessons that deserve durable memory, skill rules, or evals.
技能文档
Better Every Run
Use this skill only when the user explicitly invokes /ber, names Better Every Run, or directly asks to persist a lesson for future runs. Do not auto-capture ordinary corrections, casual preferences, or words like "remember", "always", "never", or "next time" unless the user clearly wants durable learning.
The human path is deliberately small:
/ber fix bad outcome -> desired outcome
/ber remember simple rule
/ber report
The agent runs the bundled local helper and reports the result in chat. fix and remember record only to the local .better-every-run/ evidence store. Durable memory/skill changes require the reviewed card + promote flow; eval regression cases require eval-fixture.
When To Use
- The user explicitly types
/ber fix ... -> .... - The user explicitly types
/ber remember .... - The user explicitly asks for a Better Every Run report.
- The user asks the agent to record a reusable correction as durable memory.
Human Commands
/ber fix agent wrote vague status -> agent gives exact command output and next action
/ber remember use the approved development host for active code work
/ber report
Rules
- Report CLI output back in chat; do not build web pages or dashboards.
- Do not silently edit
MEMORY.md,AGENTS.md,SOUL.md, or other durable instruction files. - Do not pass
--targetto/ber fixor/ber remember; direct durable writes are disabled and must refuse. - Only promote to durable memory or skill files after an explicit review decision using
cardthenpromote. - Memory promotions must target an existing
memory/*.mdfile. - Skill promotions must target
SKILL.mdin the current skill project. - Eval fixtures must use
eval-fixtureand target.jsonor.jsonlundertests/orevals/. - Scanner hard blocks and warnings both block promotion. Adjust, quarantine, or supersede the lesson instead of forcing it through.
- Keep corrections factual: bad outcome, desired outcome, and scope.
- Use scope metadata when it helps decide whether a lesson belongs only to this run, the project, the workspace, a skill, memory, or an eval.
- Avoid private data unless the user explicitly wants it captured.
- Keep the user-facing flow short, but disclose persistence: local store, durable target file if promoted, and how it was reviewed.
- Design for the shortest path to the user's outcome.
- Never publish
.better-every-run/state, local lessons, event logs, or private corrections.
Workflow
For normal chat use, keep the visible flow to one command:
/ber fix bad outcome -> desired outcome
For simple one-line preferences:
/ber remember simple rule
Use /ber report to show what was learned, including open proposals and promotion suggestions.
Agent Implementation
Keep low-level helper commands out of normal chat unless debugging, but never hide persistence. A normal answer should say whether the lesson was recorded only in .better-every-run/ or promoted to a named durable file through a lesson card.
Read references/workflow.md for the full workflow.
Read references/report-template.md for expected chat output shape.
Before publishing or packaging, verify:
.better-every-run/is excludedmake testpasses- direct
fix --target,remember --target, andapply-memory-patchattempts refuse without changing target files - lesson-card, lifecycle, and eval-fixture demos are generic and contain no private workspace paths, chat IDs, tokens, or hostnames
- examples contain no private workspace paths, chat IDs, tokens, or hostnames
- docs say activation is explicit-only and persistence is disclosed
相关技能
在本地磁盘以分类纯 Markdown 文件保存需要长期留存的事实,与智能体内置记忆并存。
Be one of the first to benchmark your agent's memory — and help shape how AI remembers. Peer-review-grade evaluation (LLM-as-judge, nDCG/MAP/MRR with 95% CIs, ablations) against your live memory system. Runs entirely LOCALLY by default — no memory content leaves your machine. The optional OpenAI judge is opt-in, prints exactly what it would send, redacts secrets first, requires typed consent, and refuses to run unattended. Submitting results is a separate confirmed step that previews every public field, and identifies you only if you pass --contributor. Built for the TinkerClaw fork — github.com/globalcaos/tinkerclaw. See Permissions, Data Flow & Consent.
Audit and maintain OpenClaw-style long-term memory. Use for MEMORY.md cleanup, daily-note digestion, duplicate detection, stale-memory review, and promoting...
Score and improve AI agent responses across six dimensions (intent, reasoning, tools, safety, efficiency, value). Use when an agent's answer was wrong, the user said "that's not right" or "actually no", output quality dropped, you're tuning an agent before deployment, or you need a structured quality score. Triggers on "evaluate agent", "agent quality", "agent review", "self-review", "why was this wrong".
Unlimited organized memory for your AI agent. Store, search, and organize projects, contacts, decisions, and knowledge across categories. Never lose context...