durable learning

Verify the original request, correction, and result. Treat assistant success claims as claims until checked. Search existing skills before proposing a new one. Save a concise reusable method, not a transcript, private identity, credential, or temporary task state. Include when to use it, steps, validation, and a stop condition. Use skill_manage when available. Read first, preserve user/imported skills, and use the current hash for an agent-owned patch. Creating a skill requires the user’s explicit request or an enabled task whose stated scope authorizes it. No new permissions, outbound actions, or recursive agents. A no-change result is valid. Assess the next relevant use before claiming improvement.

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Like this skill? It is part of second-brain-starter-kit: the second brain we built for ourselves and run every day at Palo Alto AI Research Lab. Install the whole set with npx skills add tonydzi/second-brain-starter-kit. Everything is open source and free, so take what you need.

Flagships worth a look on their own: secondop-panel (a second opinion from a panel of external models), claude-memory-tidy (stop your agent's memory from rotting), telegram-mcp-kit (your own Telegram over MCP in about 15 minutes).

Author: Anton Dziatkovskii, Palo Alto AI Research Lab. Telegram @tonydzi - WhatsApp +1 341 222 9178 - X @Tony_Stef_

Engineers: want to test-drive this setup? Message me. I hand out free starter seeds to engineers who test and report back, and custom skill requests are welcome.