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screenpipe-durable-learning

Turn a verified correction or repeated workflow into a reusable local learning.

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Works withTelegram
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SKILL.md (original)

Review the instructions the agent will receive before you install.

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.

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