Skill: antoniovfranco/algernon-synthesis (version 1.0.0)

--- name: algernon-synthesis description: > Cross-material knowledge synthesis session for OpenAlgernon. Use when the user runs `/algernon synthesis`, says "quero conectar os materiais", "sintese entre materiais", "como X se relaciona com Y", "visao geral do curriculo", "integrar o conhecimento", or "ver o quadro geral". Requires at least 2 materials with reviewed cards. Surfaces conceptual bridges across materials and ends with a production scenario challenge. --- # algernon-synthesis You run a cross-material synthesis session. The goal is to build explicit connections between concepts learned in different materials — the kind of holistic understanding that separates someone who memorized facts from someone who can actually design systems. ## Constants ``` DB=/home/antonio/Documents/huyawo/estudos/vestibular/data/vestibular.db NOTION_CLI=~/go/bin/notion-cli ``` ## Step 1 — Check Eligibility ```bash sqlite3 $DB \ "SELECT m.slug, m.name, COUNT(r.id) as review_count FROM materials m JOIN decks d ON d.material_id = m.id JOIN cards c ON c.deck_id = d.id JOIN reviews r ON r.card_id = c.id GROUP BY m.id HAVING review_count > 0 ORDER BY review_count DESC;" ``` If fewer than 2 materials have reviews: "Synthesis requires at least 2 studied materials. Study more material first." ## Step 2 — Identify Cross-Material Concept Overlaps From the tags and topics of reviewed cards across all studied materials, identify 3-5 concept pairs that appear in multiple materials but may be understood differently in each context. Examples of strong synthesis pairs: - "evaluation" in RAG vs LLMOps contexts - "chunking" in embedding vs RAG contexts - "latency" in inference vs retrieval contexts - "context" in prompt engineering vs agent memory contexts - "retrieval" in BM25 vs vector similarity vs caching contexts Prefer pairs where the same word genuinely means something different in each context — that contrast is the richest learning opportunity. ## Step 3 — Synthesis Questions For each concept pair, ask: AskUserQuestion (free text): > "[CONCEPT] appears in both [MATERIAL_A] and [MATERIAL_B]. How does the meaning > or role of [CONCEPT] differ between these two contexts? Where do they overlap?" After each answer, give brief feedback: - Name what the user connected well. - Name any distinction they missed (without lecturing — one sentence). ## Step 4 — Production Scenario Challenge AskUserQuestion (free text): > "If you were building a production AI system, how would the knowledge from > [MATERIAL_A] and [MATERIAL_B] work together? Give a concrete scenario with > specific design decisions." Evaluate for: 1. Coherence — does the scenario make technical sense? 2. Specificity — are there real design decisions, not just buzzwords? 3. Correct use of concepts — are terms from both materials used accurately? ## Step 5 — Summary Display: ``` Synthesis session complete. Materials covered: [list] Conceptual bridges built well: [list] Bridges that need reinforcement: [list] ``` ### Send to Notion Send to the Notion page of the most recent phase studied: ```bash ~/go/bin/notion-cli append --page-id PHASE_PAGE_ID --content "MARKDOWN" ``` Include: - Cross-material concepts explored - Gaps identified (bridges that need reinforcement) - The production scenario the user described ### Save Memory Append to today's conversation log: ``` [HH:MM] synthesis session Materials: [list] | Bridges built: N | Needs reinforcement: [list] ```


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