Skills algernon-synthesis

install
source · Clone the upstream repo
git clone https://github.com/openclaw/skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/antoniovfranco/algernon-synthesis" ~/.claude/skills/openclaw-skills-algernon-synthesis && rm -rf "$T"
OpenClaw · Install into ~/.openclaw/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/antoniovfranco/algernon-synthesis" ~/.openclaw/skills/openclaw-skills-algernon-synthesis && rm -rf "$T"
manifest: skills/antoniovfranco/algernon-synthesis/SKILL.md
source content

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

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:

~/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]