Recommend
Get research-backed architecture advice for your knowledge system.
Install
npx skills add agenticnotetaking/arscontexta --skill recommend -g -a claude-code -y- Repository
- agenticnotetaking/arscontexta3.5k
- Category
- Writing
- Updated
- 8mo ago
- License
- MIT
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- No, instructions only
What it does
Get research-backed architecture advice for your knowledge system. Describe your use case, constraints, and goals, get specific recommendations grounded in TFT research with rationale for each decision. Triggers on "/recommend", "what would you recommend", "architecture advice", "knowledge system for".
Runtime Configuration (Step 0, before any processing)
Read these files to configure recommendation behavior:
-
${CLAUDE_PLUGIN_ROOT}/reference/tradition-presets.md, tradition and use-case presets- Pre-validated coherence points in the 8-dimension space
- Starting points for customization, not final answers
-
${CLAUDE_PLUGIN_ROOT}/reference/methodology.md, universal methodology principles -
${CLAUDE_PLUGIN_ROOT}/reference/components.md, component blueprints (what can be toggled) -
${CLAUDE_PLUGIN_ROOT}/reference/dimension-claim-map.md, maps each dimension position to supporting research claims -
${CLAUDE_PLUGIN_ROOT}/reference/interaction-constraints.md, hard blocks, soft warns, cascade effects between dimensions -
${CLAUDE_PLUGIN_ROOT}/reference/claim-map.md, topic navigation for the research graph
If any reference file is missing, note the gap but continue with available information. The recommendation degrades gracefully, fewer citations, same structure.
EXECUTE NOW
Target: $ARGUMENTS
Parse immediately:
- If target is empty or a question: enter conversational mode, ask 1-2 clarifying questions, then recommend
- If target contains a use case description: proceed directly to recommendation mode
- If target contains
--compare [A] [B]: enter comparison mode, compare two presets or configurations
START NOW. Reference below defines the workflow.
Philosophy
Advisory, not generative.
/recommend exists for exploration. The user is considering a knowledge system, maybe they have a use case, maybe they're comparing approaches, maybe they're curious what the research says about a specific pattern. /recommend answers with specific, research-backed reasoning without creating any files.
