smartart-selector

name	smartart-selector
description	Orchestrates the SmartArt type selection negotiation. Dispatches the smartart-selector agent to recommend graphic types and enrichment tiers, then runs approval/rejection loop with the narrative-architect. Writes smartart-recommendations.json.
allowed-tools	Bash(python *), Read, Glob, Agent

/smartart-selector

Orchestrate the SmartArt graphic type and enrichment tier selection for all slides in a deck. You dispatch the smartart-selector agent to propose recommendations, then evaluate them as the narrative-architect (considering narrative arc, audience, and adjacency) to approve or reject.

Prerequisites

Before running, these DeckContext files must exist:

• ./tmp/deck/outline.json — SlideOutline with optional visual_intent fields
• ./tmp/deck/style-guide.json — StyleGuide for design token feasibility
• ./tmp/deck/talk-brief.json — TalkBrief for audience and tone context
• ./tmp/deck/pipeline-state.json — PipelineState for budget state

Plugin Setup

PLUGIN_ROOT=$(python3 -c "
from pathlib import Path
import sys, os
if os.environ.get('JACK_TAR_DECKHAND_ROOT'):
    print(os.environ['JACK_TAR_DECKHAND_ROOT']); sys.exit()
home = Path.home()
for base in [home / '.claude' / 'plugins' / 'cache']:
    for p in base.rglob('jack-tar-deckhand/.claude-plugin/plugin.json'):
        print(str(p.parent.parent)); sys.exit()
dev = Path.cwd() / 'plugins' / 'jack-tar-deckhand'
if dev.exists():
    print(str(dev)); sys.exit()
print('NOT_FOUND')
" 2>/dev/null)
if [ -z "$PLUGIN_ROOT" ] || [ "$PLUGIN_ROOT" = "NOT_FOUND" ]; then echo "ERROR: jack-tar-deckhand not found" && exit 1; fi

Step 1: Read DeckContext

PYTHONPATH="$PLUGIN_ROOT" python3 -c "
import json
with open('./tmp/deck/outline.json') as f:
    outline = json.load(f)
with open('./tmp/deck/style-guide.json') as f:
    style_guide = json.load(f)
with open('./tmp/deck/talk-brief.json') as f:
    talk_brief = json.load(f)
print(f'Slides: {len(outline[\"slides\"])}')
candidates = [s for s in outline['slides'] if s.get('visual_intent')]
print(f'SmartArt candidates (explicit visual_intent): {len(candidates)}')
"

Step 2: Dispatch SmartArt Selector Agent

Use the Agent tool to dispatch the smartart-selector agent. Provide:

• The full SlideOutline (all slides, for adjacency context)
• StyleGuide palette and typography
• TalkBrief audience and tone
• Current budget state from pipeline-state.json

The agent returns JSON with 2-3 ranked recommendations per candidate slide.

Step 3: Evaluate Recommendations (as Narrative Architect)

For each candidate slide, evaluate the agent's recommendations against:

1. Narrative arc — does this graphic type serve the story at this point?
2. Audience context — is the enrichment level appropriate for this audience?
3. Adjacent slides — would this create visual repetition with neighbouring slides?
4. Content fit — does the data in body_points actually suit this graphic type?

Approve the best recommendation or reject all with specific feedback.

Step 4: Handle Rejections (max 2 rounds)

If rejecting, provide clear feedback:

• "The data is comparative, not sequential — suggest a matrix or radar instead"
• "Too many flowcharts in sequence — try a different visual for variety"

Re-dispatch the agent with the feedback. After 2 rejection rounds, fall back to composed strategy for that slide.

Step 5: Write SmartArtRecommendations

PYTHONPATH="$PLUGIN_ROOT" python3 -c "
import json
from src.deckcontext import write_contract, validate_contract
recommendations = {
    'slides': [...]  # approved recommendations
}
validate_contract('smartart-recommendations', recommendations)
write_contract('./tmp/deck', 'smartart-recommendations', recommendations)
print('SmartArtRecommendations written')
"

Output

• File: ./tmp/deck/smartart-recommendations.json
• Schema: src/schemas/smartart_recommendations.schema.json
• Each slide entry has approval_status: "approved" and selected_index pointing to the chosen recommendation