This skill should be used when users ask about which AI model to use for coding, mentions 'cost', 'batch', 'API', 'configure analysis', wants to process multiple documents, or needs to understand model capabilities and costs for Stage 2.
Guides AI model selection and cost estimation for batch coding analysis of multiple documents.
npx claudepluginhub linxule/interpretive-orchestrationThis skill inherits all available tools. When active, it can use any tool Claude has access to.
scripts/estimate-costs.jsModel selection, cost estimation, and batch processing setup for AI-assisted coding. Helps researchers configure their analysis approach with awareness of tradeoffs.
Use this skill when:
| Model | Best For | Cost | Quality |
|---|---|---|---|
| Claude Opus 4.5 | Complex interpretive coding, nuanced themes | $$$ | Highest |
| Claude Sonnet 4 | Balanced quality and cost for systematic coding | $$ | High |
| Claude Haiku | Initial passes, high volume, simple categorization | $ | Good |
Deep Interpretive Coding (@dialogical-coder)
Initial Categorization
Pattern Characterization
| Documents | Model | Estimated Cost |
|---|---|---|
| 10 interviews (~50 pages) | Opus | $15-25 |
| 10 interviews (~50 pages) | Sonnet | $5-10 |
| 50 documents | Opus | $75-125 |
| 50 documents | Sonnet | $25-50 |
Variables:
Estimates API costs based on document characteristics.
node skills/analysis-orchestration/scripts/estimate-costs.js \
--documents 25 \
--avg-pages 5 \
--model sonnet \
--passes 2
Returns: Estimated cost range and token counts.
Is this initial exploratory coding?
├── Yes → Consider Haiku for volume, validate with Sonnet
└── No, this is interpretive coding
├── Budget constrained?
│ ├── Yes → Sonnet 4 (good balance)
│ └── No → Opus 4.5 (best quality)
└── Need to process >50 documents?
├── Yes → Two-pass: Haiku then Sonnet on subset
└── No → Single-pass with Sonnet or Opus
Using @dialogical-coder (4-stage process)?
├── Yes → 5-10 documents per session (reflection breaks)
└── No, systematic application?
├── Complex coding scheme → 10-15 documents
└── Simple categorization → 20-30 documents
/qual-configure-analysis triggers this skillcoding-workflow/ for batch processing executionExpert guidance for Next.js Cache Components and Partial Prerendering (PPR). **PROACTIVE ACTIVATION**: Use this skill automatically when working in Next.js projects that have `cacheComponents: true` in their next.config.ts/next.config.js. When this config is detected, proactively apply Cache Components patterns and best practices to all React Server Component implementations. **DETECTION**: At the start of a session in a Next.js project, check for `cacheComponents: true` in next.config. If enabled, this skill's patterns should guide all component authoring, data fetching, and caching decisions. **USE CASES**: Implementing 'use cache' directive, configuring cache lifetimes with cacheLife(), tagging cached data with cacheTag(), invalidating caches with updateTag()/revalidateTag(), optimizing static vs dynamic content boundaries, debugging cache issues, and reviewing Cache Component implementations.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.