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This skill should be used for project-level decisions about LLM-powered systems: whether an LLM is the right primitive for the task at hand, the shape of a multi-stage batch or agent pipeline, token and cost estimation, choosing between single-agent and multi-agent at the project level, structured output design for downstream parsing, and structuring agent-assisted iteration. Use this when the unit of work is a whole project or a multi-stage pipeline. Route individual tool design to tool-design
Passed 10 of 22 test cases with the skill, and 0 of 22 without it.
10 fixed0 passed either way12 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
The instructions your model reads when the skill loads.
This skill covers the principles for identifying tasks suited to LLM processing, designing effective project architectures, and iterating rapidly using agent-assisted development. The methodology applies whether building a batch processing pipeline, a multi-agent research system, or an interactive agent application.
The unit of work for this skill is the whole project or a multi-stage pipeline. Individual tool design (descriptions, schemas, error messages) belongs to tool-design. Per-skill activation routing belongs to the corresponding skill plus the corpus index. This skill owns the project-level questions: should you build this with an LLM at all, what shape should the pipeline take, what does it cost, how should it be iterated.
Activate this skill when the unit of work is a whole project or pipeline:
Do not activate this skill for adjacent work owned by other skills:
tool-design.context-optimization.multi-agent-patterns.harness-engineering.Evaluate task-model fit before writing any code, because building automation on a fundamentally mismatched task wastes days of effort. Run every proposed task through these two tables to decide proceed-or-stop.
Proceed when the task has these characteristics:
| Characteristic | Rationale |
|---|---|
| Synthesis across sources | LLMs combine information from multiple inputs better than rule-based alternatives |
| Subjective judgment with rubrics | Grading, evaluation, and classification with criteria map naturally to language reasoning |
| Natural language output | When the goal is human-readable text, LLMs deliver it natively |
| Error tolerance | Individual failures do not break the overall system, so LLM non-determinism is acceptable |
| Batch processing | No conversational state required between items, which keeps context clean |
| Domain knowledge in training | The model already has relevant context, reducing prompt engineering overhead |
test cases passed, vs 0 / 22 without
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