The core idea
A prompt doesn't find the right answer hidden in the model. Each section changes what the model will say — and order, combination, and strength are the real design decisions.
Concretely: the role you assign projects the model into one way of working; constraints narrow it; examples teach patterns no rule states; the output schema fixes the form. The framework calls each section an operator and gives it a measurable profile — what it amplifies, what it suppresses, how strongly, and where in the order it belongs. Every operator is drawn from documented prompt- and context-engineering practice, not invented rules.
How it works
What happens when you use it.
Two modes: build a prompt from scratch, or hand it one you already have.
You ask
“Help me design a prompt for [task] — use the prompt-architect skill.” Works in any tool-calling runtime.
It interviews
A few targeted questions. The structure is inferred from your request — you're never quizzed about things you already said.
It drafts & audits
Every section written and placed deliberately, then checked — structure, section interactions, honest scoring.
You receive
The finished prompt, paired artifacts when the shape needs them, usage notes for your runtime, and a runnable verifier.
From scratch
You describe a task. The framework infers the shape, runs a brief operator-aware interview, drafts, audits, scores itself honestly, and delivers.
- Shape inference — the structure is read from your request, not asked
- Adaptive interview — capped, co-authoring on hard decisions
- Drafting — every section placed deliberately
- Audit — structure and section-interaction checks
- Honest self-evaluation — three axes, named gaps
- Delivery — prompt, verifier, usage notes
Refactor
You paste a prompt you already have. The framework recovers its intent, then does exactly the level of work you ask for — never a silent rewrite.
- AUDIT — diagnostic report: findings, severity, fixes. No edits.
- TARGETED — surgical edits for a named symptom, via a symptom-to-cause table.
- REWRITE — full redraft with explicit diff and tradeoff analysis; the original is preserved.
Anatomy of a generated prompt
A finished prompt is a stack of sections, each doing one job. Inspect two real ones.
Pick a situation. The framework assembles a different set of sections for each shape — and treats every section as an operator: something that changes what the model will do, with a strength, a profile, and a correct position in the order. Select a section to inspect it.
Seven shapes
Every prompt has a structural archetype. The framework infers it, then builds to its spine.
Shape is orthogonal to domain. The same seven shapes apply whether the work is engineering, research, writing, or ops — domain changes the content of sections, not which sections appear. The framework reads your task and infers the shape, so the interview stays short.
Long-horizon work
Multi-checkpoint projects with a memory file and human-in-the-loop handoffs.
Inspect a real one ↗Single complex call
Classification, extraction, transformation, generation — done in one pass. Cognitive tools attach here when the task is genuinely hard-reasoning.
Inspect a real one ↗Staged pipeline
Coordinated calls with gates between stages and aligned inter-stage schemas.
Consumed by an agent
Output designed for another model to parse, not a human to read.
Stable assistant
Identity that holds across unknowable inputs, with layered capability lockdown.
Graders & scorers
Calibrated rubrics with bias controls and reasoning forced before the score.
Multi-agent systems
Four coordination topologies — orchestrator-workers, parallelization (sectioning & voting), evaluator-optimizer — composed from the six single-agent shapes above, with verified interface contracts between roles.
What it applies — and where it's from
Every technique traces to published work.
The framework absorbs a documented technique only when it carries measurable consequences — vocabulary without measurable effect is what its audit rejects. Here is the core catalog, each practice paired with the work it comes from. The complete annotated set, including what was evaluated and rejected and why, is in CITATIONS.md.
-
Context engineeringBudget every token; load critical context early and persist it across turns.SourceEffective context engineering for AI agents
-
Few-shot · abstention · prefillingCurated examples; explicit permission to say “I don't know”; prefilling to lock the output's form.SourceBest practices for prompt engineering
-
Cognitive toolsModular reasoning steps shipped as real tool definitions, not prose.SourceEliciting Reasoning in Language Models with Cognitive Toolscentral
-
Multi-agent topologiesOrchestrator–workers, parallelization (sectioning & voting), evaluator–optimizer.SourceBuilding Effective AI Agents
-
Parallel agent teamsCoordinating a team of parallel agents against one codebase.SourceBuilding a C compiler with a team of parallel Claudes
-
Memory files & lockdownA CLAUDE.md that outlives the context window; layered capability lockdown; “never delegate understanding.”SourceClaude Code internal architecture
-
The operator lensThe organizing metaphor: sections as operators on the model's semantic state.SourceA quantum semantic framework for natural language processing
-
Mechanistic basisWhy operators compose the way they do — the symbolic mechanisms behind abstract reasoning.SourceEmergent Symbolic Mechanisms Support Abstract Reasoning in LLMs