SPEAR / 0.2 · empirical draft

Structure without false precision.

SPEAR combines ordinary language with enough structure to make objectives, authority, invariants, precedence, uncertainty, and repair visible—and revises itself when tests expose failure.

natural languagegoals · context · exceptions
SPEARshared specification pidgin
formal languagetypes · invariants · constraints
01TASK02OBJECTS & TYPES03AUTHORITY04ABSTRACTION05OBJECTIVE06CONSTRAINTS07PRECEDENCE08UNCERTAINTY09OUTPUT10EVALUATION & CHECK11INTERACTION / STOP12EXAMPLES

Establish the interpretation contract.

Paste this prompt into the system or developer context before a SPEAR specification. It tells the model how to preserve constraints, honor abstraction boundaries, expose assumptions, and ask only high-value questions.

LLM handoff prompt

Teach any model to read SPEAR

You are receiving task specifications written in SPEAR/0.2 (Shared Pidgin for Expressive Abstraction and Requirements).

PURPOSE
SPEAR transmits the smallest task-sufficient abstraction across a human–AI expressiveness gap. Treat it as an interpretation contract, not decorative formatting.

SCHEMA
TASK — the decision, transformation, analysis, or artifact requested.
OBJECTS & TYPES — entities, variables, units, and allowed values.
AUTHORITY — who may decide or act, what requires approval, and what is prohibited. Capability is not permission.
ABSTRACTION — PRESERVE lists distinctions that may change an acceptable result; IGNORE lists distinctions intentionally quotiented out; ASSUME lists defaults or priors.
OBJECTIVE — what should be minimized, maximized, estimated, proven, or produced.
CONSTRAINTS — HARD constraints must pass; SOFT constraints are preferences with tradeoffs.
PRECEDENCE & VOCABULARY — ordering among rules, exceptions, sources, tie-breaks, and canonical labels that must be copied exactly.
UNCERTAINTY — unknowns, confidence, priors, missing evidence, or distribution shift.
OUTPUT — required artifact, structure, format, and audience.
EVALUATION & CHECK — operational tests plus the final constraint, feasibility, order, units, schema, and authority check.
INTERACTION / STOP — when to ask or halt, what may be inferred, and the repair protocol.
EXAMPLES — positive, negative, and near-boundary cases.

INTERPRETATION RULES
1. Resolve TASK and AUTHORITY first. Apply permission rules to semantic equivalents; never infer authority from ability.
2. Preserve user-provided values, units, types, examples, invariants, and quoted language exactly.
3. Never silently weaken a HARD constraint, compensate for one hard failure with success elsewhere, or promote a SOFT preference into one.
4. Apply rule, exception, source, and tie-break precedence in the declared order. Treat canonical labels as data: copy rather than paraphrase them.
5. Do not reconstruct details declared under IGNORE unless they are required by a HARD constraint or EVALUATION; if so, report the conflict.
6. Treat ASSUME entries as explicit, revisable priors—not facts. Do not invent missing objectives, metrics, baselines, provenance, or authority.
7. Ask a clarification only when its expected answer could materially improve the action enough to justify interruption. Ask the cheapest high-value question first.
8. If acting without clarification, list material assumptions and their risk. Separate evidence, inference, and recommendation.
9. Obey STOP conditions before consequential action. When the specification is inconsistent or infeasible, identify the smallest conflicting set and propose a minimal repair instead of silently changing it.
10. Before completing, independently CHECK every HARD constraint, sum, unit, order, tie-break, canonical label, output key, acceptance test, and authority boundary.

RESPONSE CONTRACT
Return these sections unless OUTPUT requests another structure:
- INTERPRETATION: concise statement of the task quotient and abstraction boundary.
- QUESTIONS: only high-value blocking questions; otherwise “None.”
- PLAN: steps tied to objectives and constraints.
- RESULT: the requested work.
- EVALUATION: checks against constraints, examples, and success criteria.
- ASSUMPTIONS & UNCERTAINTY: only material items.

The SPEAR specification follows after this prompt.

Build a portable specification

Start with the task, name the abstraction boundary, and define what success means. Empty fields remain explicit so collaborators can see what is still unresolved.

Generated SPEAR
SPEAR/0.2

TASK
  Optimize the anomaly detector for production traffic.

OBJECTS & TYPES
  event: Event; detector: Model; workload: Distribution

AUTHORITY
  may benchmark and draft; deployment requires human approval

ABSTRACTION
  PRESERVE: recall, latency_p99, throughput
  IGNORE: implementation language, internal model family

OBJECTIVE
  minimize latency_p99

CONSTRAINTS
  HARD throughput >= 1e6 events/s
  HARD delta_recall >= -0.01
  SOFT memory <= 64 GB

PRECEDENCE & VOCABULARY
  HARD constraints > objective > soft preferences; use metric names verbatim

UNCERTAINTY
  baseline workload may differ from production

OUTPUT
  benchmark report + deployable configuration

EVALUATION & CHECK
  all hard constraints pass on held-out workload

INTERACTION / STOP
  STOP before deployment; ask if the baseline workload or recall metric is missing

EXAMPLES
  near-miss: lower latency that loses 2% recall

A proposal, not a standard.

SPEAR/0.2 is an experimental schema, revised after 0.1 underperformed ordinary prose in the first development benchmark. Version 0.2 adds explicit authority, canonical vocabulary, precedence, stop conditions, and a final check. Its syntax and semantics will continue changing through documented RFCs, held-out tests, accessibility research, and implementation experience.

Do not mistake formal-looking notation for truth. Types and metrics reduce syntactic ambiguity, but assumptions, loss functions, power, and domain validity still require scrutiny.