prompt-engineering

Prompt Engineering Skill

Version: 1.0.0 Framework: Jerry Framework v0.9.0 Constitutional Compliance: Jerry Constitution v1.0 SSOT Reference: .context/rules/prompt-quality.md, .context/rules/prompt-templates.md

---

Document Sections

Section	Purpose
Overview	What this skill does and why
When to Use This Skill	Triggers and use cases
Available Agents	Agent registry with routing guide
Invoking an Agent	Three invocation options (natural language, explicit, Task tool)
Quick Reference	Copy-paste examples for common tasks
Routing Disambiguation	When this skill is the wrong choice
Constitutional Compliance	Principle mapping with consequences
Architecture Notes	Design rationale and references

---

Document Audience (Triple-Lens)

This SKILL.md serves multiple audiences:

Level	Audience	Sections to Focus On
L0 (ELI5)	Users new to prompt engineering	Overview, When to Use, Quick Reference
L1 (Engineer)	Developers building prompts and constraints	Available Agents, Quick Reference, Architecture Notes
L2 (Architect)	Framework maintainers and skill designers	Routing Disambiguation, Constitutional Compliance, Architecture Notes

---

Overview

The Prompt Engineering skill operationalizes PROJ-014 negative prompting research findings into a reusable tool for constructing high-quality structured prompts within the Jerry Framework. PROJ-014 validated that NPT-013 structured negation (NEVER + consequence + alternative) achieves 100% compliance vs 92.2% for positive-only framing (p=0.016, CONDITIONAL GO via PG-003).

Core Capabilities

• Interactive Prompt Builder: Walks users through the 5-element prompt anatomy (routing, scope, data source, quality gate, output path) producing XML-wrapped structured prompts.
• NPT Constraint Generator: Converts intent descriptions into NPT-009/NPT-013 formatted constraints with <forbidden_actions> and <constraint> XML wrapping.
• Prompt Quality Scorer: Evaluates prompts against the 7-criterion rubric (C1 Task Specificity through C7 Positive Framing) and returns dimension scores with improvement suggestions.

NPT Format Reference

Format	Structure	Use Case
NPT-009	{PRINCIPLE} VIOLATION: NEVER {action} -- Consequence: {impact}	Agent forbidden actions, constitutional guardrails
NPT-013	NEVER {action} -- Consequence: {impact}. Instead: {alternative}	Behavioral constraints, routing rules, methodology guardrails

---

When to Use This Skill

Invoke /prompt-engineering when you need to:

• Build a structured Jerry prompt from scratch using the 5-element anatomy.
• Generate NPT-009 or NPT-013 formatted constraints for agent definitions, rule files, or skill documentation.
• Score an existing prompt against the 7-criterion rubric to identify quality gaps.
• Convert positive-only instructions to structured negation format per PROJ-014 findings.
• Produce XML-wrapped constraint blocks (<forbidden_actions>, <constraint>) for agent governance YAML.

NEVER invoke this skill when:

• Task requires adversarial quality review of a deliverable -- Consequence: prompt engineering generates constraints and scores prompts, not deliverables; quality assessment of artifacts requires /adversary with S-014 rubric scoring
• Task requires research, analysis, or root cause investigation -- Consequence: prompt engineering is a construction tool, not an analytical methodology; no research capability, no causal investigation; use /problem-solving instead
• Task is executing an existing prompt template from .context/rules/prompt-templates.md -- Consequence: template execution does not require prompt construction; the 5 templates are self-contained and ready to use with placeholder substitution
• Task is modifying agent definition YAML frontmatter or governance files -- Consequence: agent definition structure follows agent-development-standards.md schema, not prompt anatomy; use direct file editing with schema validation per H-34

See Routing Disambiguation for full exclusion conditions with consequences.

---

Available Agents

Agent	File	Model	Cognitive Mode	Purpose
pe-builder	skills/prompt-engineering/agents/pe-builder.md	opus	integrative	Interactive prompt assembly -- walks user through 5 elements, generates XML-wrapped structured prompt
pe-constraint-gen	skills/prompt-engineering/agents/pe-constraint-gen.md	sonnet	systematic	NPT pattern selector and constraint formatter -- takes intent, outputs NPT-009/NPT-013 XML blocks
pe-scorer	skills/prompt-engineering/agents/pe-scorer.md	haiku	convergent	Prompt quality scorer -- evaluates against 7-criterion rubric, returns dimension scores + improvement suggestions

Agent Routing Guide

Keywords in Request	Likely Agent	Rationale
build, create, construct, assemble, walk me through, 5 elements	pe-builder	Interactive prompt construction with element-by-element guidance
constraint, NPT, forbidden, NEVER, consequence, XML, guardrail	pe-constraint-gen	Systematic constraint formatting using NPT pattern catalog
score, evaluate, rate, quality, rubric, dimensions, improve	pe-scorer	Convergent evaluation against 7-criterion rubric

P-003 Compliance

All prompt engineering agents are workers, NOT orchestrators. The MAIN CONTEXT (Claude session) orchestrates the workflow.

P-003 AGENT HIERARCHY:
======================

  +-------------------+
  | MAIN CONTEXT      |  <-- Orchestrator (Claude session)
  | (orchestrator)    |
  +-------------------+
     |        |        |
     v        v        v
  +------+ +------+ +------+
  | pe-  | | pe-  | | pe-  |   <-- Workers (max 1 level)
  |build | |const | |score |
  +------+ +------+ +------+

  Agents CANNOT invoke other agents.
  Agents CANNOT spawn subagents.
  Only MAIN CONTEXT orchestrates the sequence.

---

Invoking an Agent

Option 1: Natural Language Request

Simply describe what you need:

"Build a prompt for researching authentication patterns for a .NET microservice"
"Generate NPT-013 constraints for a research agent that must not hallucinate sources"
"Score this prompt against the quality rubric"
"Convert these positive instructions to NPT-009 format for agent governance"

The orchestrator selects the appropriate agent based on keywords and context.

Option 2: Explicit Agent Request

Request a specific agent:

"Use pe-builder to walk me through constructing a C3 orchestration prompt"
"Have pe-constraint-gen produce NPT-009 forbidden actions for a T4 research agent"
"I need pe-scorer to evaluate this prompt and tell me what to fix"

Option 3: Task Tool Invocation

For programmatic invocation within workflows:

Task(
    description="pe-constraint-gen: Generate NPT-013 constraints",
    subagent_type="general-purpose",
    prompt="""
You are the pe-constraint-gen agent (v1.0.0).

## INPUT
- **Intent:** Prevent hallucinated source citations in a research agent
- **Target Context:** Agent governance YAML forbidden_actions
- **NPT Format:** NPT-009 (governance YAML context)

## REFERENCE
Load pattern reference: skills/prompt-engineering/rules/npt-pattern-reference.md

## TASK
Generate NPT-009 formatted constraints for the specified intent.
Persist output to: {output_path}
"""
)

---

Quick Reference

Build a Structured Prompt

"Build a prompt for researching authentication patterns for a .NET microservice project"

pe-builder walks through: (1) skill routing, (2) domain scope, (3) data source, (4) quality gate, (5) output path. Produces a complete XML-wrapped prompt.

Generate NPT-013 Constraints

"Generate NPT-013 constraints for a new research agent that must not hallucinate sources"

pe-constraint-gen produces XML blocks:

<forbidden_actions>
  <constraint format="NPT-013">
    NEVER fabricate or hallucinate source citations -- Consequence: downstream agents
    build analysis on nonexistent evidence, compounding errors through the pipeline.
    Instead: explicitly state when no source is available and mark confidence as low.
  </constraint>
</forbidden_actions>

Score an Existing Prompt

"Score this prompt against the quality rubric:
'Research authentication patterns and write them up somewhere'"

pe-scorer returns dimension-level scores (C1-C7) with weighted composite and specific improvement suggestions per failing criterion.

Common Workflows

Need	Agent	Example
Build prompt from scratch	pe-builder	"Help me build a prompt for a C3 architecture decision"
Generate agent guardrails	pe-constraint-gen	"Generate NPT-009 forbidden actions for a T4 research agent"
Convert positive to negation	pe-constraint-gen	"Convert these DOs to NPT-013 format: 'Always cite sources'"
Evaluate prompt quality	pe-scorer	"Score this prompt and tell me what to fix"
Full build-and-score cycle	pe-builder + pe-scorer	"Build a prompt for X, then score it"
Build, score, iterate	pe-builder + pe-scorer (loop)	"Build a prompt for X, score it, and iterate until it reaches 90+"

End-to-End Build-Score-Iterate Workflow:

1. pe-builder constructs prompt (5-element anatomy)
2. pe-scorer evaluates prompt (7-criterion rubric)
3. If score < target: pe-builder revises based on scorer findings
4. Repeat steps 2-3 until score >= target or 3 iterations reached

Default target: >= 90 for standard prompts; adjust per prompt-quality.md threshold table. The orchestrator (main context) coordinates this loop. Agents do not call each other (P-003).

---

Integration Points

Skill/Resource	Relationship	Integration Pattern
/adversary	Distinct scope	/adversary scores deliverables against S-014 quality gate; /prompt-engineering scores prompts against the 7-criterion rubric. Different rubrics, different targets.
/problem-solving	Consumer	ps-researcher, ps-analyst, and other agents consume prompts constructed by pe-builder. The prompt quality directly affects downstream research quality.
.context/rules/prompt-templates.md	Complement	Templates provide ready-to-use prompts with placeholder substitution. pe-builder constructs new prompts when no template fits. Use templates first; invoke pe-builder when templates are insufficient.
.context/rules/prompt-quality.md	Source rubric	pe-scorer implements the 7-criterion rubric defined in prompt-quality.md. The rubric is the SSOT; pe-scorer operationalizes it.

---

Routing Disambiguation

When this skill is the wrong choice and what happens if misrouted.

Condition	Use Instead	Consequence of Misrouting
Adversarial quality review of a deliverable	/adversary	Prompt engineering scores prompts against the 7-criterion rubric, not deliverables against the S-014 quality gate; wrong rubric applied, wrong scoring dimensions used
Research, analysis, or investigation tasks	/problem-solving	Prompt engineering constructs prompts, not research artifacts; no analytical methodology, no data source access, no root cause investigation
Requirements, design, or architecture work	/nasa-se	Prompt engineering produces prompts and constraints, not requirements specifications or architecture documents; wrong deliverable type
Executing an existing prompt template	Direct template use	Template execution requires placeholder substitution only; invoking prompt engineering adds unnecessary construction overhead to a ready-to-use artifact
Agent definition schema validation	/ast or direct H-34 validation	Agent governance YAML follows JSON Schema validation per H-34, not prompt quality rubric; wrong validation mechanism applied
Transcript parsing or meeting notes	/transcript	Prompt engineering has no audio/VTT/SRT processing capability; fundamentally different domain

---

Constitutional Compliance

All agents adhere to the Jerry Constitution v1.0:

P-002 scope note: P-002 applies to pe-builder and pe-constraint-gen (output.required: true). pe-scorer supports optional file persistence (output.required: false); inline scoring output is permitted per its evaluation-only role.

Principle	Requirement	Consequence of Violation
P-002	NEVER produce transient-only output -- persist all artifacts to files	Work products lost on session end; no audit trail; downstream agents cannot reference output
P-003	NEVER spawn recursive subagents -- max 1 level	Agent hierarchy violation; uncontrolled token consumption
P-004	NEVER omit source attribution for generated constraints	Constraint provenance untraceable; reviewers cannot verify NPT pattern compliance
P-020	NEVER override user intent -- ask before destructive ops	Unauthorized action; trust erosion
P-022	NEVER deceive about actions, capabilities, or confidence	Governance undermined; quality assessment invalidated

---

Architecture Notes

Design Rationale

This skill operationalizes three knowledge sources into reusable tooling:

1. PROJ-014 Research Findings: NPT-013 structured negation achieves statistically significant compliance improvement over positive-only framing. The pe-constraint-gen agent encodes this finding into a systematic constraint generation workflow.
2. Prompt Quality Rubric: The 7-criterion rubric from .context/rules/prompt-quality.md is encoded as the pe-scorer evaluation framework. Scoring uses the same weighted formula: total = sum((raw_score_N / 3) * weight_N * 100).
3. 5-Element Prompt Anatomy: The pe-builder agent implements the structured prompt construction pattern from .context/rules/prompt-quality.md (routing, scope, data source, quality gate, output path).

References

Source	Content
.context/rules/prompt-quality.md	7-criterion rubric, 5-element anatomy, anti-patterns
.context/rules/prompt-templates.md	5 copy-paste templates (research spike, implementation, orchestration, architecture, batch)
skills/prompt-engineering/rules/npt-pattern-reference.md	NPT pattern catalog (NPT-009, NPT-013 formats)
projects/PROJ-014-negative-prompting-research/orchestration/neg-prompting-20260227-001/phase-6/final-synthesis.md	PROJ-014 research synthesis (NPT-013 validation data: 100% vs 92.2%, p=0.016)
projects/PROJ-014-negative-prompting-research/orchestration/neg-prompting-20260227-001/ab-testing/ab-testing-synthesis.md	A/B testing results (CONDITIONAL GO via PG-003)
.context/rules/agent-development-standards.md	Agent definition schema, guardrails template, forbidden action format
.context/rules/quality-enforcement.md	Quality gate SSOT, criticality levels, enforcement architecture
projects/PROJ-006-jerry-prompt/	PROJ-006 research: 5-element anatomy derivation, quality rubric development, template validation

---

Skill Version: 1.0.0 Constitutional Compliance: Jerry Constitution v1.0 SSOT: .context/rules/prompt-quality.md, .context/rules/prompt-templates.md Source: PROJ-014 Negative Prompting Research Created: 2026-03-01