Mega eval prompt derivation
Skill Cartooli/mega-eval-skill/skills/mega-eval-prompt-derivation
Decompose any user request into a structured Prompt Derivation Engine spec — a JSON object that captures intent, task type, context, output contract, instructions, eval checks, and execution plan. Use this skill when you need to convert a vague or complex request into a precise, schema-validated prompt blueprint before execution. Trigger on: 'derive a prompt for', 'prompt spec', 'decompose this request', 'prompt derivation', 'structured prompt', 'prompt blueprint', or when an upstream orchestrator needs a machine-readable task breakdown.From its SKILL.md
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SKILL.md
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Prompt Derivation Engine
Convert a user request (plus any attached artifacts) into a fully populated Prompt Derivation Engine Spec — a single JSON object conforming to schemas/prompt-derivation-engine.schema.json.
When to Use
- Standalone: User asks you to derive / decompose / blueprint a prompt.
- Sub-prompt: An orchestrator (e.g. mega-eval Phase 0) calls this skill to produce a structured spec that downstream phases can consume programmatically.
Inputs
| Input | Required | Description |
|---|---|---|
| User request text | Yes | The raw ask — can be a sentence, paragraph, or pasted document |
| Artifacts | No | Files, URLs, JSON blobs, images, etc. attached to the request |
| User preferences | No | Tone, verbosity, format, citation policy overrides |
Procedure
Step 1 — Parse the request
Read the user input and any artifacts. Identify:
- What they want (goal, task type, subject)
- How they want it (format, length, tone, constraints)
- What could go wrong (failure modes, uncertainty)
- What tools/data are needed (web search, retrieval, closed-book)
Step 2 — Populate the spec
Fill every required field of the schema. Use these guidelines:
| Section | Key decisions |
|---|---|
request | Generate a UUID-style request_id. Copy user_input verbatim. Map stated preferences; default to tone: "neutral", verbosity: "medium", format_preference: "markdown". |
task_spec | Pick the single best task_type. List concrete requested_operations (1-4). Write success_criteria as testable statements. Set freshness.required true only if the answer depends on data newer than model training. |
latent_task_state | Classify intent_class and knowledge_mode. Be honest about uncertainty_level. List failure_modes the downstream executor should watch for. |
context_bundle | Include one context_item per meaningful input chunk (user text, each artifact, any system context). Set compression_policy based on total context size — minimal if small, balanced if large. |
output_contract | Choose format matching user preference or task nature. must_include = things the output must contain. must_avoid = hallucination risks, off-topic drift, banned content. |
instruction_package | Write a tight system_instruction (role + constraints, 2-4 sentences). Write user_instruction as the concrete ask (1-3 sentences). Prefer instruction_style: "structured_minimal". Add banned_patterns for known failure modes (e.g. "Do not start with 'Sure, here is...'"). |
eval_plan | Define 2-5 checks matching the success criteria. Pick repair_strategy based on what's most likely to fail. |
execution_plan | List tool_calls if the task needs web search, file reads, etc. Write ordered_steps as a concise numbered plan (3-8 steps). |
metadata | Set created_at to current ISO-8601 timestamp. engine_version: "1.0.0". |
Step 3 — Validate
Mentally walk the schema's required fields and enum constraints. Fix any mismatches before outputting.
Step 4 — Output
Return the JSON object. When called as a sub-prompt, save it to:
<workspace>/prompt-derivation-spec.json
When called standalone, print it directly.
Output Format
A single JSON object conforming to schemas/prompt-derivation-engine.schema.json. No wrapping markdown, no commentary — just the JSON (unless the user explicitly asks for explanation).
Schema Reference
The full JSON Schema lives at:
schemas/prompt-derivation-engine.schema.json
Read it before your first derivation to ensure field-level compliance.
Example (abbreviated)
{
"schema_version": "1.0.0",
"request": {
"request_id": "a1b2c3d4",
"user_input": "Compare the top 3 project management tools for small teams"
},
"task_spec": {
"goal": "Produce a structured comparison of project management tools suitable for teams of 2-15 people",
"task_type": "compare",
"subject": "project management tools for small teams",
"requested_operations": ["compare", "rank", "evaluate"],
"constraints": [
{ "type": "scope", "value": "limit to top 3 by market share" },
{ "type": "length", "value": "medium — 800-1200 words" }
],
"success_criteria": [
"Covers pricing, collaboration features, and integrations for each tool",
"Includes a summary comparison table",
"Ends with a clear recommendation"
],
"freshness": { "required": true, "recency_days": 90, "notes": "Pricing may have changed recently" }
},
"latent_task_state": {
"intent_class": "analysis",
"knowledge_mode": "retrieval_augmented",
"uncertainty_level": "medium",
"needs_external_data": true,
"needs_tools": true,
"failure_modes": ["outdated pricing", "missing a major competitor", "generic pros/cons"],
"minimal_context_keys": ["user_input"],
"canonical_prompt_form": "compare"
},
"context_bundle": {
"context_items": [
{ "key": "user_request", "value": "Compare the top 3 project management tools for small teams", "source": "user_input" }
],
"compression_policy": "minimal"
},
"output_contract": {
"format": "markdown",
"sections": ["Introduction", "Tool Profiles", "Comparison Table", "Recommendation"],
"must_include": ["pricing", "comparison table"],
"must_avoid": ["unsupported claims", "affiliate-style language"],
"length_target": "medium"
},
"instruction_package": {
"system_instruction": "You are a product analyst writing for non-technical small-team leads. Be specific about pricing tiers and feature limits. Cite sources when possible.",
"user_instruction": "Compare the top 3 project management tools for small teams (2-15 people). Include a comparison table and a recommendation.",
"instruction_style": "structured_minimal",
"banned_patterns": ["In conclusion", "As an AI"]
},
"eval_plan": {
"checks": [
{ "name": "has_comparison_table", "type": "format", "severity": "high" },
{ "name": "covers_all_three_tools", "type": "coverage", "severity": "high" },
{ "name": "pricing_is_current", "type": "evidence", "severity": "medium" }
],
"repair_strategy": "context_repair"
},
"execution_plan": {
"tool_calls": [
{ "tool_name": "WebSearch", "purpose": "Find current pricing and feature lists for top PM tools" }
],
"ordered_steps": [
"Search for current top project management tools by market share",
"Gather pricing, features, and integration details for each",
"Build comparison table",
"Write per-tool profiles",
"Draft recommendation based on small-team fit",
"Self-check against eval_plan"
]
},
"metadata": {
"created_at": "2026-03-27T12:00:00Z",
"engine_version": "1.0.0"
}
}