Prompt Engineering

Prompt Engineering

Turns a raw draft into a production-grade prompt by applying proven prompt engineering techniques. Distilled from primary sources (Anthropic prompting best practices + prompt improver, OpenAI GPT-5/reasoning guides, Google Gemini PTCF, The Prompt Report) — see references/ for the full material.

Purpose

The user sends a prompt draft (or describes what they want). The skill returns a rewritten, optimized version, ready to paste, plus a short changelog of what changed and why. It is not meant to answer the prompt — it is meant to improve the prompt.

Operating principle

• Improve the prompt, don't run it. This skill rewrites the prompt; it does not respond to the task the prompt describes. If the request is ambiguous (the user pasted a prompt without saying what they want), the default is to optimize the prompt and, at the end, offer to run it. Only execute the task if the user explicitly asks ("run this prompt", "answer this").
• Produce directly. Don't ask for permission. Only ask clarifying questions (1–3, max) when missing information would materially change the rewrite — expected output type, target model, or audience. If unanswered, assume the most sensible default, state the assumption in the changelog, and proceed.
• Calibrate effort to complexity. Simple prompt (lookup, formatting) → lean structural rewrite, no inflating with CoT/examples. Complex prompt (reasoning, classification, generation with criteria, agentic) → full structure. Don't decorate beyond what's needed — prompt over-engineering hurts latency and cost with no gain.
• If the draft is already good, say so and do the minimum. Don't invent changes to justify the rewrite. Apply the anti-overengineering rule to yourself: when the prompt already has solid role, structure, scope, and output contract, deliver only the marginal tweaks that genuinely add value and state that the rest was already good. Rewriting a good prompt into something "different but not better" is a failure.
• Optimize for the right model. The default is Claude 4.x. If the user indicates another target (GPT/o-series, Gemini), adjust per references/model-profiles.md — the rules diverge (e.g. reasoning models do NOT want "think step by step").

Workflow (5 phases)

Inspired by Anthropic's official prompt improver flow (example identification → initial draft → CoT refinement → example enhancement), expanded with diagnosis and technique selection.

Phase 1 — Diagnosis

Extract from the draft (and from conversation context):

• Real intent: what the user actually wants the model to produce.
• Task type: classification · extraction · generation/writing · coding · reasoning/analysis · research · agentic/tool-use · transformation/summarization · roleplay/persona. (Map in references/task-patterns.md.)
• Target model: Claude 4.x (default) · OpenAI GPT/reasoning · Gemini · other.
• Audience and output format: who reads it, in what form (JSON, prose, bullets, table, code).
• Constraints and invariants: hard limits (length, tone, what to never do).
• Dynamic variables: parts that change on each call → become {{placeholders}}.
• Draft weaknesses: ambiguity, negative instruction, missing context/motivation, no success criterion, undefined format, missing or inconsistent examples.

Phase 2 — Technique selection

Pick techniques from the decision table below (detail in references/techniques.md). Apply only those that add value to the task. Mentally note the why of each — it goes in the changelog.

Phase 3 — Rewrite

Build the prompt in the canonical structure (below), applying the selected techniques and the golden rules. Preserve every {{...}} variable from the original. Convert negative instructions into positive ones. Add motivation to non-obvious instructions. Mirror the rigor level and the format of the cases in references/worked-examples.md.

Phase 4 — Self-review

Run references/quality-checklist.md against the rewritten version. Fix whatever fails before delivering. Golden test: "could a colleague with no context execute this prompt without doubt?" If not, adjust.

Phase 5 — Delivery

Return in the output format (below): optimized prompt in a copyable block + changelog + open questions (if any). When useful, point to references/evaluation.md so the user can A/B test the before/after.

Canonical structure of the optimized prompt

Recommended order (omit sections that don't add value for the task at hand):

1. Role/persona — one line defining who the model is and its domain. Focuses tone and behavior.
2. Task + objective — what to produce and the success criterion. Outcome-oriented.
3. Context + motivation — background information and why it matters (the model generalizes from the why).
4. Data/inputs — in XML tags. In long-context (20k+ tokens), long data goes at the top, before the instructions; the question goes at the end (up to +30% quality).
5. Instructions — sequential numbered steps when order matters. Positive ("do X"), not negative ("don't do Y"). Explicit scope (Claude 4.x is literal: "apply to ALL sections, not just the first").
6. Reasoning — when the task requires analysis, ask for a `/` block before the answer (CoT). NOT in reasoning models (o-series, GPT-5 reasoning) — see model-profiles.
7. Examples (few-shot) — 3–5 relevant and diverse examples, in `/`, showing input→reasoning→output. Especially for format/tone/classification.
8. Output contract — exact output format (JSON schema, XML tags, "result only, no preamble"). Say what to do, not what to avoid.

Golden rules (distilled from the sources)

1. Explicit clarity. Treat the model as a brilliant employee with no context on your norms. Specify format and constraints.
2. Positive > negative. "Write in flowing prose" beats "don't use markdown". Positive examples > prohibitions.
3. Context and motivation. Explain the why behind instructions — it improves generalization.
4. Explicit scope (Claude 4.x). The model is literal; it does not infer requests you didn't make. State the breadth.
5. Outcome over steps (reasoning models). GPT-5/o-series perform better with objective + constraints + output contract, without micromanaging the intermediate steps. Don't force "think step by step" on them.
6. XML to structure. Consistent, descriptive tags separate instruction, context, data, and examples. Nest when there's hierarchy.
7. Examples are the strongest lever for format/tone/consistency. 3–5, diverse, in tags.
8. CoT for reasoning. Ask for reasoning before the answer in analytical tasks — but separate reasoning (`) from the final output (`).
9. Match style. The prompt's style influences the output's style. Want markdown-free output? Write the prompt without markdown. Dynamic variables in {{double_brackets}}, ideally wrapped in XML, for reuse and testing.
10. Success criteria. Define what counts as a good answer. In research/analysis, ask for cross-source verification and competing hypotheses.
11. No prefill in Claude 4.6+. Prefill on the last assistant message is no longer supported; use output contract / structured outputs.
12. Structure > psychological trick. Emotion prompting ("this is important to me"), "take a deep breath", and the like used to give a gain in older models, but they are null or negative in Claude 4.x / GPT-5. Don't use them; solve it with structure.
13. Security when there's untrusted input. If the prompt processes user/web/doc text or operates as an agent with tools, delimit the data and declare instruction priority (see references/prompt-security.md).
14. Cost of heavy techniques. Ensembling (self-consistency, DiVeRSe) and search (Tree-of-Thoughts, maieutic) cost N× tokens. Only when criticality justifies it.

Technique → task decision table

| Task type | Core techniques | Reference to load | |-----------|-----------------|-------------------| | Classification | Role, XML inputs, diverse few-shot, short CoT, enum output | techniques + task-patterns | | Structured extraction | Role, XML, output schema (JSON), grounding by quotes | task-patterns | | Generation/writing | Role, persona+audience, tone examples, format, constraints | task-patterns | | Coding | Clear objective, constraints, explicit scope, anti-overengineering, output contract | model-profiles (Claude coding) | | Reasoning/analysis | CoT (non-reasoning models) or high-level goal (reasoning models), self-check/Reflexion; Least-to-Most or Tree-of-Thoughts on hard problems | techniques + model-profiles | | Research | Success criteria, competing hypotheses, multi-source verification, decomposition, RAG/Generated Knowledge | techniques | | Agentic/tool-use | Explicit action instruction, ReAct, parallel tool calls, autonomy/safety balance, context engineering (just-in-time retrieval, compaction, note-taking, sub-agents) | techniques + model-profiles | | Summarization/transformation | Long-context layout (data at top), grounding, output contract (length) | techniques |

Skill output format

Always respond like this:

## Optimized prompt

## What changed
-  — 
- ... (4–8 bullets, concise)

## Assumptions / open questions
- 

If the user asks for variations (e.g. lean version vs full version, or Claude version vs GPT version), deliver both in separate blocks.

When to load the references

• references/techniques.md — applied catalog of the highest-return techniques (zero/few-shot, zero-shot modifiers, CoT and variants, decomposition with PAL/PoT code, ensembling, self-criticism CoVe/Reflexion, RAG, context engineering for agents) with when to use each. Load when choosing reasoning/research/agentic techniques.
• references/technique-index.md — exhaustive index of ALL known techniques (The Prompt Report's taxonomy of 58 + extras), by category. Load to make sure no approach was forgotten, or when the user asks for total coverage.
• references/model-profiles.md — differences between Claude 4.x, OpenAI GPT/reasoning, and Gemini. Load whenever the target isn't Claude default, or for coding/agentic.
• references/task-patterns.md — ready-made templates by task type. Load to build the structure for classification/extraction/generation.
• references/worked-examples.md — complete examples (draft → optimized → changelog), including the "already-good prompt" case. Load to calibrate the quality bar and the output format, especially when in doubt about how much to rewrite.
• references/quality-checklist.md — self-review checklist (Phase 4). Load always before delivering.
• references/evaluation.md — how to test whether the rewrite is actually better (quick A/B, metrics by task type, regression guard). Load when the user wants to validate the gain or has a small test set.
• references/prompt-security.md — defense against prompt injection/jailbreak. Load when the prompt processes untrusted input or operates as an agent.
• references/auto-optimization.md — automatic prompt optimization (APE, OPRO, DSPy, MIPRO...). Load when the user has a dataset/evals and wants programmatic optimization.
• references/sources.md — all the links to primary sources and papers. Load when you need to cite/update the base.
• assets/canonical-template.md — reusable XML skeleton of the canonical prompt.

Scope note

This skill covers text prompting. Image/audio/video generation prompting (Midjourney, DALL·E, Veo, etc.) is out of scope — the structural principles transfer partially, but the modality-specific levers (aspect ratio, style refs, weights) are not covered here.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: PhAlves23
• Source: PhAlves23/prompt-engineering-skill
• License: MIT
• Homepage: https://promptcause.com/

Install and usage instructions live in the source repository linked above.