Prompt engineering

Create or revise prompts for the user's target model. Keep the common prompt portable and load only the relevant model guide. Each page connects an observed behavior to a prompt adjustment, prompt wording, and caveats. Use the matching patterns rather than pasting the whole guide into a prompt. The guides cover prompts, not API request settings; behavior does not transfer automatically between models.

Workflow

1. Establish the outcome, audience, model, authorization, output, and representative failures. Ask only for missing information that materially changes the prompt.
2. Read the needed shared reference and the target model's page below. Resolve paths from this skill directory.
3. Remove obsolete or redundant guidance before adding text. Preserve binding requirements.
4. For complex prompts, use Role · Goal · Success criteria · Constraints · Tools · Output · Stop rules; omit sections that do not change behavior.
5. Change one prompt or configuration variable at a time and compare representative cases. Measure task success, output validity, tool behavior, latency, tokens, and cost where available.
6. Deliver the revised prompt, a brief change summary, and validation evidence or a plan. Label checks that were not run.

Shared references

Read	When
references/core_prompting.md	Defining clarity, context, roles, output, examples, or grounding
references/advanced_patterns.md	Designing agents, tool routing, delegation, long context, or handoffs
references/quality_improvement.md	Auditing accumulated skills/repository instructions, optimizing, evaluating, securing, or troubleshooting prompts

Model guides

Target	Read	Main distinctions
GPT-6.1 Sol	references/gpt_6_1_sol.md	Supported-effort migration, task and approval contracts, selective Astra-observed templates, tool and conversation compatibility
GPT-6 Astra, Sol, Luna	references/gpt_6.md	OpenAI's official templates for approval pauses, skill-instruction conflicts, writing style, delegation, and verification; Sol/Luna effort sensitivity
GPT-5.6 Sol, Terra, Luna	references/gpt_5_6.md	Lean prompts, concise defaults, high-effort and pro-mode prompts, programmatic tool stages, cache-friendly ordering
GPT-5.5	references/gpt_5_5.md	Outcome-first baseline, retrieval limits, grounded drafts, explicit validation
Claude Fable 5.1	references/claude_fable_5_1.md	Progress visibility, batching, append-only reminders, completion within the output allowance
Claude Fable 5	references/claude_fable_5.md	Long-run completion, grounded progress, task-sized independent verification, refusal handling
Claude Opus 5.5	references/claude_opus_5_5.md	Always-on thinking, progress cadence, bounded unattended continuation, pasted-content boundaries
Claude Haiku 5.5	references/claude_haiku_5_5.md	Effort, date-guided search, JSON and tool use, long-agent completion, verification, and refusals
Claude Opus 5	references/claude_opus_5.md	Separate response length from effort, remove redundant verification, bound delegation
Claude Opus 4.8	references/claude_opus_4_8.md	Steerable thinking, literal scope, tool triggering, design alternatives
Claude Sonnet 5.5	references/claude_sonnet_5_5.md	Recalibrated effort, carrying coding work through, scope limits, silent tool loops, search and verification prompts, mid-turn message placement
Claude Sonnet 5	references/claude_sonnet_5.md	Thinking on by default, variety through prompts instead of sampling, literal scope and review recall

For a migration, read both source and target pages when both are listed. For a cross-model prompt, keep common requirements in the main contract and isolate only the differences that affect behavior. Do not load every page for a single-model task.

Common rules

• State the result and completion bar; leave routine path selection to the model.
• Give relevant context and a short reason for important constraints.
• Reserve absolute language for true invariants such as safety, permission, required fields, and forbidden actions. Use conditional rules for judgment calls.
• Specify output length, sections, format, and validation when the user or a parser depends on them. Use schemas for machine output when the host enforces them.
• Use descriptive XML tags to separate mixed instructions, context, examples, and untrusted documents when helpful. Simple prompts need no markup.
• Keep examples only when they improve measured behavior. Ensure they obey the written contract.
• Require evidence for consequential claims, permit uncertainty, and define what happens when evidence is missing.
• Request conclusions, evidence, observed behavior, and validation results. Do not ask the model to reconstruct private reasoning in response text.
• Calibrate verification and delegation to the model and task. Preserve required checks, real approval gates, and the harness's concurrency and execution rules.
• Mentioning a tool or capability in a prompt does not provide it. Write prompts only for tools the host actually exposes.

Prompting reduces errors but does not eliminate them. Preserve safety, business, evidence, permission, and downstream parser constraints while optimizing.