Claude Five Prompting Guide

Claude 5 Prompting Guide

Prompting Claude 5 generation models (Sonnet 5, Opus 5, Fable 5) inverts two habits from earlier generations:

1. The bottleneck moved. Work quality is no longer capped by model capability but by the user's ability to surface unknowns — the gap between the map (prompts, skills, context) and the territory (the codebase, the real world). Prompting is now an iterative discovery process, not a specification exercise.
2. Constraints became liabilities. Anthropic removed over 80% of Claude Code's system prompt for these models with no measurable loss on coding evals. Rigid rules that once prevented worst cases now create conflicting instructions and crowd out the model's judgement.

Everything in this skill derives from two primary sources by Thariq Shihipar (Anthropic, July 2026). When in doubt, they are authoritative over any paraphrase here:

• Field guide: https://claude.com/blog/a-field-guide-to-claude-fable-finding-your-unknowns
• Context engineering: https://claude.com/blog/the-new-rules-of-context-engineering-for-claude-5-generation-models

Core principles (always apply)

Prefer judgement over rules. State the goal and let the model read the room. Replace "never do X, always do Y" with a principle that generalizes: Write code that reads like the surrounding code: match its comment density, naming, and idiom. Reserve hard rules for genuinely non-negotiable areas (security, compliance, destructive operations).

Design interfaces, not examples. Few-shot examples constrain the exploration space of Claude 5 models. Instead, make the interface itself expressive: an enum of pending | in_progress | completed plus "keep exactly one item in_progress" teaches tool usage better than three worked examples.

Disclose progressively. Load the right context at the right time instead of front-loading everything. Split long guidance into a tree of files; reference them from a lightweight entry point. A CLAUDE.md should briefly say what the repo is for and spend its tokens on gotchas — never on things Claude can learn by reading the filesystem.

Say it once. Repetition and end-of-context emphasis were workarounds for older models. Put tool instructions in tool descriptions, not duplicated in the system prompt. Every duplicate is a future source of conflicting instructions.

Prefer rich references over prose specs. Source code is the highest-fidelity reference — point Claude at a folder that implements the desired behavior, even in another language. HTML mockups beat design descriptions and screenshots. Test suites and rubrics beat requirement lists.

Balance specificity. Too specific and Claude follows instructions off a cliff when a pivot was appropriate; too vague and it fills gaps with industry defaults that may not fit. The escape hatch from this dilemma is surfacing unknowns early (see below), plus always giving Claude your starting point: where you are in your thinking, and your experience with the problem and codebase.

Workflow selection

Match the situation to the right reference file. Read only what applies:

For quick questions answerable from the core principles above, answer directly without loading references.

Invocation with arguments: audit mode

This skill may be invoked with one or more file paths as arguments (e.g. /claude-five-prompting-guide ~/.claude/CLAUDE.md). An argument switches the skill into audit mode: review the given file(s) against this guide and report findings. Without arguments, operate in consultative mode as described above.

Audit mode is self-contained — it needs only the target file(s) and this skill's references, not the session's conversation context. Because of that, prefer delegating it to a read-only sub-agent so the reference material and the target file never enter the main context; only the findings report comes back. Run it in the foreground when the audit result is the immediate next input (the user is waiting to act on it); use background only if other work continues meanwhile. If no sub-agent mechanism is available, run the audit inline.

The sub-agent prompt should instruct it to:

1. Read references/anti-patterns.md and references/context-engineering.md from this skill's directory (add references/unknowns-framework.md only if the target is a prompt/spec for a concrete task rather than standing context).
2. Read the target file(s).
3. Apply the audit procedure at the end of anti-patterns.md.
4. Return only a findings report: per finding, the anti-pattern name, the offending lines (quoted or line-referenced), the suggested rewrite, and an estimated line delta. End with a summary table: findings count per anti-pattern and total estimated reduction.

Report the findings to the user verbatim; do not re-read the reference files in the main context to double-check — trust the delegated audit, and offer to apply the rewrites as a follow-up.

The unknowns loop in one paragraph

Classify what you know using the four quadrants (known knowns, known unknowns, unknown knowns, unknown unknowns). Before implementation: run a blind spot pass for unknown unknowns, brainstorm and prototype to surface unknown knowns, have Claude interview you one question at a time prioritizing architecture-changing answers, provide source-code references, and review an implementation plan that leads with the decisions most likely to change. During implementation: keep an implementation-notes.md logging deviations — on unexpected edge cases, pick the conservative option, log it, keep going. After: build a pitch/explainer artifact and take a quiz on the changes before merging. Full patterns with canonical example prompts live in references/unknowns-framework.md.

Verification

When producing or reviewing a prompt/CLAUDE.md/skill with this guide, check:

• Could any rule be restated as a principle plus trust in judgement? If a rule fires incorrectly in an imaginable common case, it is over-constrained.
• Does any instruction appear in more than one place? Deduplicate to the most specific location.
• Is anything stated that the model could infer from the filesystem, the surrounding code, or its own knowledge? Delete it.
• Is rarely-needed content behind progressive disclosure (separate file, skill, or deferred tool) instead of always-loaded?
• In Claude Code, /doctor automates this rightsizing — recommend it to the user.