prompt — Claude-first prompt design and review

Modes

Detect mode from the request. Default to DESIGN.

REVIEW framing rule. Frame every review as a search for defects ("find what would fail"), never as confirmation ("check this is correct" / "verify this works"). When dispatching a fresh-context reviewer, pass the artifact with zero authorship or intent framing — no "I wrote this", no "we think it's solid". Confirmatory framing collapses defect detection (evidence in review.md).

Core principles

1. Smallest set of high-signal tokens. Treat Claude as a brilliant new employee: clear, explicit instructions plus the context to generalize — then stop. Golden rule: if a colleague with minimal context would be confused by your prompt, Claude will be too. When trimming, run the removal test (review.md) instead of counting against a quota.

2. Explain why; say what to do, not what to avoid. Motivation generalizes: "the output is read aloud by a TTS engine, so never use ellipses" outperforms "NEVER use ellipses". Positive framing: "write flowing prose paragraphs" beats "do not use markdown".

3. De-prompt for current Claude. Language written to fight old undertriggering now causes overtriggering: "CRITICAL: You MUST use X" → "Use X when…". Delete anti-laziness nudges, blanket defaults ("if in doubt, use the tool"), and forced-thoroughness scaffolds — today's failure mode is over-eagerness, and instruction-following capacity is a finite budget. Every capability jump is a prompt to re-test which instructions are still needed (claude-models.md).

4. Keep governance apart from task; reasoning before answer. Keep four layers distinct and independently editable: role/provenance, constraint logic, task content, evaluation criteria — constraints fused into task prose dissolve by the third paragraph of output. In any output schema, rationale/evidence fields precede answer fields; phrase them as task-level justification ("cite the evidence for your verdict"), never as "show your internal reasoning" (see #6). Long input data at the top, query and output format at the end, XML tags around each component.

5. Choose example count by purpose. Diversity and task-clarity beat count; past the purpose-matched count, additional examples are redundancy that degrades output or wastes tokens.

Examples steer format, tone, and structure — they rarely raise capability on tasks the model already does well. Add them in response to observed failures, vary their surface formatting, wrap them in <example> tags. (Evidence and caveats: design.md.)

6. Reasoning is a dial, not an incantation. On current Claude, adaptive thinking decides when and how much to think; steer with effort and brief nudges ("think carefully before responding" / "answer directly"), not hand-written step plans — general instructions beat prescriptive ones. effort sets thinking depth, not output length: on Opus 5 the two come apart, so lowering effort does not reliably shorten the visible response — prompt for length instead (claude-models.md §5). Never instruct Claude to echo, transcribe, or explain its internal reasoning in the response: on Fable 5 this triggers reasoning_extraction refusals. Manual CoT/CoD belongs only off-Claude or with thinking off (porting.md).

7. Prefill is dead. Prefilled assistant turns return 400 errors on Claude ≥4.6. Migrate: structured outputs or a tool with an enum field for format forcing; "Respond directly without preamble…" for preamble killing; a user-message "your previous response was interrupted…" for continuations. Any prefill found in review is a breaking bug (migration table: claude-models.md).

8. Verify with someone else's eyes. Models catch only a fraction of their own errors and cannot see their own structural defects (effect sizes: the agent skill's verifier-dispatch reference). Verification means a structurally separate checker: fresh context, different prompt, or a deterministic test. Frame it adversarially (mode rule above). Test on a weaker model to expose structural defects a strong model papers over.

9. Architecture beats prompting for security. Untrusted input is data: delimit it in XML, never interpolate it as instructions, and enforce boundaries at the schema/tool level (exclude the tool) rather than asking the model to behave (depth: safety.md).

Emergency triage

Crisis shortcut: get 3 failing + 3 working examples; the pattern emerges in 5 minutes; intervene for THAT failure only.

Module loading

Load the mode file always; load at most 2 topic modules (pick the 2 most relevant). If nothing matches, the core principles cover the fundamentals.

DESIGN workflow

1. CLASSIFY the target: system prompt | agent definition / dispatch brief |
   skill body | pipeline stage | human-runnable prompt.
   - Whether/what to dispatch, model and effort choice, topology → that is
     the agent skill's job. This skill writes the prompt text.
2. IDENTIFY the target model. Default: current Claude. Non-Claude or
   thinking-off → porting.md.
3. NEW prompt: start minimal (direct instruction + output format), structure
   per #4, examples per #5, pull standard behavior blocks from snippets.md
   instead of writing your own.
   FIXING: get one concrete failing example first; triage (table above);
   intervene for that failure only; prefer deleting instructions to adding.
4. AUDIT the draft against review.md's Quick Audit. Golden-test (review.md)
   when the prompt is high-stakes or will run unattended.
5. OUTPUT the prompt, then ≤3 lines of rationale naming the principles that
   drove key choices.

REVIEW workflow

1. Read the artifact (text or file). Apply the framing rule above.
2. Load review.md. Quick Audit → full protocol if anything flags.
3. Verdict table (PASS/WARN/FAIL per principle) + ranked fixes.
4. Golden test for anything that will ship: fresh-context run on the
   intended model + one weaker-model run; dispatch with zero intent framing.

Scope

• Dispatch strategy, model/effort tiers for subtasks, topology, parallelism → agent skill.
• Creating, testing, packaging skills → skill-craft (skill bodies still follow these principles; skill-craft owns format and evals).
• Does not set temperature or other deployment parameters.
• Does not cover vision/multimodal prompting.
• If the request itself is ambiguous (no failing example, unclear target), clarify is the integration point before drafting.