kanso-prompting

name	kanso-prompting
description	Use when rewriting, sharpening, or producing a prompt for Claude or another current frontier model. Sets standing rules for getting better LLM output through better prompts. Loaded by /kanso-task; not directly invoked by the user.
user-invocable	false

kanso-prompting

Standing rules for prompting current frontier models. These principles apply to any prompt this session produces, including prompts being constructed on the user's behalf.

The single governing principle: a prompt earns its length the same way code does. Every clause must do work. Specificity beats verbosity; clarity beats cleverness; structure beats hope.

The prompting taxonomy

1. Lead with the desired outcome

State what the model should produce, in what shape, for what audience, before any context. Don't bury the ask under preamble.

Bad: I've been working on this codebase for a while and I noticed that the auth module has some issues, particularly around session handling, and I was wondering if maybe you could take a look...

Good: Audit src/auth/session.ts for race conditions in session creation. Report findings as a numbered list with file:line references.

2. Explain the why, not just the what

Reasons let the model generalise correctly to cases you didn't spell out. Without the why, it follows the letter and misses the spirit.

Bad: Never use ellipses.

Good: Never use ellipses — the output will be read aloud by a TTS engine that can't pronounce them.

3. State what to do, not what to avoid

Negative-only instructions trigger the behaviour they warn against and leave a vacuum where positive guidance should be. Replace every "don't" with a "do".

Bad: Don't use markdown.

Good: Write in flowing prose paragraphs.

4. Front-load the full task in turn one

For coding and agentic work, ambiguous first turns produce worse results than a single dense first turn. Specify task, scope, constraints, and definition of done upfront. Don't rely on follow-up turns to recover.

5. Show, don't tell — examples beat description

Two or three concrete examples steer the model more than a paragraph of instruction. Use diverse examples that cover edge cases, not three rewrites of the same shape.

6. Structure complex prompts with tags

When a prompt mixes instructions, context, examples, and input, wrap each in tags: <instructions>, <context>, <examples>, <input>. Cuts the misinterpretation rate on anything non-trivial.

7. Long context: documents first, question last

Put large reference material at the top of the prompt; put the actual question at the bottom. Wrap each document in <document> with <source> and <document_content> subtags. Measured uplift on long-context retrieval.

For analysis over long documents, ask the model to extract grounded quotes before reasoning: First extract relevant passages into <quotes>, then answer using only what's there.

8. Match the prompt's voice to the desired output

Markdown-dense prompts produce markdown-dense replies. Plain-prose prompts produce plain-prose replies. Terse prompts produce terse replies. The prompt is a tone sample whether you intend it or not.

9. Ask for action when you want action

Suggest changes produces suggestions. Make these edits produces edits. Review this and recommend produces a memo. The verb sets the artefact.

10. Build in self-checks for high-stakes output

Before finishing, verify each step against the constraints in <criteria> catches a meaningful fraction of errors in code and reasoning tasks. Cheaper than a second prompt.

11. Chain rather than ask for the moon in one shot

Draft → critique against criteria → refine. Three small prompts beat one large one whenever the steps are inspectable. Use single-shot only when the task is genuinely indivisible.

12. Use the system prompt to set role and stance

Even one sentence (You are a senior reviewer who flags only blocking issues) sharpens tone, scope, and what gets surfaced. Don't waste it on filler.

Current frontier models — what to assume

Prompt scaffolding written for older model generations now degrades output rather than improving it. Assume the following of any current frontier model:

• Instruction following is literal. The model does not silently generalise an instruction from one item to all similar items. If you want it applied broadly, say so explicitly.
• Drop the aggressive language. CRITICAL, YOU MUST, ALL CAPS, and triple exclamation marks over-trigger and cause worse output, not better. Use normal declarative voice.
• Drop the anti-laziness scaffolds. Force-thorough, force-tools, "do not stop until" preambles were workarounds for older models and are now noise.
• Verbosity self-calibrates. Short on simple tasks, long on open-ended ones. If you need a specific length, prompt with a positive example rather than a word count.
• General reasoning instructions beat prescribed step plans. Think thoroughly before answering outperforms a hand-written checklist in most cases. The model's reasoning is often better than what you'd prescribe.
• Default voice is direct and not validation-forward. Fewer emoji, less "great question". If you want warmth, ask for it.
• If reasoning is shallow, raise effort before adding prompt scaffolding. The effort parameter does more than re-prompting can.

Perishable facts — checked 2026-07

Everything above is behavioural and ages slowly. The facts below are pinned to specific releases and go stale first — verify against current docs before repeating them:

• Current families: Claude 5 (Fable/Mythos), Opus 4.8, Sonnet 5, Haiku 4.5.
• Assistant-prefill is unavailable on Claude 4.6+ models. Use structured outputs or system-prompt guidance instead.

Anti-patterns

• Vague one-liners (make it better, clean this up) with no success criteria.
• Stacking contradictions (be thorough but concise, be creative but conservative) and leaving the model to pick.
• Negative-only instructions with no positive replacement.
• ALL-CAPS emphasis or CRITICAL: flags — degrades on current models.
• Long document pasted after the question instead of before it.
• Stale Claude 3-era anti-laziness boilerplate copied forward.
• Asking can you suggest when you want edits made.
• Treating clarifying turns as a substitute for a precise first turn in agentic workflows — each clarifying turn costs context and momentum.
• Burying the ask under preamble. The first sentence is the highest-leverage sentence.

Positive defaults

• Start with the verb. Audit, Refactor, Write, Explain.
• Name the artefact. …as a numbered list, …as a single commit message, …as a diff.
• Name the audience. …for a senior reviewer, …for a new contributor.
• State the stopping condition. Stop after the report; do not edit files.
• Give two examples when shape matters more than description.

The kanso test for a prompt

Before sending: would another engineer, given only this prompt and no context from your head, produce something close to what you want? If not, the prompt is incomplete. Add the missing piece — don't hope the model fills it.