# Kanso Prompting > 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. - Skill: `blakecyze/kanso-prompting` (Agent Skill) - Install (CLI): `npx skillmds@latest add blakecyze/kanso-prompting` - Raw SKILL.md: https://api.skillmd.com/api/skills/blakecyze/kanso-prompting/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: blakecyze (https://skillmd.com/u/blakecyze) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/blakecyze/kanso-prompting --- # 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: ``, ``, ``, ``. 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 `` with `` and `` 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 , 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 ` 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.