How do I install the AI Instruction Writing skill?

Run npx skillmds@latest add tomevault-io/ai-instruction-writing in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is AI Instruction Writing safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with AI Instruction Writing?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is AI Instruction Writing free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published AI Instruction Writing?

tomevault-io (@tomevault-io) published this skill. Their other Agent Skills are listed on their SkillMD profile.

AI Instruction Writing by tomevault-io

Name: AI Instruction Writing
Author: tomevault-io

AI Instruction Writing

Write AI-consumed content using positive, action-oriented language as the primary carrier of every instruction. Use when creating or editing any artifact an AI reads at inference time — SKILL.md files, .mdc rule files, CLAUDE.md/AGENTS.md/GEMINI.md configuration, prompt templates, system prompts, commands, agent and sub-agent definitions, instruction sets, and persona definitions. Use when this capability is needed.

tomevault-io Updated Sep 17, 2026

File contents

ai_instruction_writing

Source: theafh/ai-modules — distributed by TomeVault.

---name: ai-instruction-writingdescription: Write AI-consumed content using positive, action-oriented language as the primary carrier of every instruction. Use when creating or editing any artifact an AI reads at inference time — SKILL.md files, .mdc rule files, CLAUDE.md/AGENTS.md/GEMINI.md configuration, prompt templates, system prompts, commands, agent and sub-agent definitions, instruction sets, and persona definitions. Use when this capability is needed.---# ai_instruction_writing<ai_instruction_writing> <objective> Write AI-consumed content so every instruction's primary carrier is a positive, action-oriented statement that tells the LLM what to do, what something is, or how it should be. Allow negative or contrastive supplements only as a catch-all for what positive guidance cannot enumerate. </objective> <core_rule> Every instruction's primary carrier is a positive statement that tells the LLM what to do, what something is, or how it should be. A negative or contrastive supplement ("don't X", "avoid Y", "X instead of Y", double negatives, implicit negation by comparison) earns its place only when listing every positive case is infeasible — the negative then names a broader class as a catch-all for what falls outside the positive guidance. When the positive set is fully enumerable, the negative restates the inverse and adds nothing; cut it. </core_rule> <self_check> <procedure>Delete the negative or contrastive portion of a rule, then apply the matching outcome below.</procedure> <when_positive_is_empty_or_vague>The rule is inverted. Rewrite the positive carrier first.</when_positive_is_empty_or_vague> <when_positive_is_complete>The negative just restates the inverse. Drop the negative — it is redundant.</when_positive_is_complete> <when_negative_names_a_broader_class>Keep the negative. It covers a long tail that no single positive could enumerate.</when_negative_names_a_broader_class> </self_check> <applicability> This rule governs the model's *output*. In meta or teaching context — including this skill — contrastive pairs, ❌/✅ examples, and "X replaces Y" patterns are legitimate when they illustrate how to transform inputs. </applicability> <authoring_guidelines> <start_with_action_verbs>Use, Write, Create, Define, Implement, Apply.</start_with_action_verbs> <be_specific>Tell exactly what to do with concrete details.</be_specific> <use_imperative_mood>Write commands that tell the LLM what to do.</use_imperative_mood> <lead_with_the_positive_carrier>Put the actionable instruction first; layer supplements after.</lead_with_the_positive_carrier> <preserve_technical_precision>Keep specific details, error codes, and identifiers when transforming.</preserve_technical_precision> <enhance_rather_than_replace>Add specificity, rationale, and context on top of existing positive rules.</enhance_rather_than_replace> </authoring_guidelines> <positive_only_examples> <when_to_apply>These enumerate their full positive set, so no catch-all negative is needed — see catch_all_negative for the cases where the long tail forces one.</when_to_apply> <examples> <example>Use specific exception types like `except ValueError:`.</example> <example>Use absolute imports like `from package.module import function`.</example> <example>Define named functions for reusable logic.</example> <example>Write one statement per line for readability.</example> <example>Implement error handling with specific exception types.</example> <example>Apply consistent formatting throughout the codebase.</example> <example>Write clear, descriptive error messages that guide users.</example> <example>Provide specific examples for each concept.</example> </examples> </positive_only_examples> <catch_all_negative> <principle> A negative supplement earns its place when listing the positive cases exhaustively is infeasible. The negative names a broader class as a catch-all for what falls outside the positive guidance. When the positive set is finite and enumerable, no negative is needed. </principle> <valid_catch_all> <pattern>Positive carrier plus catch-all for the long tail.</pattern> <examples> <example>"Use ASCII characters in identifiers; don't include Unicode symbols, emoji, or non-printing characters." The positive is one finite class; Unicode is too broad to enumerate, so the negative names the excluded class.</example> <example>"Open every section with an action verb (Use, Write, Create, Define, Implement, Apply); don't lead with passive voice or noun phrases." The positives are partial; the negative catches the long tail of non-action openings.</example> </examples> </valid_catch_all> <invalid_negative_only> <pattern>Negative-only, no positive carrier.</pattern> <examples> <example>"Don't use relative imports." Rewrite: "Use absolute imports like `from package.module import function`."</example> <example>"Avoid unused imports." Rewrite: "Import only modules you actively use; remove unused imports immediately to prevent F401 errors."</example> </examples> </invalid_negative_only> <invalid_redundant_negative> <pattern>Negative just inverts an enumerable positive (noise).</pattern> <examples> <example>"Use 4-space indentation; don't use tabs or 2-space indents." Drop the negative: "Use 4-space indentation."</example> </examples> </invalid_redundant_negative> </catch_all_negative> <transformation_patterns> <when_to_use>Apply these patterns when rewriting existing negative rules into positive ones. The pairs show the transform; the model's output is the positive half, optionally with supplemental context.</when_to_use> <action_focused>"Use X" replaces "Don't use Y".</action_focused> <outcome_focused>"Ensure X" replaces "Avoid Y".</outcome_focused> <solution_focused>"Implement X" replaces "Prevent Y".</solution_focused> <guidance_focused>"Follow X" replaces "Never Y".</guidance_focused> <success_focused>"Apply X" replaces "Stop Y".</success_focused> </transformation_patterns> <good_vs_poor_transformations> <principle>Both rows take a negative input and produce a positive output. The good versions preserve the technical precision the poor ones discard.</principle> <poor> <issue>Deletes important information.</issue> <examples> <example>"Never import unused modules" → "Import only what you use".</example> <example>"Don't assign unused variables" → "Use variables only when needed".</example> </examples> </poor> <good> <strength>Preserves and enhances information.</strength> <examples> <example>"Never import unused modules" → "Import only modules you actively use; remove unused imports immediately to prevent F401 errors".</example> <example>"Don't assign unused variables" → "Assign variables only when you need them; use `_` for intentionally unused values to prevent F841 errors".</example> </examples> </good> </good_vs_poor_transformations> <enhancement_strategies> <when_to_apply>Apply these when improving existing positive rules — layer value on top rather than rewriting.</when_to_apply> <add_specificity>Include error codes, specific examples, concrete details.</add_specificity> <expand_context>Add rationale, benefits, and when to apply.</expand_context> <enhance_examples>Provide more detailed, actionable examples.</enhance_examples> <improve_clarity>Make the action more specific without losing content.</improve_clarity> </enhancement_strategies> <content_layout> <lead_with_positive>Always start sections with what to do.</lead_with_positive> <allocate_most_space_to_positive_guidance>The positive form is the carrier; supplements stay short.</allocate_most_space_to_positive_guidance> <end_with_positive>Conclude with positive reinforcement when wrapping a section.</end_with_positive> </content_layout></ai_instruction_writing>---> Source: [theafh/ai-modules](https://github.com/theafh/ai-modules) — distributed by [TomeVault](https://tomevault.io).

Frequently asked questions