Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add theafh/ai-modules --skill ai_instruction_writinggit clone --depth 1 https://github.com/theafh/ai-modulesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/theafh/ai-modules/ai_instruction_writing)<a href="https://agentmods.dev/skills/theafh/ai-modules/ai_instruction_writing"><img src="https://agentmods.dev/badge/skills/theafh/ai-modules/ai_instruction_writing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/theafh/ai-modules/ai_instruction_writing"><img src="https://agentmods.dev/badge/skills/theafh/ai-modules/ai_instruction_writing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00087 | $0.02511 |
| Opus 5 | $0.00044 | $0.01256 |
| Sonnet 5 | $0.00017 | $0.00502 |
| Haiku 4.5 | $0.00009 | $0.00251 |
Grade A, and why
ai_instruction_writing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ai_instruction_writing
<ai_instruction_writing> 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 when they add information the positive carrier cannot imply: a broad catch-all class, specific banned forms, or an exact set a downstream mechanism checks. Build each of those statements as an ordinary sentence, so no em dash or en dash appears in it.
<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 when it adds information the positive carrier cannot imply. It may name a broader catch-all class for what falls outside the positive guidance, or it may specify exact banned forms or checked sets that a reader or tool needs. Cut a negative only when it restates the inverse of the positive and adds nothing. </core_rule>
<self_check> Delete the negative or contrastive portion of a rule, then apply the matching outcome below. <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 because it is redundant.</when_positive_is_complete> <when_positive_is_complete_but_negative_is_load_bearing>The positive can read complete while the listed exclusions still carry information it cannot imply. Keep the negative when the reader learns a banned form from it, or when a downstream tool acts on the exact named set.</when_positive_is_complete_but_negative_is_load_bearing> <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>
<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>
<sentence_construction>
<plain_sentence_carrier>Carry each instruction on an ordinary sentence whose clauses are joined by ordinary punctuation, so the reader takes the relation between two clauses from the joint that holds them together.</plain_sentence_carrier>
<ordinary_joins>Join clauses with a comma, a colon, a full stop, or a conjunction such as because, so, while, or although.</ordinary_joins>
<rewrite_an_unearned_em_dash>Where an em dash is the only thing holding two clauses together, rebuild the sentence: introduce the elaboration with a colon, or split it into two sentences. Substituting a hyphen, a double hyphen, or an en dash preserves the same unearned break, so it resolves nothing and counts as no fix.</rewrite_an_unearned_em_dash>
<split_inside_a_yaml_scalar>Inside an unquoted YAML scalar such as a frontmatter description:, split the sentence in two rather than reaching for the colon, because a mid-value colon is a parser footgun that downstream discovery checks flag.</split_inside_a_yaml_scalar>
<rewrite_an_aside_or_list_em_dash>An em dash that sets off a genuine aside or introduces a list goes the same way, because the character is banned outright rather than only where it stands in for a joint. Set the aside off with a pair of commas or parentheses, give it its own sentence, or introduce the list with a colon.</rewrite_an_aside_or_list_em_dash>
<encoding_stays_a_separate_question>A curly quote and an ellipsis are valid UTF-8, so a tool that flags them raises a question about what the consuming parser reads, which is separate from the dash rules above. Answer it by confirming the consumer reads UTF-8, and keep the character as written.</encoding_stays_a_separate_question>
</sentence_construction>
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago Changed · +3 tokens per session cf73bf3ac410
- 9d ago First seen · 139 lines · 84 tokens per session scan A 852ac25ae57f
ai_instruction_writing is a skill published in the GitHub repository theafh/ai-modules (38 stars, last pushed yesterday), licensed MIT. It adds 87 tokens to every session and 2,511 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…