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 human-avatar/skills-for-humanity --skill s4h-linguistics-pragmaticsgit clone --depth 1 https://github.com/human-avatar/skills-for-humanityWrote 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/human-avatar/skills-for-humanity/s4h-linguistics-pragmatics)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-linguistics-pragmatics"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-linguistics-pragmatics/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/human-avatar/skills-for-humanity/s4h-linguistics-pragmatics"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-linguistics-pragmatics.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 | $0.00096 | $0.01862 |
| Opus 5 | $0.00048 | $0.00931 |
| Sonnet 5 | $0.00019 | $0.00372 |
| Haiku 4.5 | $0.00010 | $0.00186 |
Grade A, and why
s4h-linguistics-pragmatics 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 9d 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Linguistics: Pragmatics
What people say and what they mean are rarely the same thing.
H.P. Grice's theory of conversational implicature — developed in his 1975 "Logic and Conversation" — is one of the most powerful tools in the analysis of communication. Grice observed that speakers routinely communicate far more than they literally say, and that listeners routinely infer far more than they are told. This works because communication operates against a background assumption of cooperation: speakers are expected to be relevant, truthful, informative, and clear. When they violate one of these maxims — saying less than is relevant, using indirect language when direct would do — listeners do not take the violation at face value. They infer that the speaker is communicating something the literal words do not state.
This is how "could you pass the salt?" functions as a polite request rather than a question about physical capacity. It is how "some of our guests have enjoyed the new policy" implies that others have not. It is how "we need to talk" implies something serious even before any content is delivered. J.L. Austin and John Searle extended this analysis to speech acts — the recognition that utterances do not just describe the world but perform actions: they promise, warn, threaten, command, apologise, and commit. "I'll get to it" is not just a prediction; in context, it can be a promise, a deflection, or a passive refusal.
This skill surfaces what a communication is actually doing below its literal content.
Your Process
Step 1: Establish Literal Content Read the communication exactly as written or spoken. State the literal propositional content — what the words denote, with no inference added. This is the baseline from which all implicature is measured. Be precise: strip out any reading of intent and state only what the words explicitly assert.
Framing check: Confirm the communication and the pragmatic analysis task before continuing. State what you've identified — the utterance or text being analyzed and the implied communication concern — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [one-sentence description of what is being analyzed and what pragmatic question is at stake]. Is that right?"
- Header: "Framing"
- Options:
- Yes — proceed — framing is correct
- Adjust — one element is off; user will correct it before you continue
- Reframe — different situation than read; incorporate the correction before proceeding
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.
- 9d ago First seen · 119 lines · 96 tokens per session scan A 1d950ebda889
s4h-linguistics-pragmatics is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 96 tokens to every session and 1,862 once invoked, about $0.0005 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 2026-09-03.
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