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 pproenca/dot-skills --skill language-spec-authorgit clone --depth 1 https://github.com/pproenca/dot-skillsWrote 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/pproenca/dot-skills/language-spec-author)<a href="https://agentmods.dev/skills/pproenca/dot-skills/language-spec-author"><img src="https://agentmods.dev/badge/skills/pproenca/dot-skills/language-spec-author/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/pproenca/dot-skills/language-spec-author"><img src="https://agentmods.dev/badge/skills/pproenca/dot-skills/language-spec-author.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 | $0.00198 | $0.02237 |
| Opus 5 | $0.00099 | $0.01118 |
| Sonnet 5 | $0.00040 | $0.00447 |
| Haiku 4.5 | $0.00020 | $0.00224 |
Grade A, and why
language-spec-author 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 5d 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 — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Author an Implementable Language Specification
Take an author from a rough language idea to a specification precise enough that a developer with zero access to the author can build a conforming implementation from the document alone. The output is a spec in the mold of the GraphQL specification — grammar, semantics, validation, execution, and conformance — that other devs can implement and interoperate against.
The hard part of a language spec is not prose; it is eliminating the ambiguities the author does not know they are leaving. Two implementers reading a vague sentence produce two incompatible languages. So this skill's method is grilling: ask one sharp question at a time, recommend a default, and refuse to write down any answer that fails the stranger / edge-case / two-implementers tests. It bundles a scaffold script, a completeness linter, and reference docs for the anatomy, the formal notation, and the interview itself.
When to Apply
- The user wants to design or formalize a language: a DSL, query language, config or data format, template language, expression language, or wire protocol.
- The user has a working idea or prototype and needs a written spec others can implement against — "spec out my query language", "formalize this syntax".
- The user asks for a grammar, a language spec, or an implementable definition and needs the lexical/syntactic/semantic structure worked out, not just examples.
- The user has a spec draft that implementers keep asking questions about — the holes need to be found and closed.
Do not use this for: authoring a Python language proposal (use python-pep-author),
an internal company RFC or design doc (use dev-rfc / feature-spec), or documenting an
API surface that already has a fixed definition.
Prerequisites
- Bash + coreutils (
awk,sed,grep,date) for the two scripts — present by default on macOS/Linux. No language runtime is required to draft or lint. - The author available to answer questions. This skill is an interview; it cannot invent the language's decisions, only extract, pressure-test, and record them.
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 173 lines · 198 tokens per session scan A 8d8a5c022357
language-spec-author is a skill published in the GitHub repository pproenca/dot-skills (205 stars, last pushed 24d ago), licensed MIT. It adds 198 tokens to every session and 2,237 once invoked, about $0.0010 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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