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 kotaroyamame/formal-agent-contracts --skill export-human-specgit clone --depth 1 https://github.com/kotaroyamame/formal-agent-contractsWrote 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/kotaroyamame/formal-agent-contracts/export-human-spec)<a href="https://agentmods.dev/skills/kotaroyamame/formal-agent-contracts/export-human-spec"><img src="https://agentmods.dev/badge/skills/kotaroyamame/formal-agent-contracts/export-human-spec/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/kotaroyamame/formal-agent-contracts/export-human-spec"><img src="https://agentmods.dev/badge/skills/kotaroyamame/formal-agent-contracts/export-human-spec.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00212 | $0.02934 |
| Opus 5 | $0.00106 | $0.01467 |
| Sonnet 5 | $0.00042 | $0.00587 |
| Haiku 4.5 | $0.00021 | $0.00293 |
Grade A, and why
export-human-spec 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 10d 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 — 345 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Exporting VDM-SL Specifications to Human-Readable Documents
Read a VDM-SL specification and produce a structured, clear natural language document that domain experts and non-technical stakeholders can review and understand.
VDM-SL仕様を読み取り、ドメインエキスパートや非技術者が理解・レビューできる 構造化された自然言語仕様書を生成する。
Why This Skill Exists
Formal specifications are precise but opaque to most stakeholders. In practice, formal specs need a "human-facing twin" — a document that says the same things in natural language so that:
- Domain experts can verify the spec matches business reality
- Project managers can understand scope and constraints
- QA teams can derive test scenarios from readable rules
- New team members can onboard without learning VDM-SL
- Compliance/legal can review business logic for regulatory conformance
This skill bridges formal precision and human accessibility. The generated document is not a simplification — it's a faithful translation that preserves all information while making it readable.
Dialogue Flow
Step 1: Read the VDM-SL Specification
Ask the user for the .vdmsl file path (or find it in the workspace).
Parse and catalog the specification elements:
- Modules — How many, what are they named
- Types — Record types, union types, aliases, with their invariants
- State — State variables, initialization, state invariants
- Functions — Pure functions (side-effect free)
- Operations — State-mutating operations with pre/post-conditions
- Values — Named constants
Report what was found:
📄 Specification Analysis: bank-account.vdmsl
Modules: 1 (BankAccount)
Types: 4 (Account, AccountId, Money, TransferResult)
State: 1 (BankSystem with 2 variables)
Invariants: 3
Operations: 5 (Deposit, Withdraw, Transfer, GetBalance, CloseAccount)
Pre-conditions: 4
Post-conditions: 5
Functions: 1 (CalculateInterest)
Step 2: Ask About Output Preferences
Before generating, clarify what the user needs. Different audiences need different levels of detail. Use AskUserQuestion:
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.
- 10d ago First seen · 345 lines · 212 tokens per session scan A cc1e3726697a
export-human-spec is a skill published in the GitHub repository kotaroyamame/formal-agent-contracts (1 stars, last pushed 2mo ago), licensed MIT. It adds 212 tokens to every session and 2,934 once invoked, about $0.0011 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 2026-08-31.
Other skills, from other repositories
incident
Incident response and postmortem generation from git/deploy context. When something breaks in production, this skill builds a timeline, identifies the probable cause, and generates a structured postmortem document. Flags: --since, --service, --sev, --revert, --comms, --dry-run.
presentation
Generate PowerPoint (PPTX) presentations from a topic, outline, or content file. Creates professional slides using python-pptx with consistent theming and typography. Modes: [topic] (from scratch), from [file] (from markdown), --slides N, --theme dark|light|corporate, --outline-only, --out [path], --lang [code].
knowledge-curation
Process a source document (functional spec, technical doc, ADR, meeting notes, interview, workshop, glossary) into curated knowledge under /knowledge. Use when asked to curate, extract, decompose, or structure a business document, or when curation is mentioned alongside a file path or attachment or a non-document…
skill-doc-delivery
Convert markdown to DOCX, PPTX, XLSX, PDF office documents — use when you need exportable deliverables.
wiki
Markdown-first knowledge base where the LLM acts as librarian. Ingests raw sources, compiles and interlinks topic files, self-maintains an index. No vector DB or embeddings required -- uses LLM-native navigation over structured markdown up to 400K words.
meeting-notes
Use when a meeting just ended and a transcript or rough notes must become a record the absent can act on — decisions with their why, actions with an owner and a real date, open questions, a recap inside 24–48h — or to clean an AI-notetaker draft that invented tasks or swapped speakers. NOT the durable ADR with…