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 ArabelaTso/Skills-4-SE --skill nl-to-constraintsgit clone --depth 1 https://github.com/ArabelaTso/Skills-4-SEWrote 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/arabelatso/skills-4-se/nl-to-constraints)<a href="https://agentmods.dev/skills/arabelatso/skills-4-se/nl-to-constraints"><img src="https://agentmods.dev/badge/skills/arabelatso/skills-4-se/nl-to-constraints/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/arabelatso/skills-4-se/nl-to-constraints"><img src="https://agentmods.dev/badge/skills/arabelatso/skills-4-se/nl-to-constraints.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 | $0.00064 | $0.02049 |
| Opus 5 | $0.00032 | $0.01025 |
| Sonnet 5 | $0.00013 | $0.00410 |
| Haiku 4.5 | $0.00006 | $0.00205 |
Grade A, and why
nl-to-constraints 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 8d 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 — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Natural Language to Constraints/Specifications
You are an expert requirements engineer who transforms informal natural language into precise, structured specifications and constraints.
Core Capabilities
This skill enables you to:
- Parse natural language requirements - Extract structured information from user stories, verbal descriptions, and business rules
- Identify constraints - Detect and categorize data, business, temporal, state, authorization, cardinality, and performance constraints
- Generate formal specifications - Produce structured output in BDD format, JSON Schema, and plain text
- Validate completeness - Detect ambiguities, missing edge cases, and conflicting requirements
- Create test scenarios - Derive testable scenarios from requirements
Workflow
Follow this process when converting natural language to specifications:
Step 1: Analyze the Input
Read the natural language input carefully and:
- Identify the main entities and actors
- Extract explicit requirements and rules
- Note implicit assumptions that need clarification
- Flag ambiguous or vague language
- Detect conflicting statements
Step 2: Classify Requirements
Categorize each requirement by:
- Type: Functional, non-functional, business, technical, UI, security
- Priority: Critical, high, medium, low
- Constraint category: Data, business rule, temporal, state, authorization, cardinality, performance
Use the constraint patterns in references/constraint_patterns.md to identify and classify constraints systematically.
Step 3: Extract Constraints
For each identified constraint, extract:
- Entity - What is being constrained
- Category - Type of constraint (see constraint_patterns.md for categories)
- Severity - Must/should/may (RFC 2119 compliance)
- Formal expression - Logical representation when possible
- Validation method - How to check compliance
- Error message - What to show when violated
Example:
What ships with it
2 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.
- 8d ago First seen · 274 lines · 64 tokens per session scan A e195bd17ffd0
nl-to-constraints is a skill published in the GitHub repository ArabelaTso/Skills-4-SE (252 stars, last pushed 22d ago), licensed Apache-2.0. It adds 64 tokens to every session and 2,049 once invoked, about $0.0003 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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