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 specification-to-temporal-logic-generatorgit 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/specification-to-temporal-logic-generator)<a href="https://agentmods.dev/skills/arabelatso/skills-4-se/specification-to-temporal-logic-generator"><img src="https://agentmods.dev/badge/skills/arabelatso/skills-4-se/specification-to-temporal-logic-generator/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/specification-to-temporal-logic-generator"><img src="https://agentmods.dev/badge/skills/arabelatso/skills-4-se/specification-to-temporal-logic-generator.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 | $0.00112 | $0.01485 |
| Opus 5 | $0.00056 | $0.00743 |
| Sonnet 5 | $0.00022 | $0.00297 |
| Haiku 4.5 | $0.00011 | $0.00148 |
Grade A, and why
specification-to-temporal-logic-generator 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Specification-to-Temporal-Logic Generator
Translate natural-language requirements into formal temporal logic properties for model checking and formal verification.
Workflow
1. Parse Input Requirements
Extract requirements from natural language text, structured documents, or semi-formal notations.
Identify key elements:
- Events/Actions: What happens (e.g., "request", "response", "button_press")
- States/Conditions: System states (e.g., "authenticated", "locked", "idle")
- Temporal relationships: When things happen (e.g., "always", "eventually", "before")
- Quantification: Scope (e.g., "every", "some", "at least once")
2. Identify Property Type
Classify requirements:
Safety (something bad never happens): Keywords "never", "always not", "must not" → G(!bad_event)
Liveness (something good eventually happens): Keywords "eventually", "will", "guaranteed" → F(good_event)
Response (if X then eventually Y): Keywords "whenever", "if...then", "leads to" → G(X -> F Y)
Precedence (X before Y): Keywords "before", "precedes", "only after" → (!Y) U X
Fairness (repeated opportunities): Keywords "infinitely often", "repeatedly" → G F X
3. Handle Ambiguities
When requirements are ambiguous:
Check common ambiguities: temporal scope, quantification, ordering, duration
Ask clarifying questions:
"The system responds to requests" could mean:
1. Every request eventually gets a response: G(request -> F response)
2. Some requests get responses: EF(request && response)
Which interpretation matches your intent?
State assumptions explicitly:
Formula: G(request -> F response)
Assumptions:
- "Every request" means all requests (universal quantification)
- "Responds" means eventually, with no time bound
See ambiguity_resolution.md for detailed patterns.
4. Select Appropriate Logic
Use LTL for single execution paths, "infinitely often" (G F), "eventually forever" (F G)
What ships with it
6 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.
- 9d ago First seen · 192 lines · 112 tokens per session scan A 801d35ad1828
specification-to-temporal-logic-generator is a skill published in the GitHub repository ArabelaTso/Skills-4-SE (252 stars, last pushed 22d ago), licensed Apache-2.0. It adds 112 tokens to every session and 1,485 once invoked, about $0.0006 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.
Other skills, from other repositories
analysis-to-delivery
A routing guide for turning a new feature request into an analysis and development workflow.
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
google-ads-audit
Google Ads account audit and business context setup. Use for account-health audits and business-context setup. Trigger on "audit my ads", "ads audit", "set up my ads", "onboard", "account overview", "how's my account", "ads health check", "what should I fix in my ads", or when the user is new to NotFair and hasn't run…
webhook-management
Configure and validate CCAM webhook targets across supported chat, incident, automation, and generic providers. Use when listing provider requirements, creating or updating a target, scoping it to alert rules, sending a test notification, reviewing delivery history, or deleting a target.
gesellschaftsrechtliche-satzungen-agb
Für Gesellschaftsrechtliche Satzungen AGB Abgrenzung: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt. Fachgebiet: AGB-Recht-Prüfer. Route: gesellschaftsrechtliche-satzungen-agb.
master-yinguang
A reference-based assistant for questions about Yinguang and Pure Land Buddhism, a Buddhist tradition focused on faith, ethical living, and practice connected with rebirth in the Pure Land. It can answer in Yinguang’s historical teaching style.