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 tlaplus-model-reductiongit 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/tlaplus-model-reduction)<a href="https://agentmods.dev/skills/arabelatso/skills-4-se/tlaplus-model-reduction"><img src="https://agentmods.dev/badge/skills/arabelatso/skills-4-se/tlaplus-model-reduction/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/tlaplus-model-reduction"><img src="https://agentmods.dev/badge/skills/arabelatso/skills-4-se/tlaplus-model-reduction.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 | $0.00087 | $0.01301 |
| Opus 5 | $0.00044 | $0.00651 |
| Sonnet 5 | $0.00017 | $0.00260 |
| Haiku 4.5 | $0.00009 | $0.00130 |
Grade A, and why
tlaplus-model-reduction 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 7d 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 — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TLA+ Model Reduction (Spec Minimizer)
Overview
This skill analyzes TLA+ specifications and produces minimized versions by eliminating redundancies while preserving all specified properties. The reduction process analyzes reachability, dependency relations, and property relevance to ensure semantic equivalence between the original and reduced specifications.
Workflow
Step 1: Input Analysis
Parse and understand the input TLA+ specification:
-
Identify components:
- State variables (VARIABLES declaration)
- Initial state predicate (Init)
- Next-state relation (Next)
- Invariants (INVARIANT declarations)
- Temporal properties (PROPERTY declarations)
- Type invariants and constraints
-
Extract structure:
- List all actions (disjuncts in Next)
- Identify action guards (enabling conditions)
- Map variable dependencies
- Note fairness constraints if present
-
Understand properties:
- Safety properties to preserve
- Liveness properties to maintain
- User-specified invariants
Step 2: Dependency Analysis
Build comprehensive dependency graphs:
-
Variable dependency graph:
- For each variable, identify which actions read/write it
- Determine which variables are used in property specifications
- Find derived variables (computed from others)
- Identify unused variables
-
Action dependency graph:
- Map which actions enable/disable other actions
- Identify independent vs. dependent action sequences
- Find actions with identical effects
-
Property dependency graph:
- Determine which variables each property depends on
- Identify which actions affect property satisfaction
Step 3: Identify Reduction Opportunities
Systematically find redundancies using the techniques in reduction_techniques.md:
- Redundant state variables:
- Variables never referenced in actions or properties
- Variables whose values are always derivable from others
- Variables that remain constant after initialization
What ships with it
1 file 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.
- 7d ago First seen · 165 lines · 87 tokens per session scan A 23dd6bfa2f29
tlaplus-model-reduction is a skill published in the GitHub repository ArabelaTso/Skills-4-SE (251 stars, last pushed 20d ago), licensed Apache-2.0. It adds 87 tokens to every session and 1,301 once invoked, about $0.0004 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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