What is model-validation?

A model-checking and validation workflow for assessing whether a systems model and its evidence are sound. It includes a six-level validator and interpretation of diagnostic results.

Use it to choose checks, run validation, interpret diagnostics, and assess model evidence.

It helps identify problems in the model and assess evidence such as regression, source, numerical, and integration results.

How 1cFE/fusion-tea configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to 1cFE/fusion-tea. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/1cFE/fusion-tea/main/.agents/skills/model-validation/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/1cFE/fusion-tea

Made for: Claude Code.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/1cfe/fusion-tea/model-validation/github.svg)](https://agentmods.dev/skills/1cfe/fusion-tea/model-validation)

Your own site

<a href="https://agentmods.dev/skills/1cfe/fusion-tea/model-validation"><img src="https://agentmods.dev/badge/skills/1cfe/fusion-tea/model-validation/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/1cfe/fusion-tea/model-validation"><img src="https://agentmods.dev/badge/skills/1cfe/fusion-tea/model-validation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 30 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 898 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00030	$0.00898
Opus 5.5	$0.00012	$0.00359
Sonnet 5.5	$0.00006	$0.00180
Haiku 4.5	$0.00003	$0.00090

Measured 24d ago against content hash beb5a1088aa8, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

model-validation 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 24d 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.

.agents/skills/model-validation/SKILL.md · 65 lines

How it starts

The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Before executing this skill, read .agentic-mbse/claude.md in Claude Code or .agentic-mbse/codex.md in Codex. Resolve supporting paths from this skill’s installed directory; keep generated outputs in the project or a temporary directory. Read referenced skills from .agents/skills/<name>/SKILL.md when their guidance is needed.

Model Validation

Choose checks for the promised behavior and its actual risks. Validate early enough to catch useful failures, then validate the integrated outcome. The number of phases or model elements does not determine the strength of the evidence.

Tool Checks and Their Limits

Level	What the tool examines
1 — Syntax	Parser diagnostics
2 — Structural completeness	Definitions, inputs, and bindings
3 — Dependency integrity	Dependency cycles
4 — Constraint coverage	Authored constraints and executable coverage
5 — Documentation	Documentation presence
6 — Architecture/readiness	Supported patterns and downstream execution readiness

These checks do not establish source fidelity or sufficient engineering coverage. Interpret each diagnostic's severity and applicability; retain the actual exit status. L1–L3 errors normally block valid execution, and relevant L6 failures can block a required downstream route. A documented project-specific exception needs applicable evidence, not blanket dismissal of a level or an unchanged issue count.

Run the Relevant Checks

Follow the project's environment instructions and toolkit-awareness guidance:

uv run agentic-mbse validate models/             # fail-fast default
uv run agentic-mbse validate --complete models/  # report all six levels
uv run agentic-mbse validate --level=3 models/   # focused level
uv run pytest tests/models/ -v                  # model regressions, if present

Use the model scope and test selection appropriate to the change. During editing or a prototype, run the checks that answer the immediate question. Before completion, assess all applicable levels and integrated regressions. After repair, rerun the affected checks; broaden when new changes or failures justify it.

Read the full file on GitHub · 65 lines

Changes

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.

24d ago Changed · +4 lines beb5a1088aa8
1mo ago First seen · 61 lines · 30 tokens per session scan A b8f2691590ec

Subscribe to this mod's changes

model-validation is a skill published in the GitHub repository 1cFE/fusion-tea (5 stars, last pushed 24d ago), licensed MIT. It adds 30 tokens to every session and 898 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-29.

Scientific Testing

model-validation next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
model-validation1cFE/fusion-tea	Skill	5	24d ago	MIT	A	30	Claude Code
instrument-data-to-allotropeanthropics/knowledge-work-plugins	Skill	35k	2d ago	Apache-2.0	A	123	Claude Code
exploratory-data-analysisK-Dense-AI/scientific-agent-skills	Skill	56k	4d ago	MIT	A	83	Claude Code
matlabK-Dense-AI/scientific-agent-skills	Skill	56k	4d ago	MIT	A	44	Claude Code
phylogeneticsK-Dense-AI/scientific-agent-skills	Skill	56k	4d ago	MIT	A	71	Claude Code, Codex
research-engineerdavila7/claude-code-templates	Skill	33k	yesterday	MIT	A	43	Claude Code, Codex
mapping-to-snomedmaziyarpanahi/openmed	Skill	5.8k	yesterday	Apache-2.0	A	205	Claude Code

fusion-tea: Skill for Claude Code