substrate-framework: Skill for Codex

.agents/skills/physics-discovery/SKILL.md

physics-discovery is a skill for Codex from vantasnerdan/substrate-framework. It costs 66 tokens per session (1,522 once invoked), scanned A, original, Apache-2.0.

A research guide for generating and checking new physics mechanisms and mathematical constructions. It uses changes of viewpoint and related structures to turn an open question into a testable idea.

In plain words
What is it for?
It is for discovering mechanisms, forming conjectures, comparing related ideas, and planning an initial calculation that can distinguish them.
Why use it?
It helps when a research problem needs a new possible explanation before anyone can choose or prove a solution.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: installed under .agents/ (shared by several agents); mentions AGENTS.md.

This is vantasnerdan/substrate-framework's own configuration. It tells Codex how to work on substrate-framework itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything substrate-framework configures →

Reuse

Borrowing it

Nothing to install: this file belongs to vantasnerdan/substrate-framework. 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/vantasnerdan/substrate-framework/main/.agents/skills/physics-discovery/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/vantasnerdan/substrate-framework

Made for: Codex.

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.

agentmods badge for physics-discovery

README.md
[![agentmods](https://agentmods.dev/badge/skills/vantasnerdan/substrate-framework/physics-discovery/github.svg)](https://agentmods.dev/skills/vantasnerdan/substrate-framework/physics-discovery)
Your own site
<a href="https://agentmods.dev/skills/vantasnerdan/substrate-framework/physics-discovery"><img src="https://agentmods.dev/badge/skills/vantasnerdan/substrate-framework/physics-discovery/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.

agentmods 80×15 button for physics-discovery

Your own site · 80×15
<a href="https://agentmods.dev/skills/vantasnerdan/substrate-framework/physics-discovery"><img src="https://agentmods.dev/badge/skills/vantasnerdan/substrate-framework/physics-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 66 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,522 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00066 $0.01522
Opus 5.5 $0.00026 $0.00609
Sonnet 5.5 $0.00013 $0.00304
Haiku 4.5 $0.00007 $0.00152

Measured 1mo ago against content hash 99fde110f146, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

physics-discovery 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 1mo 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/physics-discovery/SKILL.md · 125 lines

How it starts

The opening of the file, as written. The whole thing — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Physics Discovery

Earn a useful change in how the problem is seen, then turn it into a construction that can be tested. Apply AGENTS.md for authority, user scope, continuation and promotion; physics-erdos-loop owns claim execution. This skill supplies ideas and their first informative checks. Its methods are a menu, not a quota or a new acceptance checklist.

Focus the question

Use the existing proposal/research contract as the home for a compact discovery brief. Refresh it when the question or explanatory obstacle changes:

  • The physical phenomenon or exact theorem sought, and why its answer matters.
  • Sourced equations, domains, symmetries and known constraints. Separate the user's invariants, accepted premises and optional ansatz/representation choices.
  • The unresolved tension: which two apparently reasonable requirements resist coexistence, or which implication is still missing?
  • A useful nearby construction and, when available, a structural analogue from another area; identify precisely what transfers and what remains unmatched.
  • The next calculation that could distinguish mechanisms or reveal structure.

Keep mandatory instructions and accepted authority available. Focus the working brief on this problem; consult specialized verification and delivery details when those decisions arise. Source facts remain traceable, earlier exposure is declared, and the parent campaign retains its complete durable record.

Retrieve relationships and transform the problem

Search by mathematical structure as well as physical names: a constraint and its generator, a local-to-global obstruction, a soft direction coupled to a stiff core, a conserved quantity under reduction. Reuse and new generation can proceed together; a nearest accepted example supplies ingredients, not a prescribed form. Verify a retrieved construction at source before relying on it. Distinguish a recalled analogy worth investigating from an established imported result.

Choose transformations that address the actual obstacle:

Read the full file on GitHub · 125 lines

Files

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.

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.

  1. 1mo ago First seen · 125 lines · 66 tokens per session scan A 99fde110f146

Subscribe to this mod's changes

physics-discovery is a skill published in the GitHub repository vantasnerdan/substrate-framework (5 stars, last pushed 11d ago), licensed Apache-2.0. It adds 66 tokens to every session and 1,522 once invoked, about $0.0003 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 2026-09-10.

Related

Other skills, from other repositories

instrument-data-to-allotrope

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…

anthropics/knowledge-work-plugins · 123 tokens

matlab

Builds, reviews, migrates, and plans MATLAB or GNU Octave numerical workflows. Use for arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.

K-Dense-AI/scientific-agent-skills · 44 tokens

exploratory-data-analysis

Performs bounded, local exploratory analysis of explicitly supported scientific files. Supports 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…

K-Dense-AI/scientific-agent-skills · 83 tokens

phylogenetics

Builds and analyzes phylogenetic trees using MAFFT multiple sequence alignment, IQ-TREE maximum likelihood with ModelFinder and branch support, and FastTree approximate inference. Uses ETE3 for tree summaries and visualization. Applies to homologous nucleotide or protein sequences, microbial gene trees, protein…

K-Dense-AI/scientific-agent-skills · 71 tokens

research-engineer

An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.

davila7/claude-code-templates · 43 tokens

presenting-conference-talks

Generates conference presentation slides (Beamer LaTeX PDF and editable PPTX) from a compiled paper with speaker notes and talk script. Use when preparing oral talks, spotlight presentations, or invited talks for ML and systems conferences.

Orchestra-Research/AI-Research-SKILLs · 53 tokens