hamiltonian-mechanics

Hamiltonian mechanics — symplectic integrators (leapfrog, Yoshida), Hamilton's equations, Poisson brackets, canonical transformations, action-angle variables, and KAM theory analysis. Use for energy-conserving long-time integration of conservative systems.

Quality

82%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

Quality

Content

65%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A highly actionable single-file skill with strong runnable examples and useful reference tables, but it is verbose in places and keeps all content inline rather than progressively disclosing detail via reference files.

Suggestions

Trim the matplotlib axis/label/legend boilerplate in the Kepler example to a minimal plot call, or move plotting into a short separate snippet, to tighten conciseness.

Add an explicit validate-fix-retry checkpoint pattern (e.g., check energy drift, adjust dt if above tolerance, re-run) and apply it consistently across the core workflows.

Split the integrator comparison table, troubleshooting table, and advanced examples (Yoshida, Poincare) into a reference file linked from a concise overview to improve progressive disclosure.

Dimension	Reasoning	Score
Conciseness	Mostly efficient executable code, but the matplotlib plotting boilerplate in the Kepler example (~20 lines of axis/label setup) and the 'Why Symplectic Matters' rationale pad the body beyond what earns its place.	2 / 3
Actionability	Fully executable, copy-paste-ready implementations (leapfrog, Yoshida4, N-body figure-8, Poincare section) with concrete runnable examples and a comparison table.	3 / 3
Workflow Clarity	Numbered core workflows give a clear sequence, but validation is shown only in example 1 (energy-conservation check); the other workflows lack explicit validate-fix-retry checkpoints.	2 / 3
Progressive Disclosure	Sections are well organized, but the ~250-line body is monolithic with all detail inline and no reference files to split out the comparison table, troubleshooting, or advanced examples.	2 / 3
	Total	9 / 12 Passed

Description

100%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, specific description that states concrete capabilities and an explicit 'Use for' trigger in third person. It clearly distinguishes the skill and is unlikely to conflict with others.

Dimension	Reasoning	Score
Specificity	Names many concrete capabilities — 'symplectic integrators (leapfrog, Yoshida)', 'Hamilton's equations', 'Poisson brackets', 'canonical transformations', 'action-angle variables', 'KAM theory analysis' — matching the multiple-specific-actions anchor.	3 / 3
Completeness	Explicitly answers both what (the listed analysis/integration capabilities) and when via the 'Use for energy-conserving long-time integration of conservative systems' trigger clause.	3 / 3
Trigger Term Quality	Uses natural domain terms a physics user would actually say — 'Hamiltonian mechanics', 'symplectic integrators', 'leapfrog', 'Yoshida', 'energy-conserving', 'conservative systems' — with good coverage of common variations.	3 / 3
Distinctiveness Conflict Risk	Occupies a clear niche (Hamiltonian/symplectic integration) with distinct triggers unlikely to fire for a general ODE solver or unrelated skill.	3 / 3
	Total	12 / 12 Passed

Validation

93%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: synthetic-sciences/openscience
Path: backend/cli/skills/physics/hamiltonian-mechanics/SKILL.md
Commit: 52845c3

Reviewed: about 9 hours ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.