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pde-solver

Solve partial differential equations — finite differences, spectral methods, and physics-informed neural networks (PINNs via DeepXDE). Supports 1D/2D/3D, steady/transient, linear/nonlinear PDEs with Dirichlet, Neumann, and periodic boundary conditions.

60

Quality

76%

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tessl review fix ./backend/cli/skills/physics/pde-solver/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

77%Weight 40%Scale 1-5

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

The body is a strong, executable reference: five complete working solvers, a method-selection matrix, a stability table, an explicit validation checklist, and symptom/cause/fix troubleshooting with built-in error checkpoints. Weaknesses are the one bloated, off-topic prose paragraph in the Validation Checklist section and the absence of any reference-file split for the sizable inline method guides.

Suggestions

Replace the ~18-line 'When the real problem data ... supplied only at evaluation time ... hidden points' paragraph with two or three tight checklist items (e.g., 'Choose resolution at run time: solve at 2-3 refinements within the time budget and stop when the change is well below tolerance'; 'Manufactured tests must include content outside your own basis — high wavenumbers, sharp fronts — and be checked against withheld real data'), which is the main conciseness drag.

Move the DeepXDE/PINN workflow and the stability/troubleshooting reference tables into one-level-deep files (e.g., references/pinn.md, references/stability.md) with clearly signaled pointers from SKILL.md, keeping the core finite-difference and MOL workflows inline.

Trim the plotting boilerplate repeated in each workflow (figure creation, savefig) to a single shared snippet so the code examples focus on the numerics.

DimensionReasoningScore

Conciseness

Most of the body is lean — method-selection table, self-contained executable functions, compact stability and troubleshooting tables — but the Validation Checklist section carries an ~18-line dense prose paragraph ('When the real problem data ... are supplied only at evaluation time and a tolerance must be met on hidden points ...') about hidden evaluation-time oracles that is off-topic for a general PDE-solver skill and could be cut to two sentences. This matches the 3 anchor (mostly efficient, some unnecessary explanation) rather than 4, where only minor trimming would be needed.

3 / 5

Actionability

Every workflow ships complete, copy-paste-ready Python: 'solve_heat_1d' with a runtime 'CFL stability check' that raises on violation, the sparse Kronecker Poisson solver, the pseudospectral wave solver, a working DeepXDE PINN with install command, and a MOL/solve_ivp variant. The 5 anchor (fully executable, covers common cases) fits; nothing is pseudocode and key numerical details (grid construction, BC enforcement, validation against the analytical solution) are all explicit.

5 / 5

Workflow Clarity

The skill sequences decisions and validation well: a method-selection table routes by PDE type/geometry, each solver embeds a checkpoint (the CFL check raising 'ValueError(f"CFL violated: r = {r:.4f} > 0.5...")'), a six-item post-solve Validation Checklist ('Verify against analytical solution', 'Run mesh convergence study', 'Check boundary conditions'), and a Troubleshooting table pairs symptoms with causes and fixes — an explicit feedback loop. This matches the 5 anchor (explicit validation steps, error-recovery feedback loops, checklist) rather than 4, where checkpoints would only be 'mostly' present.

5 / 5

Progressive Disclosure

No bundle files exist (no references/, scripts/, or assets/), so everything lives inline in a single ~340-line SKILL.md. Sections are well-headed and navigable, but self-contained full workflows — especially the complete DeepXDE/PINN example and the stability reference table — are content that would plausibly belong in one-level-deep reference files, matching the 3 anchor (some structure, content that should be separate is inline) rather than 4 (most content appropriately placed across files).

3 / 5

Total

16

/

20

Passed

Description

66%Weight 40%Scale 1-5

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, information-dense description that names concrete methods, supported problem classes, and boundary conditions with no fluff. Its main deficiency is the complete absence of any 'Use when...' trigger guidance, which both caps completeness and leaves the natural trigger surface thinner than the description's otherwise specific vocabulary would allow.

Suggestions

Append a 'Use when...' clause (e.g., 'Use when solving PDEs like the heat, wave, Laplace/Poisson, or Navier-Stokes equations, or when the user mentions finite differences, spectral methods, or PINNs') to convert the implicit trigger surface into explicit guidance and lift completeness.

Add the widely used shorthand 'PDE' and common user phrasings ('heat equation', 'wave equation', 'Laplace equation') alongside the formal 'partial differential equations' to widen natural keyword coverage.

State the ODE boundary explicitly (e.g., 'for ODE systems use ode-solver') to reduce overlap risk with a competing ODE skill on ambiguous 'solve this equation' requests.

DimensionReasoningScore

Specificity

The description enumerates concrete capabilities — 'finite differences, spectral methods, and physics-informed neural networks (PINNs via DeepXDE)' plus '1D/2D/3D, steady/transient, linear/nonlinear PDEs with Dirichlet, Neumann, and periodic boundary conditions' — which is several specific capabilities with only minor gaps (e.g., no named example equations). It falls short of the 5 anchor because everything hangs off the single verb 'Solve' rather than listing multiple distinct concrete actions, and exceeds the 3 anchor which expects only 1-2 concrete actions.

4 / 5

Completeness

The 'what' is clear and concrete (methods, dimensions, regimes, boundary conditions), but there is no 'Use when...' clause or equivalent explicit trigger guidance anywhere in the description, which caps completeness at 3 per the judging guidelines. It is above the 2 anchor because the 'what' is specific rather than vague, and below the 4 anchor because 'when' is entirely absent rather than weakly implied.

3 / 5

Trigger Term Quality

Good natural keyword coverage: 'partial differential equations', 'finite differences', 'spectral methods', 'PINNs', 'DeepXDE', and boundary-condition names users would actually say. A few natural terms are missing — the common shorthand 'PDE' itself and frequent user phrasings like 'heat equation', 'wave equation', or 'Laplace equation' — so it fits the 4 anchor rather than the 5 anchor's comprehensive synonym coverage.

4 / 5

Distinctiveness Conflict Risk

The PDE-solving niche is well-delineated by distinct triggers ('finite differences', 'spectral methods', 'PINNs via DeepXDE'), making confusion with unrelated skills unlikely. It is not a 5 because a bare request like 'solve this differential equation' could plausibly pull in a competing ODE-solver skill given no explicit ODE/PDE disambiguation in the description itself.

4 / 5

Total

15

/

20

Passed

Validation

93%

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

CriteriaDescriptionResult

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/pde-solver/SKILL.md

Reviewed

Table of Contents

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