CtrlK
BlogDocsLog inGet started
Tessl Logo

mace-screening-and-relaxation

Use this skill for MACE-based rapid screening and relaxation loops before DFT, including candidate pruning and handoff criteria.

61

Quality

72%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./skills/mace-screening-and-relaxation/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

71%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.

A tight, well-structured instruction-only skill whose strengths are token efficiency, clear sequencing, and genuinely non-obvious method guidance (the dispersion-consistency defaults and the use-evidence-not-launch-success checkpoint). Its main gap is actionability: it names tools and parameters but never shows an example invocation or a concrete keep/drop rule for the VASP handoff it requires as output.

Suggestions

Add one example invocation for each tool (argument names and typical values for model, dispersion, relax_lattice, input_dir, output_root) so the guidance is executable rather than only descriptive.

Define a concrete keep/drop rule or shortlist criterion (e.g. an energy-ranking threshold from batch_summary_rel, or 'relax top-N by mace_sp_batch energy') instead of leaving the rule entirely to the reader.

Specify what to check in batch_state_rel/status files to decide rerun vs. accept partial outputs (e.g. which status values mean dispatch failure vs. per-structure failure), closing the workflow's validation loop.

DimensionReasoningScore

Conciseness

The 47-line body is lean, assumes domain competence, and wastes no tokens explaining known concepts — "Do not compare relax and SP outputs as if they were the same screening stage" is exactly the kind of non-obvious guidance the rubric rewards. It falls short of 5 due to minor redundancy: Quick Start steps 2-3 restate Workflow sections 1-2, and the tool list appears in both frontmatter metadata and a separate 'Suggested tools' section.

4 / 5

Actionability

Concrete guidance exists — named tools ("mace_relax_batch", "mace_sp_batch"), named parameters ("model", "head", "dispersion", "relax_lattice"), explicit constraints ("Keep output_root outside input_dir"), and named return fields ("batch_state_rel", "batch_summary_rel") — but there are no executable invocations: no example tool call, no argument shapes, and no concrete keep/drop rule (the Output Contract only requires returning one without defining what a good rule is). This matches anchor 3 ('some concrete guidance but incomplete; missing key details') better than anchor 4, which expects concrete code or commands with only minor gaps.

3 / 5

Workflow Clarity

The Quick Start gives a coherent 4-step sequence, and Workflow section 3 supplies genuine validation checkpoints for the batch operation ("Use collected evidence, not launch success alone"; "inspect those before deciding to rerun"), so the batch-operations cap does not apply. It is not 5 because the keep/drop decision — the workflow's terminal step — is left implicit, and there is no validate-then-retry loop spelling out when a rerun is warranted versus a partial-output salvage.

4 / 5

Progressive Disclosure

There are no bundle files (no references/, scripts/, or assets/ directories), and the body is under 50 lines with no content that needs offloading, so per the rubric's simple-skill guidance well-organized sections alone warrant a 5. Sections (Overview, Quick Start, Suggested tools, Workflow, Method-critical defaults, Output Contract, References) are clearly headed, one level deep, and easy to navigate; the 'References' section points to a sibling skill (vasp-input-preparation) as a handoff boundary, not a nested file.

5 / 5

Total

16

/

20

Passed

Description

73%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 concise, distinctive description that clearly states its niche (MACE screening/relaxation before DFT) with an explicit trigger clause. Main weaknesses are the second-person imperative voice (triggering the specificity penalty) and trigger phrasing that leans on a single 'before DFT' condition rather than the natural phrases a user would say.

Suggestions

Rewrite in third-person voice, e.g. 'Runs MACE-based rapid screening and relaxation loops on structure batches before DFT, with candidate pruning and VASP handoff criteria. Use when the user asks to pre-screen or pre-relax candidates before VASP/DFT, or to prune a structure pool before spending DFT resources.'

Add natural trigger synonyms users are likely to say — 'VASP', 'cheap relaxation', 'machine-learned potential', 'pre-screen', 'structure batch' — to improve trigger-term coverage from good to comprehensive.

State 1-2 more concrete capabilities (e.g. geometry cleanup via mace_relax_batch, static energy ranking via mace_sp_batch) so the 'what' matches the body's actual tool actions.

DimensionReasoningScore

Specificity

"MACE-based rapid screening and relaxation loops before DFT, including candidate pruning and handoff criteria" names the domain and several actions (screening, relaxation, pruning, handoff), which would fit anchor 4, but it opens with the second-person imperative "Use this skill for..." rather than third-person voice, so per the rubric's voice penalty the score is reduced by 1. It is not anchor 2 because the actions go beyond minimal/generic domain naming.

3 / 5

Completeness

Both parts are present: what ("MACE-based rapid screening and relaxation loops... candidate pruning and handoff criteria") and when ("before DFT", framed by the explicit trigger "Use this skill for..."). It is not 5 because the 'when' is a single timing condition rather than concrete trigger phrases (e.g. 'when the user asks to pre-screen candidates before VASP'); it is not 3 because the trigger guidance is explicit, not merely implied.

4 / 5

Trigger Term Quality

Natural terms a computational-materials user would say are present — "MACE", "screening", "relaxation", "DFT", "candidate pruning" — giving good keyword coverage. It is not 5 because common synonyms and adjacent phrasings users might use (e.g. "VASP", "machine-learned potential", "pre-relax", "cheap relaxation") are missing.

4 / 5

Distinctiveness Conflict Risk

"MACE-based rapid screening and relaxation loops before DFT" carves out a clear niche with distinct triggers (MACE, pre-DFT screening, candidate pruning); a user naming these terms is almost certainly asking for this skill. Only trivial overlap risk with a general DFT-orchestration skill keeps it from being ambiguous, and it matches the anchor-5 example's clarity.

5 / 5

Total

16

/

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

metadata_version

'metadata.version' is missing

Warning

Total

15

/

16

Passed

Repository
fernandezbaptiste/CatMaster
Reviewed

Table of Contents

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.