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research-paper-writing

Write ML papers for NeurIPS/ICML/ICLR: design→submit.

53

Quality

63%

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tessl review fix ./skills/research/research-paper-writing/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

77%

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

The body is highly actionable and exceptionally well-structured with strong validation checkpoints and feedback loops, but its ~2300-line length, repeated examples, and large inline LaTeX-tooling sections cost it conciseness and some progressive-disclosure credit.

Suggestions

Move the professional LaTeX preamble, TikZ diagram patterns, siunitx/subfigure, and SciencePlots material into a dedicated references/latex-tooling.md file and link to it from the main body to reduce inline bulk.

Deduplicate the booktabs table example and the figure/caption rules, which currently appear in both Phase 4.4 and Phase 5, to tighten conciseness.

Trim general writing heuristics (title/abstract advice, Gopen & Swan principles already in writing-guide.md) in the body, keeping only the pointer to the reference.

DimensionReasoningScore

Conciseness

At ~2300 lines the body is concrete and domain-specific, but it repeats material (the booktabs table example appears in both Phase 4.4 and Phase 5; figure rules are restated) and includes guidance Claude largely already knows (title/abstract heuristics, basic LaTeX), fitting the level-2 anchor of mostly efficient with some unnecessary or tighten-able content.

2 / 3

Actionability

It provides extensive copy-paste-ready, executable artifacts — the cost tracker, doi_to_bibtex, result aggregation, full LaTeX preamble, TikZ patterns, and chktex/validation scripts — matching the level-3 anchor of fully executable code and specific examples.

3 / 3

Workflow Clarity

The phased 0–8 structure with numbered steps, explicit validation checkpoints (citation 5-step process, pre-compilation validation, "only when valid" compilation gating) and feedback loops matches the level-3 anchor of a clear sequence with explicit validation and error-recovery loops.

3 / 3

Progressive Disclosure

Nine reference files are clearly signaled with one-level-deep markdown links and a summary table, but large inline blocks (the professional LaTeX preamble, TikZ diagram patterns, SciencePlots, siunitx/subfigure sections) could plausibly live in their own reference file, fitting the level-2 anchor where some content that should be separate is inline.

2 / 3

Total

10

/

12

Passed

Description

50%

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

The description is concise and names the right domain and venues, but it is too terse to convey the full pipeline's scope and lacks an explicit "Use when…" trigger. It sits at level 2 across all dimensions.

Suggestions

Add an explicit trigger clause, e.g. "Use when writing or revising an ML/AI research paper for NeurIPS, ICML, ICLR, ACL, AAAI, or COLM — from experiment design through submission and rebuttal."

Expand the action list to reflect the full pipeline: "design and run experiments, analyze results, draft and revise the paper, prepare camera-ready and rebuttal materials".

Differentiate from the superseded ml-paper-writing skill by noting this covers the full experiment→analysis→writing lifecycle, not just writing.

DimensionReasoningScore

Specificity

The phrase "Write ML papers for NeurIPS/ICML/ICLR: design→submit" names the domain and a few actions (write, design, submit) but does not enumerate the pipeline's many concrete capabilities (experiments, analysis, rebuttals, formatting), matching the level-2 anchor that names a domain and some actions but is not comprehensive.

2 / 3

Completeness

It states what the skill does ("Write ML papers") and implies the when via venue names, but there is no explicit "Use when…" trigger clause; per the judging guidelines a missing explicit trigger caps completeness at 2.

2 / 3

Trigger Term Quality

"ML papers" and the venue names "NeurIPS/ICML/ICLR" are natural terms a user might say, but common variations users actually use ("research paper", "experiments", "submission", "rebuttal", "camera-ready") are absent, fitting the level-2 anchor of some relevant keywords missing common variations.

2 / 3

Distinctiveness Conflict Risk

The specific venue list gives the skill a niche, but the description does not distinguish it from the closely related ml-paper-writing skill it claims to supersede, so it could still overlap with similar skills — the level-2 anchor.

2 / 3

Total

8

/

12

Passed

Validation

62%

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

Validation10 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (2378 lines); consider splitting into references/ and linking

Warning

metadata_version

'metadata.version' is missing

Warning

metadata_field

'metadata' should map string keys to string values

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

relative_links

Relative link issues: 2 missing

Warning

referenced_paths_exist

Referenced path issues: 2 missing

Warning

Total

10

/

16

Passed

Repository
NousResearch/hermes-agent
Reviewed

Table of Contents

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