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aris-grant-proposal

Draft a structured grant proposal from research ideas and literature. Supports KAKENHI (Japan), NSF (US), NSFC (China, including 面上/青年/优青/杰青/海外优青/重点), ERC (EU), DFG (Germany), SNSF (Switzerland), ARC (Australia), NWO (Netherlands), and generic formats. Use when user says "write grant", "grant proposal", "申請書", "write KAKENHI", "科研費", "基金申请", "写基金", "NSF proposal", or wants to turn research ideas into a funding application.

71

Quality

88%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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.

A highly actionable, clearly sequenced grant-writing workflow with strong checkpoints, state persistence, and feedback loops. Its main weakness is conciseness and progressive disclosure: the file is a long monolith with large reference-style tables and prompt templates kept inline rather than factored into bundled reference files.

Suggestions

Move the per-agency specification tables and grant-specific drafting guidelines into a bundled reference file (e.g. references/GRANT_TYPES.md) and link to it from the body, keeping SKILL.md as a lean overview — this would improve both conciseness and progressive_disclosure.

Extract the reusable Codex review prompt templates (Round 1 / Round 2+) into references/REVIEW_PROMPTS.md or a script, replacing the inline blocks with a short pointer, to cut significant inline tokens.

Tighten the duplicated pipeline/track diagrams (the Overview ASCII diagram, the Constants note, and the 'Composing with Other Skills' section restate the same branching pipeline) into a single canonical diagram to reduce redundancy.

DimensionReasoningScore

Conciseness

The body is densely informative and largely free of beginner-concept padding, but at ~600 lines it is long: the per-agency cultural-norms tables, the parallel Codex round-1/round-2 prompt blocks, and the duplicated pipeline diagrams could be tightened or pushed to a reference, so it is 'mostly efficient but could be tightened' rather than fully lean.

2 / 3

Actionability

It gives concrete executable guidance throughout: ready-to-paste Codex MCP prompts with threadId/config, exact sub-skill invocations (`/aris-research-lit "$ARGUMENTS"`), a fillable claims-aims-evidence matrix, file-output trees, and a copy-ready final-checklist — copy-paste ready rather than pseudocode.

3 / 3

Workflow Clarity

Five phases are explicitly sequenced (Phase 0→5) with 🚦/⛔ checkpoints, state persistence (GRANT_STATE.json with resume rules), feedback loops (review→CRITICAL/MAJOR fixes→re-review, MAX_REVIEW_ROUNDS), and a final verification checklist, matching the 'clear sequence with explicit validation steps and feedback loops' anchor.

3 / 3

Progressive Disclosure

There are no references/scripts/assets bundles and the skill is monolithic: large reference-worthy tables (per-agency specs, cultural norms, parameter pass-through) and the verbose review-prompt templates live inline rather than being split into one-level-deep reference files, fitting 'content that should be separate is inline' more than the well-signaled multi-file anchor.

2 / 3

Total

10

/

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, third-person description that concretely states capabilities, enumerates supported agencies, and supplies a rich set of natural-language triggers in multiple languages. It answers both 'what' and 'when' and occupies a clearly distinct niche.

DimensionReasoningScore

Specificity

Lists multiple concrete actions ("Draft a structured grant proposal from research ideas and literature") plus a long enumeration of specific supported agencies and sub-types (KAKENHI, NSF, NSFC with 面上/青年/优青/杰青/海外优青/重点, ERC, DFG, SNSF, ARC, NWO, generic), matching the 'lists multiple specific concrete actions' anchor.

3 / 3

Completeness

It explicitly states what the skill does ("Draft a structured grant proposal...") and when to use it ("Use when user says...") with explicit triggers, satisfying both halves of the highest anchor.

3 / 3

Trigger Term Quality

The "Use when user says" clause supplies natural multilingual trigger phrases users would actually say ("write grant", "grant proposal", "申請書", "write KAKENHI", "科研費", "基金申请", "写基金", "NSF proposal") plus a fallback intent, giving good coverage of natural terms.

3 / 3

Distinctiveness Conflict Risk

The grant-proposal niche is sharply defined by agency-specific triggers and a clear "turn research ideas into a funding application" intent, making it unlikely to fire for the wrong sibling skill in the ARIS pipeline.

3 / 3

Total

12

/

12

Passed

Validation

81%

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

Validation13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (625 lines); consider splitting into references/ and linking

Warning

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

frontmatter_unknown_keys

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

Warning

Total

13

/

16

Passed

Repository
OpenLAIR/dr-claw
Reviewed

Table of Contents

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