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inno-grant-proposal

Help professors and researchers write, revise, adapt, and polish grant proposals for US agencies (NSF, NIH, DOE, DARPA, NASA), Chinese agencies (NSFC 国自然), and other national agencies (JSPS KAKENHI 科研費, ERC, DFG, SNSF, ARC, NWO). Use this skill whenever the user mentions grants, proposals, funding applications, 基金申请, 本子, R01, R21, CAREER, 面上, 青年基金, specific aims, 立项依据, broader impacts, or wants to plan, draft, review, or resubmit any research funding proposal — even if they don't explicitly say "grant". Also use this skill when the user wants to adapt a previous proposal for a new submission. Six-phase workflow: profiling → planning → drafting → quality review → simulated peer review → submission prep.

Quality

87%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

73%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 an unusually well-engineered multi-phase workflow with explicit entry/exit criteria, layered validation with feedback loops, and rich agency-specific operational detail. Its main weaknesses are avoidable redundancy that inflates token cost, and phantom references to a templates/ directory, config.yaml, and CREDITS.md that do not exist in the bundle, which undercut both actionability and navigation.

Suggestions

Ship the referenced bundle files or remove the references: templates/us/ (nih_specific_aims.md, nsf_project_summary.md, budget_justification.md), templates/cn/ (nsfc_justification.md, nsfc_research_content.md, nsfc_research_foundation.md, nsfc_abstract_5sentence.md), config.yaml, and CREDITS.md are all cited in the body but missing from the bundle — every 'load the template' instruction currently dead-ends.

Deduplicate repeated guidance to improve conciseness: state the backup protocol once (Safety Rules) instead of restating it in Phase 2's Auto-Backup section, explain the S1-S4 model once and reference it thereafter, and consolidate the per-phase reference-loading lists into the single Lazy Loading policy.

Fix the internal inconsistency between the 'AI-Flavor Detection (16-Item Checklist)' heading in Phase 3 (and 'Re-run the 16-item AI-flavor checklist' in Step 5.1) and the correct 24-item checklist (1-16 English, 17-24 Chinese) used in Phase 4 and defined in references/common/ai_flavor_checklist.md.

DimensionReasoningScore

Conciseness

Most content is skill-specific operational knowledge (NSFC golden-ratio page budgets, the four-paragraph closure model, ROI scoring table, agency review criteria) that Claude could not reconstruct, but there is real redundancy: backup rules appear in both "Safety Rules" and "Auto-Backup & Checkpoints", the S1-S4 planning model is fully explained twice (Phase 1 Step 1.3 and the Phase 2 planning protocol), lazy-loading instructions are repeated per phase, and the full GRANT_STATE.json schema is inlined. This fits anchor 3 ("mostly efficient but includes some unnecessary explanation or could be tightened") better than anchor 4, where over-explanation would be only minor.

3 / 5

Actionability

Guidance is highly concrete for an instruction-only skill: executable script invocations ("python3 scripts/validate_length.py <proposal_dir> --mode cn|us --json"), sentence-level templates ("The long-term goal of [PI] is... The objective of this application is..."), quantified thresholds (30-50 references, 400-character abstract, 5% golden-ratio deviation), a full state schema, and a P0/P1/P2 severity taxonomy. It falls short of anchor 5 because Phase 2's core instruction "Load the appropriate template from templates/" cannot be executed — the templates/ directory and config.yaml do not exist in the bundle, so a meaningful fraction of the concrete guidance dead-ends (though a documented fallback to built-in knowledge partially mitigates this).

4 / 5

Workflow Clarity

The six phases each have explicit Entry Criteria, numbered workflow steps, and Exit Criteria, plus a command table for phase jumping, an error-handling section, and partial-use rules. Validation is layered exactly as anchor 5 requires: deterministic script checks with a manual fallback feedback loop ("If scripts are not available or fail, perform these checks manually"), Tier 2 semantic checks, simulated review, and a final compliance checklist; destructive operations are guarded by auto-backup, copy-before-write, and confirmation rules.

5 / 5

Progressive Disclosure

Structure is strong: a dedicated "Reference Files" section enumerates the bundle, references are one level deep and clearly signaled per phase, and an explicit lazy-loading policy ("Do NOT read all reference files at once... reduces token usage by ~60%") is provided; all references/ and scripts/ paths verified to exist. It stays at anchor 4 rather than 5 because the body references files that are absent from the bundle — templates/us/*, templates/cn/* (7 files), config.yaml, and CREDITS.md — which breaks navigation for the drafting and final-formatting phases, a gap beyond the 'minor organization gaps' that anchor 5 tolerates.

4 / 5

Total

16

/

20

Passed

Description

100%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.

This is a model description: it states concrete capabilities in third person, names the target audience and every supported agency, enumerates a six-phase workflow, and provides an explicit, bilingual 'Use this skill when...' trigger clause with natural keywords including colloquial terms. Coverage of both what and when is comprehensive with no padding or over-claims.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions spanning the full lifecycle — "write, revise, adapt, and polish grant proposals" plus "plan, draft, review, or resubmit" — and grounds them in a concrete enumerated workflow ("Six-phase workflow: profiling → planning → drafting → quality review → simulated peer review → submission prep"). This matches the anchor-5 pattern of multiple specific actions with comprehensive coverage; anchor 4 would require a noticeable coverage gap, and none exists across the writing/revision/review/submission lifecycle.

5 / 5

Completeness

Both 'what' (write/revise/adapt/polish proposals for named agencies NSF, NIH, DOE, DARPA, NASA, NSFC, JSPS, ERC, DFG, SNSF, ARC, NWO, via a six-phase workflow) and 'when' ("Use this skill whenever the user mentions grants, proposals, funding applications...") are explicitly and concretely answered with trigger phrases. This mirrors the anchor-5 example structure exactly; the explicit 'Use when...' clause rules out the cap at 3.

5 / 5

Trigger Term Quality

Trigger coverage is comprehensive in both English and Chinese, including natural synonyms and slang users actually say: "grants, proposals, funding applications, 基金申请, 本子, R01, R21, CAREER, 面上, 青年基金, specific aims, 立项依据, broader impacts". It even covers indirect triggers ("even if they don't explicitly say 'grant'") and the adapt-from-previous-proposal case, which exceeds the anchor-5 bar of comprehensive synonyms.

5 / 5

Distinctiveness Conflict Risk

The niche is unambiguous — research funding proposals for specific named agencies and programs (R01, CAREER, 面上, 国自然) — with triggers (基金申请, 本子, specific aims, 立项依据) that no adjacent skill (paper writing, general editing) would claim. Clear niche with distinct triggers and minimal conflict risk, matching anchor 5.

5 / 5

Total

20

/

20

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.

Validation — 13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (943 lines); consider splitting into references/ and linking

Warning

frontmatter_unknown_keys

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

Warning

referenced_paths_exist

Referenced path issues: 22 deeper-than-1-level

Warning

Total

13

/

16

Passed

Repository

OpenLAIR/dr-claw

Path

skills/inno-grant-proposal/SKILL.md

Reviewed

Table of Contents

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