funding-trend-forecaster

Analyze funding abstracts and project metadata to identify topic shifts and forecast near-term grant priorities.

Quality

57%

Does it follow best practices?

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Securityby

Passed

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Fix and improve this skill with Tessl

tessl review fix ./scientific-skills/Evidence Insight/funding-trend-forecaster/SKILL.md

Quality

Content

56%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 content is actionable with concrete commands and a validated workflow, but it is padded by a redundant boilerplate wrapper and an inaccurate architecture tree, and it inlines material that should live in reference files.

Suggestions

Collapse the duplicate wrapper sections (When to Use / Key Features / Implementation Details / Workflow / Output Requirements) into the original detailed sections to remove the parallel restatement.

Remove or correct the Architecture tree — the bundle only ships scripts/main.py, so the listed collectors/, analyzers/, and predictors/ modules are misleading.

Move the full Configuration and Output Format JSON blocks into references/ (e.g. a config-reference.md) and keep only a compact example in SKILL.md, improving both conciseness and progressive disclosure.

Dimension	Reasoning	Score
Conciseness	The body is noticeably verbose: it carries two parallel structures (a boilerplate wrapper — When to Use, Key Features, Implementation Details, Workflow, Output Requirements — plus the original Overview/Features/Usage/Parameters sections) that restate the same scope, plus an Architecture tree listing files (collectors/, analyzers/, predictors/) that do not exist in the actual bundle — fitting the "noticeably verbose" anchor rather than the incidental 3.	2 / 5
Actionability	Provides concrete executable commands (`python -m py_compile scripts/main.py`, `python scripts/main.py --source nih --months 3`), a full parameters table, a config example, and an API usage snippet — mostly executable with only minor gaps, short of the fully copy-paste-ready 5.	4 / 5
Workflow Clarity	The 5-step Workflow has a scope-validation checkpoint ("Validate that the request matches the documented scope and stop early") and an explicit fallback path on failure; Quick Check supplies a py_compile gate, giving most checkpoints with minor gaps versus the explicit validate-fix-retry 5.	4 / 5
Progressive Disclosure	The reference (references/audit-reference.md) exists and is signaled in a References section, but large inlined blocks (Configuration JSON, Output Format JSON, Architecture tree, Parameters) that belong in separate files keep it at the "some structure, content that should be separate is inline" anchor.	3 / 5
	Total	13 / 20 Passed

Description

58%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.

The description is specific and distinct, clearly stating what the skill does, but it lacks an explicit "Use when…" trigger clause, which caps completeness and leaves trigger-term coverage short of natural user phrasing.

Suggestions

Add a "Use when…" clause naming concrete trigger phrases (e.g. "Use when forecasting grant trends, analyzing NIH/NSF funding abstracts, or identifying research funding shifts").

Include natural synonyms users actually say — "funding trends", "grant trends", "research funding priorities" — alongside the current technical terms.

Mention the supported agencies (NIH, NSF, Horizon Europe) in the description to sharpen distinctiveness and reduce overlap with generic analysis skills.

Dimension	Reasoning	Score
Specificity	Lists several concrete actions — "Analyze funding abstracts and project metadata", "identify topic shifts", "forecast near-term grant priorities" — with only minor coverage gaps, matching the "several specific actions" anchor rather than the comprehensive 5.	4 / 5
Completeness	The description gives a clear "what" but has no "Use when…" clause or explicit trigger guidance, so per the judging guideline completeness is capped at 3.	3 / 5
Trigger Term Quality	Phrases like "funding abstracts", "grant priorities", and "topic shifts" are relevant but omit common natural variations users would say (e.g. "funding trends", "grant trends", "research funding"), fitting the "some relevant keywords but missing variations" anchor.	3 / 5
Distinctiveness Conflict Risk	The research-funding niche (abstracts, grant priorities, topic shifts) is mostly distinct with only minor overlap risk against general data-analysis skills, sitting above the 3 anchor but below the clearly-niche 5.	4 / 5
	Total	14 / 20 Passed

Validation

93%

Warnings & errors only

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

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: aipoch/medical-research-skills
Path: scientific-skills/Evidence Insight/funding-trend-forecaster/SKILL.md
Commit: 63c61d3

Reviewed: 8 hours ago

Table of Contents

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