feasibility-aware-study-planner

Designs a realistic, execution-aware biomedical study version under explicit constraints of samples, time, budget, data access, lab capacity, team skill, and validation resources. Always use this skill when the user has a real study idea, a candidate route, or a partially framed project but cannot assume ideal conditions. If critical feasibility inputs are missing, first clarify what resources are currently available, what resources may be obtainable, and what resources are realistically unavailable. Do not invent access, capabilities, collaborations, or validation resources. Focus first on feasibility-constrained study framing, route narrowing, dependency control, and minimum viable study design.

Quality

78%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./awesome-med-research-skills/Protocol Design/feasibility-aware-study-planner/SKILL.md

Quality

Content

63%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 skill is well-structured with clear workflows, strong progressive disclosure, and actionable framing guidance, but it is notably verbose with repeated restatements of the same constraints across multiple sections.

Suggestions

Consolidate the three-bucket resource distinction (available / obtainable / unavailable) into a single canonical location and reference it rather than repeating it in the intro, Input Validation, Step 2, Section C, and Hard Rules.

Trim or merge the "What This Skill Should Not Do", "Quality Standard", and "Hard Rules" sections, which substantially restate constraints already covered in Core Function and Execution steps.

Add an explicit feedback/retry loop for the clarification step (e.g., ask → if still missing → mark provisional) to push workflow clarity from clear checkpoints to a full validation cycle.

Dimension	Reasoning	Score
Conciseness	The body restates the same three-bucket resource distinction (available / obtainable / unavailable) at least four times across the intro, Input Validation, Step 2, Section C, and Hard Rules, and the "What This Skill Should Not Do", "Quality Standard", and "Hard Rules" sections heavily restate earlier constraints, adding notable padding beyond what Claude needs.	2 / 5
Actionability	For an instruction-only skill it gives concrete, specific guidance — exact constraint types, route families, design-adjustment options, and a fixed A–K output structure — with only minor gaps in executable mechanics.	4 / 5
Workflow Clarity	It provides a clear 8-step sequence with explicit validation checkpoints (label output "provisional and assumption-dependent" when inputs are missing; treat output as "incomplete" without both resource boundary and constrained version), though there is no explicit retry/feedback loop.	4 / 5
Progressive Disclosure	The body is a well-organized overview that signals 11 real one-level-deep reference modules, each mapped to a specific output section with "use when..." guidance, and every referenced path exists in references/.	5 / 5
	Total	15 / 20 Passed

Description

92%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, complete, and clearly scoped to a distinct niche with explicit "what" and "when" clauses. Its only weakness is slightly academic trigger phrasing that misses a few plain user terms.

Dimension	Reasoning	Score
Specificity	The description enumerates many concrete constraint dimensions ("samples, time, budget, data access, lab capacity, team skill, and validation resources") and concrete actions (route narrowing, dependency control, minimum viable study design), giving comprehensive coverage rather than vague language.	5 / 5
Completeness	It explicitly answers both "what" ("Designs a realistic, execution-aware biomedical study version under explicit constraints...") and "when" ("Always use this skill when the user has a real study idea, a candidate route, or a partially framed project...") with concrete trigger phrases.	5 / 5
Trigger Term Quality	It includes natural triggers like "real study idea", "candidate route", and "partially framed project", but the phrasing is somewhat academic and omits plain phrases users would actually say (e.g. "design a study", "scale down", "feasibility"), leaving a few natural terms missing.	4 / 5
Distinctiveness Conflict Risk	It carves a clear niche (feasibility-constrained biomedical study planning) with distinct triggers and minimal overlap risk against other skills.	5 / 5
	Total	19 / 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: awesome-med-research-skills/Protocol Design/feasibility-aware-study-planner/SKILL.md
Commit: f5ef65b

Reviewed: ~16 hours ago

Table of Contents

Discovery Implementation Validation

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.