skills/33-Galaxy-Dawn-claude-scholar/skills/skill-development/SKILL.md
This skill should be used when the user asks to create a new skill, repair an existing skill, improve trigger descriptions, reorganize skill structure, or make a Claude skill more reusable and internally consistent.
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research skill-developmentInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill to create or repair Claude skills in the current local environment, not in an abstract plugin template.
Produce a skill that is:
SKILL.md layer,references/, examples/, and scripts/ files when they are mentioned,SKILL.md focused on workflow and boundaries.references/ or examples/.Before writing anything:
Use the local inventory as the authority. Do not write guidance against an imagined plugin layout.
Define four things before editing:
If the skill only needs a short workflow, keep it short. Do not create references/, examples/, or scripts/ just because the directories are conventional.
The frontmatter should:
name,Prefer descriptions of this form:
---
name: skill-name
description: This skill should be used when the user asks to "...", "...", or needs help with ....
---
A good SKILL.md should usually contain:
Move these out of the main file when they get long:
Use bundled resources deliberately:
references/ for detailed guidance that may be loaded selectively,examples/ for real example outputs or scaffolds,scripts/ for deterministic helper logic.If a resource is mentioned in SKILL.md, it must exist.
If a resource exists but is never referenced or used, delete it.
At minimum, verify:
SKILL.md is not overloaded with material that belongs in references,SKILL.md,references/,When creating or repairing a skill, prefer ending with:
Load only what is needed:
references/checklist.md - compact quality checklist before closing a skill editreferences/integrity-checks.md - concrete local checks for missing files, dead references, and driftreferences/skill-creator-original.md - legacy background reference; use for context, not as the live source of truthdevelopment
Conduct rigorous thematic analysis (TA) of qualitative data following Braun and Clarke's (2006) six-phase framework. Use whenever the user mentions 'thematic analysis', 'TA', 'Braun and Clarke', 'qualitative coding', 'identifying themes', or asks for help analysing interviews, focus groups, open-ended survey responses, or transcripts to identify patterns. Also trigger for questions about inductive vs theoretical coding, semantic vs latent themes, essentialist vs constructionist epistemology, building a thematic map, or writing up a qualitative findings section. Covers all six phases, the four upfront analytic decisions, the 15-point quality checklist, and the five common pitfalls. Produces a Word document write-up and an annotated thematic map. Does NOT cover IPA, grounded theory, discourse analysis, conversation analysis, or narrative analysis — use a different method for those.
development
Guide users through writing a systematic literature review (SLR) following the PRISMA 2020 framework. Use this skill whenever the user mentions 'systematic review', 'systematic literature review', 'SLR', 'PRISMA', 'PRISMA 2020', 'PRISMA flow diagram', 'PRISMA checklist', or asks for help writing, structuring, or auditing a literature review that follows reporting guidelines. Also trigger when the user asks about inclusion/exclusion criteria for a review, search strategies for databases like Scopus/WoS/PubMed, study selection processes, risk of bias assessment, or narrative synthesis for a review paper. This skill covers the full PRISMA 2020 checklist (27 items), produces a Word document manuscript in strict journal article format, generates an annotated PRISMA flow diagram, and enforces APA 7th Edition referencing throughout. It does NOT cover meta-analysis or statistical pooling. By Chuah Kee Man.
testing
Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance. Use when checking model robustness, verifying lack of pre-intervention effects, or estimating study power.
data-ai
Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.