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aiming-lab
GitHub 제작자 프로필

aiming-lab

3개 GitHub 저장소에서 수집된 72개 skills를 저장소 단위로 보여줍니다.

수집된 skills
72
저장소
3
업데이트
2026-05-20
저장소 탐색

저장소와 대표 skills

agent-task-handoff
기타 컴퓨터 관련 직업

Use this skill when delegating a subtask to a sub-agent, spawning a parallel worker, or handing off work across sessions. Write a self-contained task description so the receiving agent needs no prior context.

2026-03-10
async-communication-etiquette
일반 사무원

Use this skill when writing messages in async channels (Slack, GitHub issues, email threads) where the reader may not have context and cannot ask follow-up questions immediately.

2026-03-10
audience-aware-communication
일반 사무원

Use this skill when writing any explanation, documentation, or response that will be read by someone else. Match vocabulary, depth, and format to the audience's expertise level before writing.

2026-03-10
auth-and-authorization-patterns
정보 보안 분석가

Use this skill when implementing authentication (login, token issuance) or authorization (access control, permissions). Apply whenever the task involves login flows, JWT, OAuth2, session management, or RBAC.

2026-03-10
avoid-acting-on-assumptions
프로젝트 관리 전문가

Common mistake — proceeding with assumptions about ambiguous requirements instead of asking a clarifying question first. This skill reminds you to stop and ask before acting on uncertain interpretations.

2026-03-10
avoid-hallucinating-specifics
소프트웨어 개발자

Common mistake — stating specific facts (API endpoints, library versions, config options, function signatures) with false confidence when uncertain. Always flag uncertainty rather than guessing specifics.

2026-03-10
avoid-scope-creep
프로젝트 관리 전문가

Common mistake — doing unrequested work (refactoring, adding extra features, cleaning up style) when the user asked for a specific, targeted change. Only change what was explicitly asked.

2026-03-10
clarify-ambiguous-requests
프로젝트 관리 전문가

Use this skill when the user's request is ambiguous, under-specified, or could be interpreted in multiple ways. If proceeding with a wrong assumption would waste significant work, always ask exactly one focused clarifying question before doing anything.

2026-03-10
이 저장소에서 수집된 skills 36개 중 상위 8개를 표시합니다.
fba-simulator
기타 생물 과학자

Run Flux Balance Analysis (FBA) and related constraint-based simulations using COBRApy. Covers standard FBA, parsimonious FBA (pFBA), Flux Variability Analysis (FVA), loopless FBA, gene/reaction knockouts, and carbon source swapping. Outputs flux distributions and CSV files.

2026-05-20
flux-analyzer
기타 생물 과학자

Analyse FBA flux distributions to extract biological insights. Covers gene essentiality, phenotypic phase planes, flux sampling, pathway-level aggregation, secretion product prediction, and production of publication- quality figures.

2026-05-20
gsmm-builder
기타 생물 과학자

Build or load a genome-scale metabolic model (GSMM) using COBRApy. Covers loading from BIGG, constructing minimal models from scratch, setting medium constraints, and exporting validated .json model files.

2026-05-20
gsmm-validator
기타 생물 과학자

Validate a COBRApy genome-scale metabolic model for mass/charge balance, stoichiometric consistency, biomass producibility, dead-end metabolites, thermodynamic loops, and GPR rule formatting. Outputs a structured validation report with errors and warnings.

2026-05-20
metabolic-study-planner
기타 생물 과학자

Plan publishable constraint-based metabolic modelling studies when the user has a broad biological or metabolic-engineering topic but no concrete dataset, organism, model, or hypothesis. Selects feasible BiGG/COBRA models, objectives, perturbations, analyses, metrics, figures, and risk controls before FBA code is generated.

2026-05-20
mfa-pipeline-orchestrator
기타 생물 과학자

Orchestrate the full metabolic flux analysis pipeline from model loading to phenotype prediction and publication figures. Triggers when the user provides an organism name, BIGG model ID, or custom reaction list and wants end-to-end metabolic modelling run automatically.

2026-05-20
stat-research-orchestrator
데이터 과학자

Orchestrate a statistical research pipeline centered on formal problem formulation, method proposal, theoretical analysis, experimental evaluation, comparison, and final result synthesis.

2026-05-20
stat-result-validator
데이터 과학자

Validate statistical research outputs for formulation quality, method-to- problem alignment, theory presence, experimental evidence, fair comparison, artifact completeness, and final-claim consistency.

2026-05-20
이 저장소에서 수집된 skills 34개 중 상위 8개를 표시합니다.
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