Splits work across multiple agents or sessions running at once, keeping their surfaces disjoint so results merge cleanly. Use this when facing several independent tasks with no shared state, when a plan has parallelizable steps, when a broad search or audit would be faster fanned out, or when deciding whether work can safely be split at all.
日本語の概要は準備中です。原文の説明を表示しています。
cbrock84/headcount☆ 2,0772026年10月19日 更新
Designs and audits who can reach what — authentication, authorization models, privileged access, service credentials, and joiner-mover-leaver process. Use this to design a permissions model, run an access review, reduce standing privilege, handle offboarding, set up SSO or MFA, manage service and machine credentials, or diagnose why permissions have sprawled.
日本語の概要は準備中です。原文の説明を表示しています。
cbrock84/headcount☆ 2,0772026年10月19日 更新
Builds and interrogates a project schedule — logic-driven sequencing, dependency types and lags, float and the critical path, resource loading and leveling, schedule risk analysis, and measuring progress against a baseline rather than against optimism. Use this to construct a schedule, audit one you have inherited, find out why a plan keeps slipping, work out what a date change actually costs, or judge whether a reported percentage complete means anything.
日本語の概要は準備中です。原文の説明を表示しています。
cbrock84/headcount☆ 2,0772026年10月19日 更新
Runs the pay cycle so it is right, on time, and provable — the calendar and cutoffs, what feeds pay from the HRIS and time systems, gross-to-net and the deductions in it, multi-jurisdiction registration and tax filing, off-cycle payments and corrections, and the reconciliation to the general ledger. Use this to design or audit a payroll process, work out what happens when someone is paid wrong, or understand what changes when you hire in a new state or country.
日本語の概要は準備中です。原文の説明を表示しています。
cbrock84/headcount☆ 2,0772026年10月19日 更新
Defines and applies visual brand — logo usage, palette, typography, imagery direction, and the guidelines that keep expression consistent across product and marketing surfaces. Use this to establish a brand from scratch, audit whether an existing brand is being applied consistently, build brand guidelines, or decide how a brand should express itself in an unfamiliar format.
日本語の概要は準備中です。原文の説明を表示しています。
cbrock84/headcount☆ 2,0772026年10月19日 更新
Upgrades an existing interface to a higher standard without rebuilding it — auditing what is there, identifying what reads as generic or unfinished, and sequencing changes by impact. Use this when a product works but looks dated or default, when a redesign is being considered, when deciding whether to restyle or rebuild, or when a screen needs to look substantially better with limited effort.
日本語の概要は準備中です。原文の説明を表示しています。
cbrock84/headcount☆ 2,0772026年10月19日 更新
Tracks hardware and software assets through their life — procurement, ownership, licensing, refresh, and disposal. Use this to build an asset register, prepare for a software audit, plan a refresh cycle, control license spend, or dispose of equipment safely.
日本語の概要は準備中です。原文の説明を表示しています。
cbrock84/headcount☆ 2,0772026年10月19日 更新
Executes joiner, mover and leaver processes — provisioning, group membership, access changes on role change, and complete deprovisioning. Use this to set up or fix joiner-mover-leaver, deprovision someone completely, clean up accumulated access, audit group membership, or find accounts that outlived their owners.
日本語の概要は準備中です。原文の説明を表示しています。
cbrock84/headcount☆ 2,0772026年10月19日 更新
Audits and rewrites landing pages, homepages, and sales pages to increase conversion — diagnosing why a page is not converting, rewriting headlines, hero copy and calls to action, and prioritizing which changes to test first. Use this when a page underperforms, when someone pastes a URL or screenshot asking what is wrong, when writing a new page, or when deciding which conversion changes are worth testing.
日本語の概要は準備中です。原文の説明を表示しています。
cbrock84/headcount☆ 2,0772026年10月19日 更新
Structures the tax questions a growing business faces — corporate income, sales and use, payroll, nexus, and the obligations created by hiring or selling somewhere new. Use this to work out what a new state or country obligates you to, prepare for a tax filing or audit, understand sales tax on your product, or check what a remote hire or new market triggers.
日本語の概要は準備中です。原文の説明を表示しています。
cbrock84/headcount☆ 2,0772026年10月19日 更新
Improves visibility and conversion in the App Store and Google Play — metadata, keywords, screenshots, ratings, and the listing experience that turns an impression into an install. Use this to audit or optimize an app listing, plan a launch listing, diagnose poor install conversion, or improve store search visibility.
日本語の概要は準備中です。原文の説明を表示しています。
cbrock84/headcount☆ 2,0772026年10月19日 更新
Review a change to the ffmpeg-skill repository for the failures its own contract makes possible — a claim in a result document that is true at one layer and false at the layer a caller reads, a new flag that reaches the code but not the contract/docs/demo surfaces, a "bug" that docs/design-decisions.md already decided with a pinning test, a fix with no regression test, a new runtime dependency or a raw ffmpeg shell call that breaks the scope boundary, a SKILL.md line added without one trimmed, a review that re-derives findings from a summary instead of the tree at the reviewed commit. Use when asked to review a PR or diff in this repository, when authoring code here and needing a pre-commit check, or when triaging an external review or audit report about this codebase.
日本語の概要は準備中です。原文の説明を表示しています。
kajisho5/ffmpeg-skill☆ 2,0312026年10月20日 更新
Cut GitHub Actions minutes and wall-clock time without losing coverage — the OS billing multiplier (macOS 10x / Windows 2x / Linux 1x), trigger hygiene that stops push+pull_request double-firing, concurrency that cancels superseded runs, dependency caching keyed on lockfiles, matrix discipline, and auditing scheduled crons that bill 24/7. Use when CI is burning included minutes, when runs feel slow, when setting up a new repo's workflows, or when reviewing an existing workflow for waste.
日本語の概要は準備中です。原文の説明を表示しています。
kajisho5/ffmpeg-skill☆ 2,0312026年10月20日 更新
Prevents, detects, and remediates files that should never be committed — secrets (.env, API tokens, hardcoded credentials) and dev artifacts (build output, scratch databases, editor/OS files). Covers .gitignore (and why it does not untrack), git rm --cached, auditing tracked files, history scrubbing, and credential rotation. Use when a repo has committed secrets or junk, when setting up a new repo's ignore rules, or when reviewing what a repo actually tracks.
日本語の概要は準備中です。原文の説明を表示しています。
kajisho5/ffmpeg-skill☆ 2,0312026年10月20日 更新
Review code changes for correctness, security, and maintainability. Supports local git diffs (staged or working tree) and remote Pull Requests (by ID or URL). Use when the user asks to review code, check a PR, audit changes, or wants feedback on code quality before merging.
日本語の概要は準備中です。原文の説明を表示しています。
openakita/openakita☆ 2,0032026年9月24日 更新
Converts an audited medical research gap into a complete, structured, gap-traceable study design. Always use this skill whenever a user already has one or more candidate research gaps and wants to transform them into an executable biomedical research plan rather than re-run broad topic ideation. Covers six gap-to-design patterns (evidence-completion, mechanism-resolution, cell-state/context-mapping, translation-bridge, causality-upgrade, population/stage-specific) and always outputs one recommended primary protocol, a gap-to-design dependency map, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, and verified design-support literature rules. Never fabricate references. Preserve claim-evidence discipline and do not replace a topic-specific gap with a generic workflow.
日本語の概要は準備中です。原文の説明を表示しています。
aipoch/medical-research-skills☆ 1,9752026年9月17日 更新
Designs primary, secondary, and exploratory endpoints for biomedical and clinical research protocols. Always use this skill when a user needs to translate study aims into operational endpoint definitions with event rules, assessment timing, composite logic, interpretability, and protocol-stage auditability. Focus on endpoint precision, feasibility, clinical meaning, ambiguity reduction, and implementation readiness rather than generic study design advice.
日本語の概要は準備中です。原文の説明を表示しています。
aipoch/medical-research-skills☆ 1,9752026年9月17日 更新
Identifies translationally meaningful paths for bioinformatics findings by mapping omics or computational discoveries to diagnosis, stratification, prognosis, treatment-response, monitoring, or target-nomination use cases, while auditing bridge evidence, assayability, and validation burden. Use this skill when a user wants to know whether a bioinformatics finding can be framed as a stronger translational topic without overclaiming clinical relevance. Always separate statistical signal from translational value, and never imply clinical utility, targetability, or validation depth without explicit evidence support.
日本語の概要は準備中です。原文の説明を表示しています。
aipoch/medical-research-skills☆ 1,9752026年9月17日 更新
Identifies real, evidence-audited, topic-specific research gaps in medical research by first retrieving and verifying literature from trusted sources, then mapping the current evidence landscape, rejecting pseudo-gaps, and converting only medium/high-confidence gaps into study-ready research opportunities. Always require real literature retrieval before formal gap claims. Never fabricate references, metadata, or findings.
日本語の概要は準備中です。原文の説明を表示しています。
aipoch/medical-research-skills☆ 1,9752026年9月17日 更新
Explains why studies on the same biomedical topic reach different or opposing conclusions by auditing differences in population, endpoint definition, sample source, assay or platform, study design, statistical model, adjustment strategy, validation chain, and bias control. It separates true contradiction from apparent contradiction caused by framing or methods. Never fabricate references, PMIDs, DOIs, trial identifiers, dataset details, platform details, study features, or conflict explanations that are not supported by the input.
日本語の概要は準備中です。原文の説明を表示しています。
aipoch/medical-research-skills☆ 1,9752026年9月17日 更新
Assesses whether study results are trustworthy by auditing design integrity, sample structure, statistical handling, bias control, validation chain, and claim discipline. It identifies where results are robust, fragile, overfit, under-validated, or overclaimed. Always separate reported findings from reliability judgment. Never fabricate references, PMIDs, DOIs, trial identifiers, study features, or validation claims.
日本語の概要は準備中です。原文の説明を表示しています。
aipoch/medical-research-skills☆ 1,9752026年9月17日 更新
Assesses whether a medical research topic is worth starting now by separating true novelty from pseudo-novelty, auditing real feasibility under stated resource constraints, and forcing a concrete start / narrow / redesign / stop decision. Always require explicit assumptions and never fabricate references, datasets, resource availability, precedent studies, or publication claims.
日本語の概要は準備中です。原文の説明を表示しています。
aipoch/medical-research-skills☆ 1,9752026年9月17日 更新
Use when the user asks to review, verify, or audit a report, manuscript, or analysis in the workspace for traceability — resolving citations, flagging numbers with no source, and checking figures against the code that generated them. Emits a structured review block the app renders as reviewer findings. Verifies traceability, never "correctness".
日本語の概要は準備中です。原文の説明を表示しています。
ai4s-research/open-science☆ 1,9412026年10月20日 更新
Use ONLY when the user asks to check, validate, review or audit analysis code for scientific correctness, or when you are the reviewer agent running its checks. Never run it unprompted as part of ordinary analysis work. Covers physics, earth/geo, biology, chemistry, social science and bioprocess/fermentation. Runs a deterministic domain-correctness gate that catches code which runs but is scientifically wrong (unit/dimension mismatch, Euclidean distance on lat/lon without a CRS, 0-based/1-based coordinate and strand errors, impossible SMILES valence, uncorrected multiple comparisons, averaging a categorical code, unconstrained kinetic-parameter fits, kLa computed from raw DO instead of the driving force, arithmetic mean of raw CFU counts, ANOVA/Tukey with no assumption check, a curvature DOE design fit with a first-order model, an ML model fit across process scales with no correction). Surfaces structured findings; never claims the code is correct.
日本語の概要は準備中です。原文の説明を表示しています。
ai4s-research/open-science☆ 1,9412026年10月20日 更新