13.4K skills · sorted by relevance
Run a structured sprint retrospective, a review of a short work cycle, using team feedback and delivery results. Identify shared themes and produce two or three prioritized improvements with owners, deadlines, and measures of success.
Review agent skills for risky behavior, excessive permissions, and hidden data sharing. It combines static scanning, which checks known risk patterns without running code, with source review to produce an evidence-based installation verdict.
Set up an inspection panel in React json-render apps to examine UI definitions, edit state, and review action and stream timelines. The skill also covers opening or toggling the panel and recording events from React code.
Draft a patent specification from claims and invention disclosure materials. The skill prepares sections from background to detailed embodiments and abstract, then checks whether the description supports each element of the claims.
Turn structured JSON into editable SVG diagrams for system architectures, workflows, and pipelines. Specify positions, connections, and groups precisely, reproduce the same output under fixed rendering conditions, and convert figures to PDF for papers.
Convert a Notion specification into a linked implementation plan and manageable tasks. Extract requirements and acceptance criteria, record uncertainties, and keep task statuses, checklists, blockers, and milestone updates aligned as the work progresses.
Read existing code to reconstruct system specifications, including architecture, data flows, APIs, and business rules. Produces documentation tied to code locations, separating observed behavior from inferences and questions that need clarification.
Turn a feature idea into a specification through structured questions about users, business goals, and technical constraints. Produce requirements, user stories, testable acceptance criteria, and an implementation checklist, including performance, security, and error handling.
Prepare an AI-SPEC.md design agreement for an AI development phase. The skill coordinates framework selection, documentation and domain research, and evaluation planning so the phase has a defined technical direction and assessment strategy.
Model distributed systems and concurrent algorithms in TLA+ or PlusCal, then check possible states with TLC. Use the results to investigate deadlocks, broken rules, and differences between specifications and implementation code.
Turn rough feature ideas into a clear SPEC.md through focused questions. Define target users, business rules, current scope, and acceptance criteria based on observable results, giving developers or AI agents concrete requirements to implement.
Review feature plans before development from business strategy, user experience, and engineering perspectives. Receive scored assessments, specific gaps tied to requirements, and prioritized actions that clarify what must change before the team starts building.
Turn Notion product requirements and feature specifications into linked implementation plans and manageable tasks. Extract requirements, flag ambiguities, and keep task status, milestones, blockers, and decisions aligned as work progresses.
Design specialized assistants that work with their own separate context. Define responsibilities, delegation conditions, working steps, and report formats for reusable subagents handling verification, debugging, security audits, or code reviews.
Review changes from a chosen Git reference against both project coding standards and issue or specification requirements. Separate parallel reviews report rule violations, possible design problems, missing requirements, and changes beyond the intended scope.
Review whether a proposal, specification, or design decision addresses the right problem and follows from its goals and constraints. Receive prioritized findings with supporting evidence, confidence, and concrete alternatives or questions that would resolve each concern.
Run a build-and-evaluation pipeline from a natural-language specification. Generate scenarios, implement the project, test scenarios withheld during implementation, and report scores against a target with failures and suggested next steps.
Event prospecting skill. Takes a conference / event speakers URL, extracts the people, filters their companies against the user's ICP, then deep-researches only the speakers at ICP-fit companies. Outputs a person-first HTML report where each card answers "why should the AE talk to this person?" with all public links and a one-click DM opener. Use when the user wants to: (1) find leads at a specific conference, (2) prep for an event, (3) research event speakers, (4) build a target list from a sponsor/exhibitor page, (5) scrape conference speakers and rank by ICP fit. Triggers: "find leads at {event}", "research speakers at", "prospect this conference", "stripe sessions leads", "ai engineer summit prospects", "event prospecting", "scrape conference speakers", "who should I meet at".
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Sync delta specs from a change to main specs. Use when the user wants to update main specs with changes from a delta spec, without archiving the change.
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Writes Specific Aims pages for grant applications. Use when drafting or revising the Specific Aims page (NIH R01/R21/R03), NSF Project Summary, or equivalent for any major funding agency. Also triggers on "write my specific aims", "help me draft specific aims for NIH", "what should a specific aims page include", "NSF project summary", "write my grant aims", or "how do I structure an R01".
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Continue a multi-phase engineering spec from its progress table. Use when the user says /continue-spec, "continue the spec", "continue the operations feature", or asks what the next phase is. Runs the next step (plan, implement, review) in this session on the model the spec names, stopping at the spec's gates. Never uses subagents.
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Read-only drift detector. Diffs SPEC.md against current code and reports violations grouped by severity. Writes nothing — suggests remedies via the spec or build skills but never invokes them. Triggers when the user asks to check drift, audit the spec, verify invariants, or ask whether code still matches the spec. Phrasings: "check drift", "audit the spec", "does the code still match §V", "check invariants", "spec vs code".
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A first step in the data analysis of Mass Spectrometry (MS) based proteomics data is to identify peptides and proteins. With this respect the huge number of experimental mass spectra typically have to be assigned to theoretical peptides derived from a sequence database. Search engines are used for this purpose. These tools compare each of the observed spectra to all candidate theoretical spectra derived from the sequence data base and calculate a score for each comparison. The observed spectrum
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This skill should be used when the user asks to "build a feature", "create a spec", "start spec-driven development", "run research phase", "generate requirements", "create design", "plan tasks", "implement spec", "check spec status", "triage a feature", "create an epic", "decompose a large feature", or needs guidance on spec-driven development workflow, phase ordering, or epic orchestration.
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