523 件 ・ 関連度順
概要と使いどころ
Procedure for controlled, human-gated outbound README badge provisioning PRs (Issue #1817). Preflights upstream repositories, ensures single-badge placement with deep links, and records campaign outcomes without automated retries or multi-badge noise.
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。
Append a session/state snapshot to MEMORY.md (regardless of where it lives — for this repo it's `founder/MEMORY.md`). Use when the user says "snapshot memory", "save state to memory", "/memory-snapshot", "log what happened in MEMORY", or otherwise asks to record the current session's outcomes for the next orchestrator. Locates the project's MEMORY.md, prepends a dated entry above the previous snapshot (newest first), preserves every existing entry (NEVER overwrites or truncates), and reports back the file path + byte delta. Not for one-off feedback memories (use the user-level memory system at ~/.claude/.../memory/ for those). Not for trimming or deleting old entries.
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。
Firecrawl gives AI agents and apps fast, reliable web context with strong search, scraping, and interaction tools. One install command sets up three skill segments: live CLI tools, app-integration build skills, and outcome-focused workflow skills. Route the reader to the right usage path after install.
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。
Frames a structured, testable threat-hunting hypothesis: grounding it in adversary behavior and available telemetry, defining the data sources and detection logic, and setting success criteria and outcomes. Activates for requests to start a threat hunt, write a hunt hypothesis, or plan a hypothesis-driven hunt.
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。
Turn a moment of friction — a conflicting PR, a wedged runner, a mysterious red check, a repeated manual chore — into a durable, actionable report. Sizes up the scenario, captures the evidence, separates one-off from SYSTEMIC, proposes a fix, and routes it (fix now, or hand off). Reports are LIVING documents that the fixing agent updates with the outcome. Invoke whenever something feels like it shouldn't have been this hard, or when the same annoyance shows up twice.
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。
Use when a task needs the judgment of an Engineering Teacher, Postsecondary — diagnosing a spike in a gatekeeper course's D/F/W rate, running a capstone design course with an industry sponsor, closing the loop on an ABET student-outcome assessment, or building a tenure/promotion teaching-effectiveness case beyond raw student evaluation scores.
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。
Use when a task needs the judgment of an English Language and Literature Professor — backward-designing a first-year composition or literature syllabus against a shared outcomes rubric, deciding whether an AI-detection or plagiarism flag on a student essay warrants an integrity case, reading where a tenure case's monograph actually sits in a university press's pipeline, or handling a content complaint about an assigned literary text.
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。
Use when a task needs the judgment of a Postsecondary Computer Science Professor — diagnosing why a course's DFW (D/F/withdraw) rate spiked, redesigning an intro programming course's assessment structure, staffing TAs and grading load for a large course, deciding what to prioritize pre-tenure, running an academic-integrity (MOSS similarity) investigation, or mapping a curriculum change against ABET student outcomes.
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。
Produce a flow-level summary of the current Claude Code session — executive summary, chronological timeline, and outcomes — written into the active feature directory next to spec.md / plan.md.
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。
Audit a rehabilitation recovery tracking system -- evaluate standardized outcome instruments (FIM, Barthel Index, SF-36, DASH, LEFS, PROMIS), functional assessment scoring accuracy, SMART goal and milestone tracking, regression detection with alert workflows, pain scale calibration (NRS.
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。
Audit a rehabilitation or physical therapy platform end-to-end -- evaluate recovery metrics tracking (ROM, strength, balance, gait), patient-reported outcomes (DASH, LEFS, NDI, ODI, PROMIS), home exercise program personalization and compliance tracking.
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。
Audit a grant management system for proposal workflow efficiency, deadline tracking, budget-narrative alignment, outcome reporting, compliance readiness, and win rate optimization. Use when reviewing nonprofit grant software, building a grants CRM, analyzing proposal pipelines, or evaluating funder reporting tools.
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。
Analyze disability services software — IEP and ISP management, person-centered planning workflows, HCBS Settings Rule compliance, accommodation tracking, assistive technology integration, EVV (Electronic Visit Verification), caregiver and DSP scheduling, and outcome measurement.
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。
Audit curriculum management software for learning outcome alignment, pacing guide optimization, standards coverage mapping (Common Core, NGSS, state standards), differentiation support, assessment design quality, and content gap detection across grade levels and subjects..
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。
Build QA quality dashboards, calculate defect metrics, analyze escape rates, and produce quality trend reports. Use this skill whenever a QA lead or manager needs to measure and communicate quality outcomes. Trigger on phrases like "quality metrics", "defect density", "escape rate", "bug trends", "quality dashboard", "sprint quality report", "test coverage metrics", "QA KPIs", "how is quality trending", "defect leakage", "production bugs this quarter", "mean time to detect", "quality scorecard", or any request to quantify, visualize, or report on the quality of a product or testing process. Also trigger when a manager asks "how is QA performing?" or an engineering lead asks "what does our defect data tell us?".
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。
Run and document QA test execution cycles - smoke tests, regression suites, exploratory testing sessions, and release readiness checklists. Use this skill whenever a QA engineer needs to structure or report on active testing work. Trigger on phrases like "run smoke tests", "start a regression cycle", "exploratory testing session", "test this build", "release checklist", "is this build stable", "log test results", "what passed and what failed", "document my testing", "create a test run report", or any request to execute and record test outcomes. Also trigger when someone says "we need to test before release" or "can you help me track what I've tested".
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。
Run work as an explicit event-driven state machine executed by subagents. The orchestrator is the runtime from the canonical loop — `state, effects = init(machine); execute(effects); while state is active: event = nextEvent(); state, effects = transition(machine, state, event); execute(effects); return state.output`. All routing judgment is authored ONCE, at plan time, into a machine file (states, event alphabet, transition table, budgets, final states); at runtime the orchestrator does pure table lookups and dispatches effects — it never decides, never authors, never ingests artifacts. Subagent/sync protocol is the timeboxed-iterating one: role prompts scaffolded once to disk and dispatched BY PATH, an initialiser preamble every subagent reads first, a self-populating cheatsheet, per-unit files, tiny structured returns, independent measurement gate. Use when the user says "workmachine", "run this as a state machine", "state-machine this", or when a goal has a small number of clearly named phases with explicit outcomes (built / passed / failed / blocked) and you want the routing to be auditable rather than judged on the fly. For clock-driven loops use timeboxed-iterating; for wide graphs with judged edge criteria use workgraph.
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。
Verify-before-done — exercise the current change end-to-end against its acceptance criteria (the SDD plan's, or the ticket's) and report a per-criterion PASS/FAIL verdict, then record the outcome to memory. This is the concrete action that satisfies moflo's verify-before-done gate (`gates.verify_before_done` / `/flo -v` / `/flo -sd`). Use before `gh pr create` when the change must be proven to work, not just tested in passing. Verifies only — it does not fix; on FAIL it reports the gaps and stops.
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。
Record final outcomes to history.md, not intermediate requests or reversed decisions
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。
Use Notion to track meeting patterns, identify communication insights, and improve team coordination. Record decisions, attendees, and outcomes for trend analysis.
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。
Advisory model escalation based on task outcome tracking
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。
Quantifies CRISPR editing outcomes with CRISPResso2 (Clement 2019 Nat Biotechnol) across Cas9-nuclease (indels, HDR), CBE and ABE base editors (target conversion + bystander), and prime editor (pegRNA-templated) modes. Covers single-amplicon (CRISPResso), multi-sample batch (CRISPRessoBatch), pooled-amplicon (CRISPRessoPooled), WGS off-target (CRISPRessoWGS), and sample-comparison (CRISPRessoCompare) workflows; quantification-window math that controls what is called edited; substitution-vs-indel diagnostic to distinguish BE from Cas9 contamination; MMEJ deletion pattern interpretation; allele-frequency tables; and failure modes from amplicon misalignment or contamination. Use when quantifying editing from amplicon sequencing, choosing CRISPResso mode by design, distinguishing intended edits from bystanders and indel byproducts, debugging low-alignment runs, or generating publication-grade editing reports.
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。
Performs logistic regression for clinical trial outcomes (binary, ordinal, multinomial) with marginal-vs-conditional estimand reporting per FDA 2023 covariate adjustment guidance, g-computation/standardisation for marginal effects, modified Poisson for RR, Brant test for proportional odds, Firth penalty for separation, and Hauck-Donner detection. Use when modeling binary or ordinal endpoints in confirmatory or exploratory clinical trials.
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。
Decompose total effects into direct and indirect paths through mediators using mediation, CMAverse 4-way, HIMA/HIMA2 high-dimensional, BAMA, two-step / MVMR mediation, or double-ML medDML. Use when testing whether a molecular phenotype (expression, methylation, protein) mediates a treatment-outcome relationship, decomposing exposure-mediator interaction via VanderWeele 4-way, screening high-dimensional EWAS mediators, or running MR-based mediation when sequential ignorability is implausible.
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。