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概要と使いどころ
Advises on setting up and validating LLM text classification: codebook, learning regime and model choice, prompts, validation against human coding (kappa, F1), hybrid review, measurement error, and reporting, plus a short resumable batch pattern with a rule-based baseline. Use to classify, code, or label text at scale; category discovery goes to topic-modeling.
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Designs multi-country survey experiments: case selection, localization and TRAPD translation, stimulus calibration, per-country power, and pooled versus per-country analysis with measurement-equivalence checks. Use when fielding the same experiment in several countries.
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。
Designs multi-country survey experiments, covering case selection with a stated comparative logic, instrument localization and TRAPD translation, stimulus calibration, per-country power, and pooled versus per-country analysis with measurement-equivalence checks. Use when the user fields the same experiment in several countries, asks which countries to include, how to adapt an instrument without breaking comparability, whether to pool, or how large each country sample must be. Question wording goes to survey-design, reporting to methods-reporting, the study plan to pre-registration-writing.
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。
Reviews an existing conjoint study for threats to inference and returns severity-ranked findings on design integrity, estimation, measurement error, external validity, and interpretation, including the guardrail against reading an AMCE as majority preference. Use when the user asks whether a conjoint design or analysis holds up, has referee comments on a conjoint, or wants a second opinion on estimation or interpretation. New designs go to conjoint-design, reshaping to conjoint-cleaning.
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。
Designs and diagnoses list experiments (the item count technique): whether indirect measurement is warranted, control-list construction, design variants, estimators and assumption tests in the list package, and power. Use when measuring a sensitive item or debugging an implausible prevalence estimate.
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。
OCR scanned or image-only corpora with vision-language models in three phases: `evaluate` picks a system by measured CER/WER against stratified human ground truth, `run` builds the production pipeline, and `clean` corrects raw OCR with LLM and rule-based passes, diagnostics, and span-level provenance. Born-digital documents with a text layer go to $doc-to-markdown.
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。
Advises on setting up and validating topic models for survey and experimental text: discovery vs. measurement, STM/LDA/BERTopic choice, preprocessing, covariate formulas, choosing K across coherence, FREX, held-out likelihood, and residuals, validating topics against human coding, robustness, and reporting. Coding against a fixed codebook goes to text-classification.
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。
The harnesses a campaign keeps, read outside a run: which there are and where each came from, what each is made of and what moved from its parent, what each was measured to be, trying one on a task, the audit before a promotion and the blind review at a handover, whether a part earns its place, and changing what makes a harness or what a seat is told about one. Use when the operator asks which harness is current, what a Builder changed, whether a harness is ready to promote or hand over, to run a kept harness on a task, or to change the harness contract. Reading one run is whole-run-investigation's, wording a claim attribution-and-proof's.
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。
Measure whether a change made the tree simpler, on numbers rather than impressions — line counts, branching, coupling, interface width, rule count, standing exemptions and model-visible prose — across one diff or a whole stack of pull requests. Use before claiming a pass reduced complexity, when deciding which pull request in the stack added the machinery, and to read a function's branch count, which no v2 gate 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。
Use after an Anabasis v2 comparison, checkpoint or system change, and before any sentence claims that a harness, a model or a patch improved something. States what one reading may be said to show: the identities it was made under, the counts kept apart, whether it was repeated, which part of a harness it belongs to, whether a mechanism was live in the run, and whether the measurement is valid as distinct from whether it was exploited. Ends in one fixed report.
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。
Review changed Anabasis v2 code for reuse, simplification, efficiency and altitude, then apply the fixes. Quality only: it hunts no bugs, which /code-review does. Starts from what the repository already finds (the lint plugins, the whole-tree census, the not-slop ledger), measures before and after with reduce-complexity's scripts, and holds a pass to its budgets and to the rules no pass trades. Use as /simplify on your own diff before a commit, or when the operator asks whether code is simple enough, why something exists, to go one layer higher, or to remove something and find another way.
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。
Track and measure agentic coding KPIs for ZTE progression. Use when measuring workflow effectiveness, tracking Size/Attempts/Streak/Presence metrics, or assessing readiness for autonomous operation.
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 and safely clean Xcode-related developer storage on macOS. Use whenever a developer mentions low disk space, Xcode storage, DerivedData, simulator devices or runtimes, stale beta platforms in Xcode Settings, DeviceSupport, archives, dSYMs, CocoaPods caches, simulator dyld caches, project .build folders, downloaded Xcode DMGs/XIPs, duplicate Xcode installers, or old Xcode applications. Also use this skill proactively when you hit low storage during other work, such as a build, download, or install failing with an out-of-disk-space error. Always measure first, report recoverable GiB with evidence, and obtain explicit itemized approval before any mutation.
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。
What this project has already learned the hard way, and where any new such fact belongs. Two jobs. (1) Routing: this project keeps NO per-project Claude memories — read this before writing anything down, and pick a durable home from the list here. (2) An index, by area, of non-obvious facts worth knowing before you guess: debugging rigs and measurements that lie, iOS/WebKit and the tethered iPhone, the Windows app, production and deployment, and general codebase traps. Use before setting up a browser- or server-driven debugging session, when a bug reproduces only on iPhone, in Safari or in the Windows app, before changing deployment configuration or reading store/crash data, when something behaves unexpectedly and you are about to guess why, and whenever you are tempted to record a fact for later.
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。
Plan influencer marketing campaigns — find creators, write briefs, manage partnerships, and measure ROI. Use when "influencer", "influencer marketing", "creator campaign", "UGC campaign", "brand ambassador", "creator partnership", or any request to work with influencers or content creators.
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。
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
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 concurrent scripted conversations against a target agent to measure whether it stays on task, responds correctly, and holds up in repeatable test cases.
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。
Expert product analytics strategist for SaaS and digital products. Use when designing product metrics frameworks, funnel analysis, cohort retention, feature adoption tracking, A/B testing, experimentation design, data instrumentation, or product dashboards. Covers AARRR, HEART, behavioral analytics, and impact 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。
Expert sales enablement strategist for building high-performing sales teams. Use when designing sales training programs, onboarding and ramp plans, sales playbooks, coaching frameworks, certification programs, or competitive intelligence distribution. Covers content strategy, tool adoption, performance measurement, and continuous learning systems. Use for building sales academies, creating enablement content, and optimizing sales productivity.
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。
The Mileva Method (CRISP) — Phase 5: Prove. Success validation against Phase R baseline metrics, test log review, and AI justification validation. Use after deployment. Triggers on "prove", "phase 5", "validate", "did it work", "success criteria", "measure results", or after go-live.
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。
The Mileva Method (CRISP) — Phase 2: Results. Outcome alignment, stakeholder register, baseline measurement, success metrics, tradeoff negotiation. Use after problem definition is complete. Triggers on "results", "phase 2", "define success", "success metrics", "what does done look like", or after Phase C exit checklist is complete.
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。
Go-to-market engineering for the Yupcha portfolio (OmniVoice-Studio, the Clay-alternative in this repo, ResuBird). A closed set of executable verbs — research, audit, draft, measure, prepare, publish, escalate — each with a typed contract, required sources, and a risk tier. Use when: (1) researching ICP/competitors/channels/prior art, (2) auditing our own properties for what's actually broken (dead pricing pages, 404ing clone URLs, unfilled demos, licence detection, watermark coverage), (3) drafting docs/comparison pages/release notes/launch copy, (4) measuring real numbers against GOAL.md, (5) preparing a Show HN or a listing up to the send button, (6) any question about whether a GTM action is allowed. Triggers: 'gtm', 'go to market', 'launch', 'show hn', 'growth', 'marketing', 'positioning', 'icp', 'competitors', 'outreach', 'cold email', 'audit our', 'is it ok to', 'can we post', 'promote'.
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。
Analyzes web performance using Chrome DevTools MCP. Measures Core Web Vitals (LCP, INP, CLS) and supplementary metrics (FCP, TBT, Speed Index), identifies render-blocking resources, network dependency chains, layout shifts, caching issues, and accessibility gaps. Use when asked to audit, profile, debug, or optimize page load performance, Lighthouse scores, or site speed. Biases towards retrieval from current documentation over pre-trained knowledge.
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。
Evaluate Mycelium's own process effectiveness. Measures cycle velocity, discard trends, confidence calibration, gate effectiveness, regression rate. Run quarterly or every 20 cycles.
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。