「measure」の検索結果

pytest testing patterns for any Python project: test structure, fixtures, parametrize, conftest.py, monkeypatch, tmp_path, markers, unittest.mock (MagicMock, patch), pytest-cov coverage, and best practices. Stack-agnostic — referenced by every Python plugin in the marketplace. Use this skill to: - Organise tests with conftest.py shared fixtures and a clear test/src layout. - Write parametrized tests to cover multiple inputs without duplication. - Mock external dependencies (HTTP, DB, filesystem) with unittest.mock and monkeypatch. - Measure and enforce code coverage with pytest-cov. Do NOT use this skill for: - Framework-specific test types (Django TestCase / WebTestCase, FastAPI TestClient, Flask test client — see framework plugin skills). - Language idioms — see python-foundation:python-conventions.

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Query the ChEMBL database web services for measured bioactivity data, compound records and calculated properties, targets, assays, mechanisms of action, drug indications and warnings. Use this skill to build curated SAR or QSAR datasets for a target, look compounds up by SMILES, InChIKey, name, or ChEMBL id, run similarity and substructure searches, and check what chemistry is already known against a protein. Also trigger when a query mentions ChEMBL ids (CHEMBL...), pChEMBL values, IC50/Ki/Kd/EC50 retrieval, assay confidence scores, or ebi.ac.uk/chembl.

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Compute relative and absolute binding free energies with the Open Free Energy toolkit — the rigorous alchemical alternative to docking scores when a congeneric series needs reliable potency ranking. Use this skill to plan a perturbation network over a ligand set, choose atom mappings, run hybrid-topology or separated-topology protocols, and analyse the result — per-edge ΔΔG with uncertainty, cycle-closure error, and mean unsigned error against measured affinities. Also trigger on OpenFE, alchemical transformation, thermodynamic cycle, RBFE, ABFE, SepTop, lambda window, MBAR, cycle closure, or perturbation map.

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 measured cost surface needs one-change-at-a-time reduction under frozen guardrails. Not for speed-only optimization: use optimize.

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 user needs to create or improve a deterministic, engine-agnostic fuzz harness for raw or structured target inputs. Not for coverage measurement: use fuzzing-coverage-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。

Use when a user needs to measure fuzz corpus coverage, explain a coverage plateau, or turn uncovered regions into campaign work. Not for harness creation: use fuzz-harness-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。

Use when asked to optimize code, make a path faster, make this as fast as possible, reduce allocations, repair a regression, profile a target, grill every inefficiency, or run a subsystem or repo-wide performance campaign against a measured floor; when a performance requirement, slowness report, Core Web Vitals miss, or profiling evidence identifies a bottleneck; or for an "extremely optimize" performance campaign, optimizing suspected hot paths without waiting for benchmarks, or estimating hot and complexity-neutral cold paths while refusing complexity theater. Not for remote, credential, publish, deploy, or irreversible changes.

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Apply one independently reviewable numeric Proto UI package-budget increase for an already accepted capability, using canonical cost evidence and existing development authorization. Use for standalone ceiling transactions, not feature implementation, measurement-policy changes, spending limits, publication or release.

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Measure the active Agent model with pinned offline ModelTrace sampling and return an unsigned closed-set attribution record. Use before Agent-originated commits, Issue/PR creation or material updates, comments, reviews, and current collaboration writes in either execution mode. Do not treat a fingerprint, declared model label, or record reference as authenticated identity, permission, independent review, or acceptance.

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。

Add a real achievement, measurement, skill, or project update to the master resume. Use when the user wants to record new factual experience.

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。

Use when writing, reviewing, refactoring, or modernizing CSS/Tailwind. Apply a policy-first approach: semantic tokens, explicit cascade order, static CSS, intrinsic component layouts, accessible state, measured performance, and progressive enhancement backed by current sources.

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 a codebase: lines of code, language mix, and symbol lookups.

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 viral referral programs with incentive structures, sharing mechanics, and tracking. Covers one-sided, two-sided, tiered, and milestone models with viral coefficient calculations. Use when someone wants word-of-mouth growth, viral loops, invite programs, or says 'referral', 'viral loop', 'word of mouth', 'invite program', 'refer a friend', 'growth loop', 'referral incentive', 'affiliate program', 'ambassador program', 'get users to invite friends', or 'organic growth'. Even casual mentions like 'how do I get my users to spread the word' or 'can users invite others' should trigger this. Includes anti-fraud measures and copy templates for both referrer and referee. Use this whenever user-driven acquisition or viral mechanics are discussed.

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Generate Apple App Store screenshot pages as a Next.js app with html-to-image export at required resolutions. Screenshots are advertisements, not documentation — every screenshot sells one idea. Use when building App Store screenshots, generating exportable marketing screenshots for iOS apps, or creating programmatic screenshot generators. Triggers on 'app store screenshots', 'App Store', 'screenshot generator', 'iOS screenshots', 'marketing screenshots', 'phone mockup', 'ASO screenshots', 'app store assets', 'app listing', 'app store page', or 'app preview images'. Also use when someone is about to submit an app and needs store assets, or when they say 'make my app store page look good'. Includes iPhone and iPad mockup components with pre-measured dimensions.

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Builds, measures, and improves an Answer Engine Optimization program across ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews, and Copilot. Use for AEO, GEO, LLMO, AI search visibility, AI citations, brand mentions, answer-engine monitoring, llms.txt, AI crawler audits, or questions like 'what does AI say about us?' and 'why are competitors cited instead of us?'. Produces a durable evidence report and prioritized gameplan, not a one-shot content checklist.

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 setting up a secure email inbox for any AI agent — configuring inbound email via Resend, webhooks, tunneling for local development, and implementing security measures to prevent prompt injection attacks. Also use when someone mentions 'agent email', 'bot inbox', 'receive emails for agent', 'agent webhook', 'email security for AI', 'prompt injection via email', 'inbound email for bot', or wants their AI agent to receive and respond to emails securely.

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。

Ongoing link-building campaigns that raise domain authority and expand a site's KD ceiling beyond what on-page work alone can earn. Use when the user mentions 'off-page SEO', 'link building', 'directory link building', 'backlink campaign', 'listicle placements', 'guest posts', 'outreach targets', 'get listed in listicles', or asks 'how do I get more domain authority'. Generates a prioritized, researched target list across directories, listicles, and guest-post candidates — does NOT submit anything on the user's behalf. Pairs with `mktg-backlink-prospector` agent for parallel research. For a launch-day push (submit to directories, Product Hunt, BetaList), see /startup-launcher. For measured backlink data — referring domains, backlink counts, rank tracking — see /openseo. Distinct from `seo-machine` (on-page programmatic) and `seo-audit` (technical diagnostic). The off-page half of the SEO 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。

Map who is winning an SEO market with measured data — recurring SERP domains, organic footprints, winning content themes, backlink authority, and the gaps worth attacking. Use this skill for market-level questions ("who ranks for X", "how competitive is this space", "where are the SEO openings") before committing to keyword targets or content themes. For one named competitor, use openseo-competitor-analysis instead. Triggers: "competitive landscape seo", "who ranks for", "seo market map", "how competitive is", "serp competitors".

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。

Measures the work in a code repo as deeds (caps, fixes and tends) by running the bundled `deeds` CLI over the commit history, for this repo or any GitHub repo, over any time window. Reports totals, a week-by-week trend, a per-author breakdown and the named caps, and explains what the numbers mean.

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。

Invoke only when the user explicitly asks to review code through the "single level of abstraction / layered error handling" lens — a function does only its own layer's business logic while errors are handled above or below. The agent reports detections, raw-count measurements, and move directions. Apply only when the user explicitly requests this lens.

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。

Intake for an autonomous research run (Arbor). Use when the user wants to optimize a metric in a repo over many isolated experiments — 'optimize this benchmark', 'improve the model F1 overnight', 'beat the leaderboard'. Discovers the repo/eval/splits, measures the baseline, confirms a Research Contract, then emits a ready-to-send /auto-research command. Does NOT start the run itself.

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 a dogfooding session for hyalo — build from source, exercise the CLI against real knowledgebases (own KB, MDN, GitHub Docs, VS Code docs), find bugs, verify recent fixes, assess UX, measure performance, and write a structured report. Use this skill whenever the user says /dogfood, "dogfood", "run a dogfood session", "test hyalo against real data", or wants to exercise hyalo on real knowledgebases to find issues.

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。