「program」の検索結果

Browser automation platform with 78 built-in scripts and full CLI. Turn any website into structured data with one command. Use when the user needs to scrape websites, extract data, automate browsers, run built-in scripts (Hacker News, GitHub trending, Bilibili, Zhihu, stock data, etc.), navigate pages, fill forms, take screenshots, or control Chrome programmatically. Triggers include "scrape", "extract data", "browser automation", "web data", "trending", "hot topics", "stock data", "run script", "open website", "fill form", "screenshot", "headless browser".

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。

Help users build sustainable growth engines by transitioning from accidental word-of-mouth to structured referral programs and viral product loops.

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。

Help users design and execute a modern SEO strategy that balances technical site health with programmatic content scaling and AI-driven discovery.

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。

Help users identify the most viable path into product management, assess their role fit, and execute a transition strategy through internal mobility, APM programs, or startup roles.

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。

Best-practice rulebook for a marketing site's pages, organized by page class — campaign landers, product pages, programmatic SEO trees, editorial. Use when writing or updating a landing/marketing page, wiring a campaign lander to an attribution identifier, changing site-wide nav/footer links, touching sitemap/robots/agent-readable surfaces, or auditing a public site's indexing, reachability, and conversion setup.

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。

Write warm, direct collaboration comments. Trigger: PR feedback, issue replies, reviews, Slack messages, or GitHub comments.

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。

Trigger: update skills, skill registry, actualizar skills, after skill changes. Index available skills by trigger and path.

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。

Trigger: RDD, receipt-driven development, review authority, receipt/lineage, correction/recovery, delivery gate/kill switch, bounded review defects. Guide work.

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 and triage GitHub issues from repository evidence. Trigger: issue creation, bug reports, feature requests, or issue approval.

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 commits as reviewable work units. Trigger: implementation, commit splitting, chained PRs, or keeping tests and docs with code.

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。

Trigger: improve skills, audit skills, refactor skills, skill quality. Audit and upgrade existing LLM-first skills.

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。

Trigger: PRs over 400 lines, stacked PRs, review slices. Split oversized changes into chained PRs that protect review focus.

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 Gentle AI harness discipline for Pi work: clarify first, track ODD work, use applicable test-first development by default, delegate when useful, and protect review workload.

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。

Trigger: judgment day, judgement day, dual review, adversarial review, juzgar. Run explicit blind dual review with at most two scoped fix/re-judgment rounds.

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。

Trigger: /skill-creation, skill creation, skill creator, create skill, new skill. Create LLM-first skills with valid frontmatter.

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 Gentle AI pull requests with issue-first checks. Trigger: creating, opening, or preparing PRs for review.

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。

Design docs that reduce cognitive load. Trigger: writing guides, READMEs, RFCs, onboarding, architecture, or review-facing docs.

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 generated or changed production code before it ships, using Clean Code, SOLID, DRY, KISS, YAGNI, and LLM-specific failure-mode checks in any programming language. Best used reactively after an agent writes, edits, refactors, or fixes code, before presenting, committing, or merging the result. Use when the user asks "review this PR", "is this safe to merge?", "make this cleaner", "audit this code", "refactor this", "fix this bug", or after a coding agent produced implementation code. Can also guide writing when explicitly invoked before a risky edit. Invoke it on your own initiative the moment you finish writing, editing, or refactoring non-trivial production code, before presenting or committing — don't wait to be asked. DO NOT USE for factual/conceptual questions, CI/tooling config, git workflow, running/debugging tests, pure architecture discussion, prose writing, data analysis, or test-code review (use test-guard).

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Use when an AEJ: Economic Policy manuscript's headline policy estimate needs to be shown stable and credible against specification, sample, inference, and identification threats. Organizes the robustness program by threat-to-the-policy-conclusion; it does not design the primary identification or write exhibits.

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 the credibility of the causal evaluation of a policy is the bottleneck for an AEJ: Economic Policy manuscript — DID/event study, IV, RDD/bunching, or RCT of a program. Stress-tests the quasi-experimental policy-evaluation design to the AEJ: Policy bar before exhibits are finalized; it does not build the welfare mapping or write exhibits.

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 the headline result of an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript must be shown stable across specification, sample, identification, and tuning choices. Builds the robustness program a macro referee will demand; it does not establish the primary identification or model (use aejmac-identification / aejmac-theory-model first).

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 positioning an ACM CCS submission against the security big-four and specialist literature, including arXiv preprints, prior CCS/S&P/USENIX/NDSS papers, concurrent disclosures, CVE and advisory records, and the attack-and-defense lineage that a SIGSAC program committee expects to see credited.

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 designing or auditing ACM CCS experiments, attack demonstrations, adaptive-attack defense evaluations, security measurements, baselines, overhead and cost reporting, ablations, and claim-to-evidence fit, with emphasis on evidence that survives an adversarial program committee rather than leaderboard wins.

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 revising an ACM CCS paper for a precise threat model, calibrated attack/defense claims, 12-page ACM sigconf compression, double-blind wording, adaptive-evaluation honesty, and the security house style that survives an adversarial SIGSAC program committee.

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。