839 件 ・ 関連度順
概要と使いどころ
Kalshi daily and hourly range markets on crypto prices (BTC, ETH) and equity indices (S&P 500, Nasdaq-100) — bracket structure, Gaussian P(YES) modeling on price/vol, close-offset decision timing, longshot-sell edge with honest evidence bounds
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。
Implement evaluated findings: load code-style rules, apply the accepted fixes, then close out the change with full or quick QA. Use after $evaluate-findings has tagged findings and they need to be implemented, or when the user asks to "resolve findings", "apply evaluated findings", or "dispatch findings to implementation".
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。
Implement evaluated findings: load code-style rules, apply the accepted fixes, then close out the change with full or quick QA. Use after /evaluate-findings has tagged findings and they need to be implemented, or when the user asks to "resolve findings", "apply evaluated findings", or "dispatch findings to implementation".
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。
Consolidate IBRK trade CSV files from a directory into a summary report. Groups trades by symbol, underlying, date, strike, buy/sell, and open/close. Outputs both markdown and CSV.
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。
Remove the discourse-level (structural) signs of AI writing that survive surface editing: stated lessons and moral-of-the-story closers, tidy single-track arcs, embodied-emotion performance ("chest tightened"), vague allusions instead of named references, unbroken linear structure, and shape convergence across pieces. Grounded in the StoryScope study (Russell et al. 2026): narrative structure alone detects AI text at 93.2% F1, and professional stylistic rewriting moved detection only 1.6 points. Use as the SECOND pass after the humanizer skill (which handles words and phrasing) whenever writing or revising LinkedIn posts, course lessons, blog posts, essays, newsletters, or emails that must read as human. Triggers: "humanize", "de-slop", "AI tells", "make this sound human", "structural pass", "deep humanize".
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。
Provides a structured 8-phase workflow for resolving GitHub issues in Claude Code. Covers fetching issue details, analyzing requirements, implementing solutions, verifying correctness, performing code review, committing changes, and creating pull requests. Use when user asks to resolve, implement, work on, fix, or close a GitHub issue, or references an issue URL or number for implementation.
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。
When the user needs to plan or execute a product launch — including pre-launch content sequences, launch funnels, JV/affiliate launches, cart open/close strategy, or evergreen launch funnels.
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。
Manage construction punch lists for project closeout. Track deficiencies, assign corrections, and monitor completion status.
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。
Digital punch list management for construction project closeout. Track deficiencies, assign corrections, photo documentation, and completion verification.
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。
Manage as-built documentation for project closeout. Track drawing markups, coordinate updates, and verify completeness.
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。
Tendering and reporting in OpenConstructionERP: prepare tender BOQs, compare bids, manage risks, and generate reports (weekly, monthly, closeout). Use when a project goes to tender or needs reporting.
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。
Automate as-built documentation and digital handover for construction. Compile project records, generate O&M manuals, create asset databases, and ensure complete project closeout.
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。
Turns a screenshot, mockup, Figma export, or photo of a UI into working front-end code - React + Tailwind CSS v4 by default, or Next.js, Vue, or plain HTML/CSS - matching layout, spacing, colors, and typography, then renders the result and compares it against the image to close visual gaps. Use whenever the user shares an image of a website, app screen, dashboard, component, or wireframe and wants it built, cloned, recreated, or "made real", or says "code this up", "build this design", or "match this screenshot".
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。
Builds a custom lead scoring model for a business. Takes ICP definition, historical win/loss data, CRM export. Analyzes which attributes correlate with closed-won deals. Generates lead-scoring-model.md with scoring dimensions, point values, thresholds, CRM implementation guide, and validation methodology. Can also score a batch of current leads against the model.
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 pipeline health, identify stalled deals, predict close probability, and suggest actions to move deals forward. Improves forecast accuracy and prevents revenue leakage. Use when deals get stuck or forecast accuracy is poor.
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。
Score deal velocity based on email response times, meeting frequency, and stakeholder engagement. Predict which deals will close vs stall.
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。
Cleans up CRM data from HubSpot, Salesforce, Pipedrive, Zoho, Close, Attio, or any CRM CSV export. Finds duplicate contacts and companies with fuzzy matching (not just exact email), normalizes emails, phones to E.164, names, domains, states, and countries, flags stale, invalid, role, and test records, finds broken contact-company associations, and produces a reviewable merge plan plus import-ready files without touching the live CRM. Use this skill whenever the user wants to dedupe, find or merge duplicate contacts, merge companies or accounts, clean up a CRM, prep a CRM migration or re-import, or says the CRM export is a mess. Also use it when they mention only one symptom, like "the same person is in here three times", "half our leads are junk", or "our company records don't match".
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。
Improves a voice profile by learning from manual edits. Use after editing generated text to refine registers and close voice drift over time.
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 Diffusion Planner for nuPlan autonomous-driving trajectory generation: prepare model-ready data, train or resume the diffusion model, configure closed-loop planning, and add differentiable collision or classifier guidance.
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。
Post-engagement knowledge harvest AND harness retrospective - after a box/bugbounty/pentest/CTF is completed, first diff how the engagement was EXECUTED against the skills/hooks that governed it (what discipline was skipped) and improve the harness, then sweep the whole engagement for GENERIC reusable knowledge NOT already in wiki/ and land it via the leak-gated stage->promote pipeline. Use at close-out or when asked to "extract learnings", "what did we learn", "harvest lessons into wiki", "distill this engagement", "post-mortem into the wiki", "what did we do wrong", "improve the harness from this box".
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。
Carry out a released charter to a proven result: plan only as much as the work needs, do the work through the project's own workers, prove every success check with a recorded real run, get one independent review, and close honestly. Invoke explicitly as `$deliver <charter>` (Codex) or `/deliver <charter>` (Claude, Cursor), or the way your agent invokes a skill, or when a dispatch names you the executor of a charter.
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。
PL (Project Lead) role sub-skill for acceptance and close-audit. Loaded ONLY through the pl core routing table; NEVER auto-load from context, never invoke directly.
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 PL self-approvals, estimates, board drift, and close quality after a declared night shift. Loaded ONLY by the cos core via its routing table; NEVER auto-load from context or invoke directly.
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 privacy-preserving analytics systems using differential privacy, k-anonymity, l-diversity, and t-closeness. Covers privacy budget allocation with epsilon tracking, references Google DP library, OpenDP, and Apple PPML. Includes Python differential privacy implementation for GDPR-compliant statistical 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。