BOLT: Basis of Lightning Technology (Lightning Network Specifications)
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英文目录This repository is created only for infosec professionals whom work day to day basis to equip ourself with uptodate skillset, We can daily contribute daily one hour for day to day tasks and work on problem statements daily, Please contribute by providing problem statements and solutions
A curated collection of reusable AI agent skills packaged as SKILL.md folders and ZIP bundles for OPAL-compatible environments.
LinkedIn-native long-form article and newsletter writing workflow for LinkedIn and Google-to-LinkedIn topic discovery, business-depth research, professional thought leadership, evidence-led drafting, final humanization, discussion design, SEO settings, auditing, and publish-ready packaging. Use when creating, outlining, researching, enriching, rewriting, humanizing, auditing, or packaging LinkedIn Articles, LinkedIn newsletters, LinkedIn long-form posts, LinkedIn thought leadership, LinkedIn B2B articles, LinkedIn 长文, LinkedIn 专栏, LinkedIn 话题调研, LinkedIn 商务内容, 去 AI 化编辑, or LinkedIn 发布包.
Bolt is an open source orchestration tool that automates the manual work it takes to maintain your infrastructure on an as-needed basis or as part of a greater orchestration workflow. It can be installed on your local workstation and connects directly to remote nodes with SSH or WinRM, so you are not required to install any agent software.
Read protocol for the main agent. Load on demand when answering ANY user question (per Iron Rule 4 in `using-karpathy-wiki/SKILL.md`). Defines the deterministic 6-step orientation ladder for finding wiki coverage of a question, when to inline-read vs spawn an Explore subagent vs fall through to web search, and the cite contract every wiki-grounded answer must satisfy.
Method for Predicting failures in Equipment using Sensor data. Sensors mounted on devices like IoT devices, Automated manufacturing like Robot arms, Process monitoring and Control equipment etc., collect and transmit data on a continuous basis which is Time stamped.
Perform a standards-based code review on a GitHub Pull Request, then post the findings as inline review comments and mark the PR as 'Requested changes'. Reads all of the agents files that exist in the repository under review (AGENTS.md, .agents-docs/, CLAUDE.md on the PR's base branch) — the repo's full documented standards, not just any agents files the PR happens to change — checks the diff against them plus general best practices, and gates every write behind explicit approval. Use this skill whenever the user wants to code-review a PR, review a pull request, check a PR against standards, request changes on a PR, or gives you a GitHub PR link and asks for a review. Also triggers on: 'review this PR', 'code review', 'review PR', 'check this pull request', 'request changes', 'review against our standards', or a bare github.com/.../pull/<n> URL with review intent. This is a review-only skill — it never approves, merges, closes, or pushes code.
SEO: Python script + shell script and cronjob to check ranks on a daily basis
**A end to end project - Powered by Django and Machine Learning** - This project aims to provide a web platform to predict the occurrences of disease on the basis of various symptoms. The user can select various symptoms and can find the diseases and consult to the doctor online.
Use when grading, reviewing, rewriting, or approving a Hermes Agent SOUL.md. Uses the SOUL.md field-guide research artifacts as the only normative source for what makes a good SOUL.md.
Build human adjudication / hand-labeling sheets from LLM-pipeline data without evidence truncation. Use when: (1) preparing a CSV/Excel sheet for a human to rule on cases an LLM classifier or rater panel judged, (2) a labeler reports "there is no information to label from" or cells look empty in Excel, (3) excerpt columns cluster at one exact length (e.g. all 1,500 chars — a hard truncation cap). Covers: full rating-basis recovery, Excel 32,767-char cell cap, multi-line CSV mangling, ruling dropdowns, companion text files.