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Create a new built-in evlog adapter to send wide events to an external observability platform. Use when adding a new drain adapter (e.g., for Datadog, Sentry, Loki, Elasticsearch, etc.) to the evlog package. Covers source code, build config, package exports, tests, and all documentation.
本文档教你（或 Agent）如何为一个新网站编写 site adapter。
Complete adapter lifecycle assistant for LimaCharlie. Supports External Adapters (cloud-managed), Cloud Sensors (SaaS/cloud integrations), and On-prem USP adapters. Dynamically researches adapter types from local docs and GitHub usp-adapters repo. Creates, validates, deploys, and troubleshoots adapter configurations. Handles parsing rules (Grok, regex), field mappings, credential setup, and multi-adapter configs. Use when setting up new data sources (Okta, S3, Azure Event Hub, syslog, webhook, etc.), troubleshooting ingestion issues, or managing adapter deployments.
Technical guide for creating a new Paperclip agent adapter. Use when building a new adapter package, adding support for a new AI coding tool (e.g. a new CLI agent, API-based agent, or custom process), or when modifying the adapter system. Covers the required interfaces, module structure, registration points, and conventions derived from the existing claude-local and codex-local adapters. --- # Creating a Paperclip Agent Adapter An adapter bridges Paperclip's orchestration layer to a specific AI agent runtime (Claude Code, Codex CLI, a custom process, an HTTP endpoint, etc.). Each adapter is a self-contained package that provides implementations for **three consumers**: the server, the UI, and the CLI. --- ## 1. Architecture Overview ``` packages/adapters/<name>/ src/ index.ts # Shared metadata (type, label, models, agentConfigurationDoc) server/ index.ts # Server exports: execute, sessionCodec, parse helpers execute.ts # Core execution logic (AdapterExecutionContext -> AdapterExecutionResult) parse.ts # Stdout/result parsing for the agent's output format ui/ index.ts # UI exports: parseStdoutLine, buildConfig parse-stdout.ts # Line-by-line stdout -> TranscriptEntry[] for the run viewer build-config.ts # CreateConfigValues -> adapterConfig JSON for agent creation form cli/ index.ts # CLI exports: formatStdoutEvent format-event.ts # Colored terminal output for `paperclipai run --watch` package.json tsconfig.json ```
Validate ethers and viem adapter parity for zksync-js when core or adapter behavior changes. Detect mismatches, enforce documented exceptions, and protect core-to-adapter contract consistency.
skill-sample/ ├─ SKILL.md ⭐ Required: skill entry doc (purpose / usage / examples / deps) ├─ manifest.sample.json ⭐ Recommended: machine-readable metadata (index / validation / autofill) ├─ LICENSE.sample ⭐ Recommended: license & scope (open source / restriction / commercial) ├─ scripts/ │ └─ example-run.py ✅ Runnable example script for quick verification ├─ assets/ │ ├─ example-formatting-guide.md 🧩 Output conventions: layout / structure / style │ └─ example-template.tex 🧩 Templates: quickly generate standardized output └─ references/ 🧩 Knowledge base: methods / guides / best practices ├─ example-ref-structure.md 🧩 Structure reference ├─ example-ref-analysis.md 🧩 Analysis reference └─ example-ref-visuals.md 🧩 Visual reference
More Agent Skills specs Anthropic docs: https://agentskills.io/home
├─ ⭐ Required: YAML Frontmatter (must be at top) │ ├─ ⭐ name : unique skill name, follow naming convention │ └─ ⭐ description : include trigger keywords for matching │ ├─ ✅ Optional: Frontmatter extension fields │ ├─ ✅ license : license identifier │ ├─ ✅ compatibility : runtime constraints when needed │ ├─ ✅ metadata : key-value fields (author/version/source_url...) │ └─ 🧩 allowed-tools : tool whitelist (experimental) │ └─ ✅ Recommended: Markdown body (progressive disclosure) ├─ ✅ Overview / Purpose ├─ ✅ When to use ├─ ✅ Step-by-step ├─ ✅ Inputs / Outputs ├─ ✅ Examples ├─ 🧩 Files & References ├─ 🧩 Edge cases ├─ 🧩 Troubleshooting └─ 🧩 Safety notes
Skill files are scattered across GitHub and communities, difficult to search, and hard to evaluate. SkillWink organizes open-source skills into a searchable, filterable library you can directly download and use.
We provide keyword search, version updates, multi-metric ranking (downloads / likes / comments / updates), and open SKILL.md standards. You can also discuss usage and improvements on skill detail pages.
Quick Start:
Import/download skills (.zip/.skill), then place locally:
~/.claude/skills/ (Claude Code)
~/.codex/skills/ (Codex CLI)
One SKILL.md can be reused across tools.
Everything you need to know: what skills are, how they work, how to find/import them, and how to contribute.
A skill is a reusable capability package, usually including SKILL.md (purpose/IO/how-to) and optional scripts/templates/examples.
Think of it as a plugin playbook + resource bundle for AI assistants/toolchains.
Skills use progressive disclosure: load brief metadata first, load full docs only when needed, then execute by guidance.
This keeps agents lightweight while preserving enough context for complex tasks.
Use these three together:
Note: file size for all methods should be within 10MB.
Typical paths (may vary by local setup):
One SKILL.md can usually be reused across tools.
Yes. Most skills are standardized docs + assets, so they can be reused where format is supported.
Example: retrieval + writing + automation scripts as one workflow.
Some skills come from public GitHub repositories and some are uploaded by SkillWink creators. Always review code before installing and own your security decisions.
Most common reasons:
We try to avoid that. Use ranking + comments to surface better skills:
