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CLIパーサーの新しい記事を書きました。`--reporter`の値によって`--output-file`が必須になったり禁止になったり…そういう関係、型で表現できたら楽じゃないですか?

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    Fedify 1.10.0: Observability foundations for the future debug dashboard Fedify is a #TypeScript framework for building #ActivityPub servers that participate in the #fediverse. It reduces the complexity and boilerplate typically required for ActivityPub implementation while providing comprehensive federation capabilities. We're excited to announce #Fedify 1.10.0, a focused release that lays critical groundwork for future debugging and observability features. Released on December 24, 2025, this version introduces infrastructure improvements that will enable the upcoming debug dashboard while maintaining full backward compatibility with existing Fedify applications. This release represents a transitional step toward Fedify 2.0.0, introducing optional capabilities that will become standard in the next major version. The changes focus on enabling richer observability through OpenTelemetry enhancements and adding prefix scanning capabilities to the key–value store interface. Enhanced OpenTelemetry instrumentation Fedify 1.10.0 significantly expands OpenTelemetry instrumentation with span events that capture detailed ActivityPub data. These enhancements enable richer observability and debugging capabilities without relying solely on span attributes, which are limited to primitive values. The new span events provide complete activity payloads and verification status, making it possible to build comprehensive debugging tools that show the full context of federation operations: activitypub.activity.received event on activitypub.inbox span — records the full activity JSON, verification status (activity verified, HTTP signatures verified, Linked Data signatures verified), and actor information activitypub.activity.sent event on activitypub.send_activity span — records the full activity JSON and target inbox URL activitypub.object.fetched event on activitypub.lookup_object span — records the fetched object's type and complete JSON-LD representation Additionally, Fedify now instruments previously uncovered operations: activitypub.fetch_document span for document loader operations, tracking URL fetching, HTTP redirects, and final document URLs activitypub.verify_key_ownership span for cryptographic key ownership verification, recording actor ID, key ID, verification result, and the verification method used These instrumentation improvements emerged from work on issue #234 (Real-time ActivityPub debug dashboard). Rather than introducing a custom observer interface as originally proposed in #323, we leveraged Fedify's existing OpenTelemetry infrastructure to capture rich federation data through span events. This approach provides a standards-based foundation that's composable with existing observability tools like Jaeger, Zipkin, and Grafana Tempo. Distributed trace storage with FedifySpanExporter Building on the enhanced instrumentation, Fedify 1.10.0 introduces FedifySpanExporter, a new OpenTelemetry SpanExporter that persists ActivityPub activity traces to a KvStore. This enables distributed tracing support across multiple nodes in a Fedify deployment, which is essential for building debug dashboards that can show complete request flows across web servers and background workers. The new @fedify/fedify/otel module provides the following types and interfaces: import { MemoryKvStore } from "@fedify/fedify"; import { FedifySpanExporter } from "@fedify/fedify/otel"; import { BasicTracerProvider, SimpleSpanProcessor, } from "@opentelemetry/sdk-trace-base"; const kv = new MemoryKvStore(); const exporter = new FedifySpanExporter(kv, { ttl: Temporal.Duration.from({ hours: 1 }), }); const provider = new BasicTracerProvider(); provider.addSpanProcessor(new SimpleSpanProcessor(exporter)); The stored traces can be queried for display in debugging interfaces: // Get all activities for a specific trace const activities = await exporter.getActivitiesByTraceId(traceId); // Get recent traces with summary information const recentTraces = await exporter.getRecentTraces({ limit: 100 }); The exporter supports two storage strategies depending on the KvStore capabilities. When the list() method is available (preferred), it stores individual records with keys like [prefix, traceId, spanId]. When only cas() is available, it uses compare-and-swap operations to append records to arrays stored per trace. This infrastructure provides the foundation for implementing a comprehensive debug dashboard as a custom SpanExporter, as outlined in the updated implementation plan for issue #234. Optional list() method for KvStore interface Fedify 1.10.0 adds an optional list() method to the KvStore interface for enumerating entries by key prefix. This method enables efficient prefix scanning, which is useful for implementing features like distributed trace storage, cache invalidation by prefix, and listing related entries. interface KvStore { // ... existing methods list?(prefix?: KvKey): AsyncIterable<KvStoreListEntry>; } When the prefix parameter is omitted or empty, list() returns all entries in the store. This is useful for debugging and administrative purposes. All official KvStore implementations have been updated to support this method: MemoryKvStore — filters in-memory keys by prefix SqliteKvStore — uses LIKE query with JSON key pattern PostgresKvStore — uses array slice comparison RedisKvStore — uses SCAN with pattern matching and key deserialization DenoKvStore — delegates to Deno KV's built-in list() API WorkersKvStore — uses Cloudflare Workers KV list() with JSON key prefix pattern While list() is currently optional to give existing custom KvStore implementations time to add support, it will become a required method in Fedify 2.0.0 (tracked in issue #499). This migration path allows implementers to gradually adopt the new capability throughout the 1.x release cycle. The addition of list() support was implemented in pull request #500, which also included the setup of proper testing infrastructure for WorkersKvStore using Vitest with @cloudflare/vitest-pool-workers. NestJS 11 and Express 5 support Thanks to a contribution from Cho Hasang (@crohasang@hackers.pub), the @fedify/nestjs package now supports NestJS 11 environments that use Express 5. The peer dependency range for Express has been widened to ^4.0.0 || ^5.0.0, eliminating peer dependency conflicts in modern NestJS projects while maintaining backward compatibility with Express 4. This change, implemented in pull request #493, keeps the workspace catalog pinned to Express 4 for internal development and test stability while allowing Express 5 in consuming applications. What's next Fedify 1.10.0 serves as a stepping stone toward the upcoming 2.0.0 release. The optional list() method introduced in this version will become required in 2.0.0, simplifying the interface contract and allowing Fedify internals to rely on prefix scanning being universally available. The enhanced #OpenTelemetry instrumentation and FedifySpanExporter provide the foundation for implementing the debug dashboard proposed in issue #234. The next steps include building the web dashboard UI with real-time activity lists, filtering, and JSON inspection capabilities—all as a separate package that leverages the standards-based observability infrastructure introduced in this release. Depending on the development timeline and feature priorities, there may be additional 1.x releases before the 2.0.0 migration. For developers building custom KvStore implementations, now is the time to add list() support to prepare for the eventual 2.0.0 upgrade. The implementation patterns used in the official backends provide clear guidance for various storage strategies. Acknowledgments Special thanks to Cho Hasang (@crohasang@hackers.pub) for the NestJS 11 compatibility improvements, and to all community members who provided feedback and testing for the new observability features. For the complete list of changes, bug fixes, and improvements, please refer to the CHANGES.md file in the repository. #fedidev #release
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    When I started building Fedify, an ActivityPub server framework, I ran into a problem that surprised me: I couldn't figure out how to add logging. Not because logging is hard—there are dozens of mature logging libraries for JavaScript. The problem was that they're primarily designed for applications, not for libraries that want to stay unobtrusive. I wrote about this a few months ago, and the response was modest—some interest, some skepticism, and quite a bit of debate about whether the post was AI-generated. I'll be honest: English isn't my first language, so I use LLMs to polish my writing. But the ideas and technical content are mine. Several readers wanted to see a real-world example rather than theory. The problem: existing loggers assume you're building an app Fedify helps developers build federated social applications using the ActivityPub protocol. If you've ever worked with federation, you know debugging can be painful. When an activity fails to deliver, you need to answer questions like: Did the HTTP request actually go out? Was the signature generated correctly? Did the remote server reject it? Why? Was there a problem parsing the response? These questions span multiple subsystems: HTTP handling, cryptographic signatures, JSON-LD processing, queue management, and more. Without good logging, debugging turns into guesswork. But here's the dilemma I faced as a library author: if I add verbose logging to help with debugging, I risk annoying users who don't want their console cluttered with Fedify's internal chatter. If I stay silent, users struggle to diagnose issues. I looked at the existing options. With winston or Pino, I would have to either: Configure a logger inside Fedify (imposing my choices on users), or Ask users to pass a logger instance to Fedify (adding boilerplate) There's also debug, which is designed for this use case. But it doesn't give you structured, level-based logs that ops teams expect—and it relies on environment variables, which some runtimes like Deno restrict by default for security reasons. None of these felt right. So I built LogTape—a logging library designed from the ground up for library authors. And Fedify became its first real user. The solution: hierarchical categories with zero default output The key insight was simple: a library should be able to log without producing any output unless the application developer explicitly enables it. Fedify uses LogTape's hierarchical category system to give users fine-grained control over what they see. Here's how the categories are organized: Category What it logs ["fedify"] Everything from the library ["fedify", "federation", "inbox"] Incoming activities ["fedify", "federation", "outbox"] Outgoing activities ["fedify", "federation", "http"] HTTP requests and responses ["fedify", "sig", "http"] HTTP Signature operations ["fedify", "sig", "ld"] Linked Data Signature operations ["fedify", "sig", "key"] Key generation and retrieval ["fedify", "runtime", "docloader"] JSON-LD document loading ["fedify", "webfinger", "lookup"] WebFinger resource lookups …and about a dozen more. Each category corresponds to a distinct subsystem. This means a user can configure logging like this: await configure({ sinks: { console: getConsoleSink() }, loggers: [ // Show errors from all of Fedify { category: "fedify", sinks: ["console"], lowestLevel: "error" }, // But show debug info for inbox processing specifically { category: ["fedify", "federation", "inbox"], sinks: ["console"], lowestLevel: "debug" }, ], }); When something goes wrong with incoming activities, they get detailed logs for that subsystem while keeping everything else quiet. No code changes required—just configuration. Request tracing with implicit contexts The hierarchical categories solved the filtering problem, but there was another challenge: correlating logs across async boundaries. In a federated system, a single user action might trigger a cascade of operations: fetch a remote actor, verify their signature, process the activity, fan out to followers, and so on. When something fails, you need to correlate all the log entries for that specific request. Fedify uses LogTape's implicit context feature to automatically tag every log entry with a requestId: await configure({ sinks: { file: getFileSink("fedify.jsonl", { formatter: jsonLinesFormatter }) }, loggers: [ { category: "fedify", sinks: ["file"], lowestLevel: "info" }, ], contextLocalStorage: new AsyncLocalStorage(), // Enables implicit contexts }); With this configuration, every log entry automatically includes a requestId property. When you need to debug a specific request, you can filter your logs: jq 'select(.properties.requestId == "abc-123")' fedify.jsonl And you'll see every log entry from that request—across all subsystems, all in order. No manual correlation needed. The requestId is derived from standard headers when available (X-Request-Id, Traceparent, etc.), so it integrates naturally with existing observability infrastructure. What users actually see So what does all this configuration actually mean for someone using Fedify? If a Fedify user doesn't configure LogTape at all, they see nothing. No warnings about missing configuration, no default output, and minimal performance overhead—the logging calls are essentially no-ops. For basic visibility, they can enable error-level logging for all of Fedify with three lines of configuration. When debugging a specific issue, they can enable debug-level logging for just the relevant subsystem. And if they're running in production with serious observability requirements, they can pipe structured JSON logs to their monitoring system with request correlation built in. The same library code supports all these scenarios—whether the user is running on Node.js, Deno, Bun, or edge functions, without extra polyfills or shims. The user decides what they need. Lessons learned Building Fedify with LogTape taught me a few things: Design your categories early. The hierarchical structure should reflect how users will actually want to filter logs. I organized Fedify's categories around subsystems that users might need to debug independently. Use structured logging. Properties like requestId, activityId, and actorId are far more useful than string interpolation when you need to analyze logs programmatically. Implicit contexts turned out to be more useful than I expected. Being able to correlate logs across async boundaries without passing context manually made debugging distributed operations much easier. When a user reports that activity delivery failed, I can give them a single jq command to extract everything relevant. Trust your users. Some library authors worry about exposing too much internal detail through logs. I've found the opposite—users appreciate being able to see what's happening when they need to. The key is making it opt-in. Try it yourself If you're building a library and struggling with the logging question—how much to log, how to give users control, how to avoid being noisy—I'd encourage you to look at how Fedify does it. The Fedify logging documentation explains everything in detail. And if you want to understand the philosophy behind LogTape's design, my earlier post covers that. LogTape isn't trying to replace winston or Pino for application developers who are happy with those tools. It fills a different gap: logging for libraries that want to stay out of the way until users need them. If that's what you're looking for, it might be a better fit than the usual app-centric loggers.
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    Optique 0.6.0 is here, bringing intelligent shell completion to your type-safe command-line applications. This release introduces built-in completion support for Bash, zsh, fish, PowerShell, and Nushell, making your CLIs more discoverable and user-friendly—all without sacrificing type safety or requiring duplicate definitions. For those new to [Optique]: it's a TypeScript CLI parser library that takes a fundamentally different approach from traditional configuration-based parsers. Instead of describing your CLI with configuration objects, you compose parsers from small, type-safe functions. TypeScript automatically infers the exact types of your parsed data, ensuring compile-time safety while the parser structure itself provides runtime validation. Think of it as bringing the composability of parser combinators (inspired by Haskell's optparse-applicative) together with the type safety of TypeScript's type system. Shell completion that just works The standout feature of this release is comprehensive shell completion support. Unlike many CLI frameworks that require separate completion definitions, Optique's completion system leverages the same parser structure used for argument parsing. This means your completion suggestions automatically stay synchronized with your CLI's actual behavior—no duplicate definitions, no manual maintenance. import { object } from "@optique/core/constructs"; import { argument, option } from "@optique/core/primitives"; import { string, choice } from "@optique/core/valueparser"; import { run } from "@optique/run"; const parser = object({ format: option("-f", "--format", choice(["json", "yaml", "xml"])), output: option("-o", "--output", string({ metavar: "FILE" })), verbose: option("-v", "--verbose"), input: argument(string({ metavar: "INPUT" })), }); // Enable completion with a single option const config = run(parser, { completion: "both" }); Users can now press Tab to get intelligent suggestions: myapp <TAB> # Shows available commands and options myapp --format <TAB> # Shows: json, yaml, xml myapp --format=<TAB> # Same suggestions with equals syntax myapp -<TAB> # Shows: -f, -o, -v, and other short options Setting up completion is straightforward. Users generate a completion script for their shell and source it: # Bash myapp completion bash > ~/.bashrc.d/myapp.bash source ~/.bashrc.d/myapp.bash # zsh myapp completion zsh > ~/.zsh/completions/_myapp # fish myapp completion fish > ~/.config/fish/completions/myapp.fish # PowerShell myapp completion pwsh > myapp-completion.ps1 . ./myapp-completion.ps1 # Nushell myapp completion nu | save myapp-completion.nu source myapp-completion.nu The completion system works automatically with all Optique parser types. When you use choice() value parsers, the available options become completion suggestions. When you use path() parsers, file system completion kicks in with proper handling of extensions and file types. Subcommands, options, and arguments all provide context-aware suggestions. What makes Optique's completion special is that it leverages the same parser structure used for argument parsing. Every parser has an optional suggest() method that provides context-aware suggestions based on the current input. Parser combinators like object() and or() automatically aggregate suggestions from their constituent parsers, ensuring your completion logic stays in your TypeScript code where it benefits from type safety and testing. Optique handles the differences between shells transparently. Bash uses the complete command with proper handling of word splitting, zsh leverages its powerful compdef system with completion descriptions, fish provides tab-separated format with automatic file type detection, PowerShell uses Register-ArgumentCompleter with AST-based parsing, and Nushell integrates with its external completer system. For file and directory completions, Optique delegates to each shell's native file completion system, ensuring proper handling of spaces, symlinks, and platform-specific path conventions. Custom completion suggestions For domain-specific value parsers, you can implement custom completion logic that provides intelligent suggestions based on your application's needs: import type { ValueParser, ValueParserResult } from "@optique/core/valueparser"; import type { Suggestion } from "@optique/core/parser"; import { message } from "@optique/core/message"; function httpMethod(): ValueParser<string> { const methods = ["GET", "POST", "PUT", "DELETE", "PATCH", "HEAD", "OPTIONS"]; return { metavar: "METHOD", parse(input: string): ValueParserResult<string> { const method = input.toUpperCase(); if (methods.includes(method)) { return { success: true, value: method }; } return { success: false, error: message`Invalid HTTP method: ${input}. Valid methods: ${methods.join(", ")}.`, }; }, format(value: string): string { return value; }, *suggest(prefix: string): Iterable<Suggestion> { for (const method of methods) { if (method.toLowerCase().startsWith(prefix.toLowerCase())) { yield { kind: "literal", text: method, description: message`HTTP ${method} request method` }; } } }, }; } The built-in value parsers also provide intelligent suggestions. For instance, the locale() parser suggests common locale identifiers, the url() parser offers protocol completions when configured with allowedProtocols, and the timezone parsers from @optique/temporal use Intl.supportedValuesOf() for dynamic timezone suggestions. Enhanced command documentation This release also introduces new documentation capabilities for the command() parser. You can now provide separate brief and description texts, along with a footer for examples and additional information: import { command, object, constant } from "@optique/core/primitives"; import { message } from "@optique/core/message"; const deployCommand = command( "deploy", object({ action: constant("deploy"), // ... options }), { brief: message`Deploy application to production`, // Shown in command list description: message`Deploy the application to the production environment. This command handles database migrations, asset compilation, and cache warming automatically. It performs health checks before switching traffic to ensure zero-downtime deployment.`, // Shown in detailed help footer: message`Examples: myapp deploy --environment staging --dry-run myapp deploy --environment production --force For deployment documentation, see: https://docs.example.com/deploy` } ); The brief text appears when listing commands (like myapp help), while description provides detailed information when viewing command-specific help (myapp deploy --help or myapp help deploy). The footer appears at the bottom of the help text, perfect for examples and additional resources. Command-line example formatting To make help text and examples clearer, we've added a new commandLine() message term type. This displays command-line snippets with distinct cyan coloring in terminals, making it immediately clear what users should type: import { message, commandLine } from "@optique/core/message"; import { run } from "@optique/run"; const config = run(parser, { footer: message`Examples: ${commandLine("myapp --format json input.txt")} ${commandLine("myapp --format=yaml --output result.yml data.txt")} To enable shell completion: ${commandLine("myapp completion bash > ~/.bashrc.d/myapp.bash")} ${commandLine("source ~/.bashrc.d/myapp.bash")}`, completion: "both" }); These command examples stand out visually in help text, making it easier for users to understand how to use your CLI. Migration guide If you're already using Optique, adding completion support is straightforward: Update to Optique 0.6.0 Add the completion option to your run() configuration: // Before const config = run(parser, { help: "both" }); // After const config = run(parser, { help: "both", completion: "both" // Adds both 'completion' command and '--completion' option }); That's it! Your CLI now supports shell completion. The completion option accepts three modes: "command": Only the completion subcommand (e.g., myapp completion bash) "option": Only the --completion option (e.g., myapp --completion bash) "both": Both patterns work For custom value parsers, you can optionally add a suggest() method to provide domain-specific completions. Existing parsers continue to work without modification—they just won't provide custom suggestions beyond what the parser structure implies. Looking forward Shell completion has been one of the most requested features for Optique, and we're thrilled to deliver it in a way that maintains our core principles: type safety, composability, and zero duplication. Your parser definitions remain the single source of truth for both parsing and completion behavior. This release represents a significant step toward making Optique-based CLIs as user-friendly as they are developer-friendly. The completion system proves that we can provide sophisticated runtime features without sacrificing the compile-time guarantees that make Optique unique. We hope you find the new shell completion feature useful and look forward to seeing what you build with it! Getting started To start using Optique 0.6.0: deno add --jsr @optique/core@^0.6.0 @optique/run@^0.6.0 npm add @optique/core@^0.6.0 @optique/run@^0.6.0 pnpm add @optique/core@^0.6.0 @optique/run@^0.6.0 yarn add @optique/core@^0.6.0 @optique/run@^0.6.0 bun add @optique/core@^0.6.0 @optique/run@^0.6.0 For complete documentation, visit optique.dev. Check out the new shell completion guide for detailed setup instructions and advanced usage patterns. For bug reports and feature requests, please visit our GitHub repository.
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    :botkit: Introducing #BotKit: A #TypeScript framework for creating truly standalone #ActivityPub bots! Unlike traditional Mastodon bots, BotKit lets you build fully independent #fediverse bots that aren't constrained by platform limits. Create your entire bot in a single TypeScript file using our simple, expressive API. Currently #Deno-only, with Node.js & Bun support planned. Built on the robust @fedify@hollo.social foundation. https://botkit.fedify.dev/