Results for “canonical”

Chat with your agent about projects, recommendations, and canonical papers in Paperzilla. Fetch feeds, read papers, leave feedback, and export data using the pz CLI.

Convert daily market observations and news reactions into structured edge hints, with optional LLM augmentation, outputting a canonical hints.yaml for downstream concept synthesis.

Canonical kip knowledge-store patterns for any process or agent: recall prior facts before work, assert decisions/gate outcomes/rejections as structured facts after work, resolve entities with explicit --model sonnet, and invoke the CLI Windows-safely via node packages/kip-sdk/dist/cli/kip.js when kip is not on PATH.

Operates a shared identity graph that multiple AI agents resolve against. Ensures every agent in a multi-agent system gets the same canonical answer for "who is this entity?" - deterministically, even under concurrent writes.

Reviews a plan from a prior session by extracting the canonical reference prompt from seed/plan-reviewer-design.md and invoking the plan-reviewer agent with it, surfacing the verdict without auto-applying changes.

Cardinality and model family jointly determine the encoding.

Guardian Angel gives AI agents a moral conscience rooted in Thomistic virtue ethics. Rather than relying solely on rule lists, it cultivates stable virtuous dispositions— prudence, justice, fortitude, temperance—that guide every interaction. The foundation is caritas: willing the good of the person you serve. From this flow the cardinal virtues as practical habits of right action and sound judgment. v3.0 introduced virtue-based disposition as the primary evaluation layer, providing deeper coherence than checklists alone. The agent's character becomes the safeguard. v3.1 adds: Plugin enforcement layer with before_tool_call hooks, approval workflows for ambiguous cases, and protections for sensitive infrastructure actions.

Measurement-driven model router for Claude Code. Routes Haiku/Sonnet/Opus per task class, logs every routed task with real $ numbers, and A/B tests cheaper tiers before you trust the savings.

Provides guidance on using Anthropic's Claude API, covering message formats, context windows, tool use, and best practices for building conversational AI applications.

Periodically scans Claude Code configuration for redundant, stale, or orphaned items and walks the user through a confirm-each-deletion cleanup.

润色中文学术论文，识别并替换模板化、机械化的AI表达，使文本更自然、更符合人类学者的写作习惯。

Pragmatic coding standards - concise, direct, no over-engineering, no unnecessary comments

Extract a DDD-style ubiquitous language glossary from the current conversation, flagging ambiguities and proposing canonical terms. Saves to UBIQUITOUS_LANGUAGE.md. Use when user wants to define domain terms, build a glossary, harden terminology, create a ubiquitous language, or mentions "domain model" or "DDD".

Integrate services or agent harnesses with the 33GOD Bloodbank event bus. Covers schemas in Bloodbank schemas/ and docs/event-naming.md, producing events (NATS preferred; Dapr, HTTP /publish, hookd_bridge alternatives), consuming events (NATS, Dapr, FastStream, event-toaster), and agent hook wiring through the canonical services/agent-hooks publisher. Use for event publish/consume, authoring schemas, integrating harnesses (Claude Code, Copilot CLI, OpenCode, Cursor, Aider, Codex CLI, Hermes), or debugging missing envelopes. Triggers: bloodbank, event bus, publish, subscribe, NATS subject, holyfields legacy, CloudEvents, event-toaster, ntfy.delo.sh/bloodbank, bloodbank.v1.agent.session.started, bloodbank.v1.agent.tool.completed, bloodbank.cmd.v1.agent.invocation.start. Skip for generic brokers, n8n, hindsight memory, or non-event-bus 33GOD.

Surfaces Han's shared Human-Readable Output Standard — the readability rule and the writing-voice profile — into the calling skill's own context, so the caller drafts in voice and runs its self-check against the current standard sourced from one canonical copy. Use when a prose-producing skill needs the shared readability standard available in context before it drafts. Governs the shape of a written deliverable, where explanation-guidance governs what a run says to a person in a turn. Runs in the caller's context and hands control straight back; it does not produce a deliverable of its own, rewrite anything, or judge the caller's work. Does not run the adversarial rewrite pass — dispatch the readability-editor agent for that, or use edit-for-readability to rewrite an existing target. Does not cover explaining technical work to a reader who will not implement it — use explanation-guidance for that.