telemetry — embeddings telemetry, community, embeddings, ide skills, frontmatter, markdown, parquet, semantic-search, validation, word2vec, Claude Code

v1.0.0

Sobre este Skill

Perfeito para Agentes de IA que necessitam de capacidades de telemetria avançadas para diretórios de markdown, como aqueles que utilizam Rust e Word2Vec. Telemetry and observability conventions. Apply when adding tracing, logging, metrics, or instrumentation to Rust code.

# Core Topics

edochi edochi
[1]
[0]
Updated: 3/17/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 7/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution
Review Score
7/11
Quality Score
33
Canonical Locale
en
Detected Body Locale
en

Perfeito para Agentes de IA que necessitam de capacidades de telemetria avançadas para diretórios de markdown, como aqueles que utilizam Rust e Word2Vec. Telemetry and observability conventions. Apply when adding tracing, logging, metrics, or instrumentation to Rust code.

Por que usar essa habilidade

Habilita os agentes a realizar inferência de esquema, validação de frontmatter e busca semântica em diretórios de markdown, utilizando tecnologias como Rust e Word2Vec para análise de conteúdo abrangente, e permitindo gerenciamento eficaz de níveis de log com níveis de erro, aviso e informação para telemetria e observabilidade robustas.

Melhor para

Perfeito para Agentes de IA que necessitam de capacidades de telemetria avançadas para diretórios de markdown, como aqueles que utilizam Rust e Word2Vec.

Casos de Uso Práticos for telemetry

Realizar inferência de esquema em diretórios de markdown
Validar frontmatter para consistência e precisão
Realizar busca semântica para recuperação eficiente de conteúdo

! Segurança e Limitações

  • Requer especificação de telemetria/observabilidade dedicada ou documento de convenções
  • Limitado a diretórios de markdown
  • Dependente de tecnologias como Rust e Word2Vec

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.
  • - The underlying skill quality score is below the review floor.

Source Boundary

The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.

Labs Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is telemetry?

Perfeito para Agentes de IA que necessitam de capacidades de telemetria avançadas para diretórios de markdown, como aqueles que utilizam Rust e Word2Vec. Telemetry and observability conventions. Apply when adding tracing, logging, metrics, or instrumentation to Rust code.

How do I install telemetry?

Run the command: npx killer-skills add edochi/mdvs/telemetry. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for telemetry?

Key use cases include: Realizar inferência de esquema em diretórios de markdown, Validar frontmatter para consistência e precisão, Realizar busca semântica para recuperação eficiente de conteúdo.

Which IDEs are compatible with telemetry?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for telemetry?

Requer especificação de telemetria/observabilidade dedicada ou documento de convenções. Limitado a diretórios de markdown. Dependente de tecnologias como Rust e Word2Vec.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add edochi/mdvs/telemetry. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use telemetry immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Imported Repository Instructions

The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.

Supporting Evidence

telemetry

Install telemetry, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

SKILL.md
Readonly
Imported Repository Instructions
The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.
Supporting Evidence

Telemetry & Observability Conventions

If the project has a dedicated telemetry/observability spec or conventions document, read it first — it takes precedence over the generic guidance below.

Log Levels

LevelWhen to UseExample
error!Unrecoverable failures requiring operator attentionExternal service call failed permanently
warn!Recoverable issues, degraded operationRetry triggered, fallback activated
info!Lifecycle events, operational milestonesServer started, connection established
debug!Internal state useful during developmentState machine transition details
trace!Per-frame, hot-path data (high volume)Frame encoded, bytes serialized

Instrumentation Depth

Not all code deserves the same instrumentation level. Choose based on the crate's role:

  • Hot-path code (serialization, framing, tight loops): trace! only, no spans. Spans add overhead that matters here.
  • Type definition crates (pure data types, no logic): trace! only if anything at all.
  • Core business logic (servers, handlers, state machines): Full instrumentation — #[instrument] + spans.
  • Client-facing code (SDKs, CLI flows): Full instrumentation for debuggability.

#[instrument] Rules

  • Use on async functions that represent logical operations or flow steps
  • Skip on hot-path synchronous functions (serialization, encoding, tight loops)
  • Always skip sensitive fields: tokens, keys, passwords, raw payloads
  • Include identifying fields that aid correlation (IDs, resource names)
  • Set appropriate level — default is INFO, use level = "debug" or level = "trace" for noisy functions

Structured Logging

Always use named fields, never string interpolation:

rust
1// Good: named fields — searchable, parseable 2trace!(payload_bytes = payload.len(), frame_bytes = buf.len(), "frame encoded"); 3 4// Bad: string interpolation — opaque to log aggregators 5trace!("frame encoded, payload={}, frame={}", payload.len(), buf.len());

Span Design

For request/message processing pipelines, use a three-tier span pattern:

  • Inbound span: one per received message/request (message type, size, sender ID)
  • Process span: business logic processing (operation type, affected resources)
  • Outbound span: one per response/forwarded message (recipient, payload size)

This gives visibility into where time is spent and enables per-hop latency analysis.

General Principles

  • Prefer tracing over log — structured spans enable distributed tracing
  • Log at the point of decision, not at every intermediate step
  • Include enough context to diagnose without reproducing: IDs, sizes, error details
  • Never log secrets, tokens, keys, or raw user data — even at trace! level
  • Use Display for user-facing context, Debug for developer diagnostics

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