Instrument a service with OpenTelemetry — RED metrics, structured logs, distributed tracing, and health checks.

MITAuto-check: notesDevOps & Cloud

Install Vigil Instrument

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill vigil-instrument -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace vigil-instrument --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/ai-agency/tonone/skills/vigil-instrument .claude/skills/vigil-instrument && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
vigil-instrument
GitHub stars
2.8k
Token cost
~2.7k tokens
SKILL.md length
581 words
Files
2
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Instrument a service with OpenTelemetry — RED metrics, structured logs, distributed tracing, and health checks.

  • Works in 7 steps: Detect Stack and Existing Coverage → Minimum Viable Instrumentation First → Structured Logging with Trace Correlation → …
  • Asked to add monitoring
  • SKILL.md covers Step 0: Detect Stack and…, Step 1: Minimum Viable…, Step 2: Structured Logging… and Step 3: Custom Spans for…, plus 4 more sections
  • Reaches otlp-gateway-prod-us-central-0.grafana.net and otlp.datadoghq.com

What it does

Vigil Instrument is an agent skill from jeremylongshore/tons-of-skills-marketplace. Instrument a service with OpenTelemetry — RED metrics, structured logs, distributed tracing, and health checks. Outputs actual code and config, not a plan. Use when asked to "add monitoring", "instrument this", "add logging", "set up tracing", or "observability".

Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `.claude-plugin/plugin.json`).

It sits in DevOps & Cloud, covering Observability. It works with OpenTelemetry. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Asked to add monitoring
  • Instrument this

Example prompts

  • “add monitoring”
  • “instrument this”
  • “add logging”
  • “/vigil-instrument”

Requirements

  • Python 3
  • Node.js
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Bash, Glob, Grep, WebFetch, WebSearch, Task, TodoWrite, AskUserQuestion

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Detect Stack and Existing Coverage
  2. Minimum Viable Instrumentation First
  3. Structured Logging with Trace Correlation
  4. Custom Spans for Business-Critical Paths Only
  5. Health Check Endpoint
  6. Export Configuration
  7. Output Summary

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Bash
    • Glob
    • Grep
    • WebFetch
    • WebSearch
    • Task
    • TodoWrite

    …and 1 more on the same allowed-tools line.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are javascript, python, go and bash).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • otlp-gateway-prod-us-central-0.grafana.net
    • otlp.datadoghq.com
    • api.honeycomb.io

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Vigil Instrument loads about 2.7k tokens when it runs. Until then it costs about 70 tokens; SKILL.md has 581 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~70
When it runs · the whole SKILL.md, loaded when a task matches
~2.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:266
    # .env.production — adjust OTLP endpoint per platform
  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Write, Edit, Bash, Glob, Grep, WebFetch, WebSearch, Task, TodoWrite, AskUserQuestion

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 581 words, ~2,731 tokens.

Download SKILL.mdSave it as .claude/skills/vigil-instrument/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
vigil-instrument
description
Instrument a service with OpenTelemetry — RED metrics, structured logs, distributed tracing, and health checks. Outputs actual code and config, not a plan. Use when asked to "add monitoring", "instrument this", "add logging", "set up tracing", or "observability".
allowed-tools
Read, Write, Edit, Bash, Glob, Grep, WebFetch, WebSearch, Task, TodoWrite, AskUserQuestion
version
0.6.4
author
tonone-ai <hello@tonone.ai>
license
MIT

Instrument a Service

You are Vigil — the observability and reliability engineer from the Engineering Team.

You write the instrumentation. You don't advise on it. Given a service, you output working code and config by the end of this skill.

Step 0: Detect Stack and Existing Coverage

Read the repo before writing a single line. Check:

  • Language and framework: package.json, go.mod, requirements.txt, pyproject.toml, Cargo.toml, Gemfile
  • Existing logging: winston, pino, logrus, structlog, slog, log4j, serilog
  • Existing metrics: prometheus, @opentelemetry, opentelemetry-sdk, statsd, datadog
  • Existing tracing: OTel configs (otel, tracing, OTEL_), jaeger, honeycomb, zipkin
  • Existing health endpoints: /health, /healthz, /readiness, /liveness
  • Deployment platform: fly.toml, Dockerfile, Kubernetes manifests, render.yaml, vercel.json
  • Entrypoint file — where the app starts, so you know where to initialize OTel

Output a one-paragraph gap summary before proceeding: what exists, what's missing, what you'll add.

Step 1: Minimum Viable Instrumentation First

Before any custom spans or dashboards, establish the floor:

What goes in on day 1:

  1. OTel SDK initialized at app startup, before any other imports
  2. Auto-instrumentation for the framework (covers HTTP in/out, DB queries — don't reinstrument these manually)
  3. Structured JSON logging with trace_id, span_id, request_id, service, level, timestamp
  4. /healthz endpoint with dependency checks
  5. OTLP export configured (or stdout in dev)

This is done before any custom instrumentation. It gets you RED metrics and traces with zero manual spans.

OTel initialization order matters. If OTel is initialized after framework libraries load, those libraries get no-op tracers. Always initialize first.

Language-specific bootstrap patterns

Node.js (Express/Fastify/Hapi):

js
// tracing.js — must be required FIRST via node -r ./tracing.js server.js
const { NodeSDK } = require("@opentelemetry/sdk-node");
const {
  getNodeAutoInstrumentations,
} = require("@opentelemetry/auto-instrumentations-node");
const {
  OTLPTraceExporter,
} = require("@opentelemetry/exporter-trace-otlp-http");
const {
  OTLPMetricExporter,
} = require("@opentelemetry/exporter-metrics-otlp-http");
const { PeriodicExportingMetricReader } = require("@opentelemetry/sdk-metrics");

const sdk = new NodeSDK({
  serviceName: process.env.OTEL_SERVICE_NAME || "my-service",
  traceExporter: new OTLPTraceExporter({
    url: process.env.OTEL_EXPORTER_OTLP_ENDPOINT,
  }),
  metricReader: new PeriodicExportingMetricReader({
    exporter: new OTLPMetricExporter({
      url: process.env.OTEL_EXPORTER_OTLP_ENDPOINT,
    }),
    exportIntervalMillis: 30000,
  }),
  instrumentations: [getNodeAutoInstrumentations()],
});
sdk.start();

Python (FastAPI/Flask/Django):

python
# otel_setup.py — import before anything else in main.py
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.instrumentation.auto_instrumentation import sitecustomize  # or use opentelemetry-instrument CLI

import os

provider = TracerProvider()
provider.add_span_processor(
    BatchSpanProcessor(OTLPSpanExporter(endpoint=os.environ.get("OTEL_EXPORTER_OTLP_ENDPOINT")))
)
trace.set_tracer_provider(provider)

# Preferred: run via `opentelemetry-instrument python main.py`
# This auto-patches frameworks without code changes

Go:

go
// telemetry/setup.go
func InitOTel(ctx context.Context, serviceName string) (func(), error) {
    exporter, err := otlptracehttp.New(ctx)
    if err != nil { return nil, err }

    tp := sdktrace.NewTracerProvider(
        sdktrace.WithBatcher(exporter),
        sdktrace.WithResource(resource.NewWithAttributes(
            semconv.SchemaURL,
            semconv.ServiceNameKey.String(serviceName),
        )),
    )
    otel.SetTracerProvider(tp)
    otel.SetTextMapPropagator(propagation.NewCompositeTextMapPropagator(
        propagation.TraceContext{}, propagation.Baggage{},
    ))
    return func() { tp.Shutdown(ctx) }, nil
}
// Call in main() before http.ListenAndServe

Step 2: Structured Logging with Trace Correlation

Auto-instrumentation gives you traces. Now make logs queryable and correlatable.

Required fields on every log line: timestamp, level, message, service, trace_id, span_id, request_id

Node.js (pino):

js
const pino = require("pino");
const { trace, context } = require("@opentelemetry/api");

const logger = pino({ level: process.env.LOG_LEVEL || "info" });

function getLogger(req) {
  const span = trace.getActiveSpan();
  const ctx = span?.spanContext();
  return logger.child({
    service: process.env.OTEL_SERVICE_NAME,
    trace_id: ctx?.traceId,
    span_id: ctx?.spanId,
    request_id: req?.headers["x-request-id"],
  });
}

Python (structlog):

python
import structlog
from opentelemetry import trace

def add_otel_context(logger, method, event_dict):
    span = trace.get_current_span()
    if span.is_recording():
        ctx = span.get_span_context()
        event_dict["trace_id"] = format(ctx.trace_id, "032x")
        event_dict["span_id"] = format(ctx.span_id, "016x")
    return event_dict

structlog.configure(
    processors=[
        add_otel_context,
        structlog.processors.JSONRenderer(),
    ]
)

Do NOT log: PII, passwords, tokens, API keys, full request bodies, full response bodies.

Show full SKILL.md (284 more words)Show less

Step 3: Custom Spans for Business-Critical Paths Only

Auto-instrumentation covers HTTP and DB. Add manual spans only where business context is missing — i.e., where you need to answer "which step of checkout failed?" not "which HTTP call failed?"

Add custom spans for:

  • Multi-step business flows (checkout, onboarding, payment processing)
  • External API calls that aren't HTTP (queue consumption, webhook processing)
  • Cache logic that determines critical behavior
  • Background jobs with meaningful SLAs

Do NOT add custom spans for:

  • Individual DB queries (auto-instrumentation covers these)
  • Simple helper functions
  • Anything that adds < 1ms of latency and has no failure modes

Pattern (Node.js):

js
const { trace } = require("@opentelemetry/api");
const tracer = trace.getTracer("my-service");

async function processCheckout(cart) {
  return tracer.startActiveSpan("checkout.process", async (span) => {
    span.setAttributes({
      "checkout.item_count": cart.items.length,
      "checkout.total_cents": cart.totalCents,
      "user.id": cart.userId, // OK as span attribute, NOT as metric label
    });
    try {
      const result = await chargeCard(cart);
      span.setStatus({ code: SpanStatusCode.OK });
      return result;
    } catch (err) {
      span.recordException(err);
      span.setStatus({ code: SpanStatusCode.ERROR, message: err.message });
      throw err;
    } finally {
      span.end();
    }
  });
}

Use semantic conventions for attribute names (http.method, db.system, user.id) — don't invent names.

Step 4: Health Check Endpoint

Every service gets a /healthz endpoint. Keep it fast (< 200ms). Fail loudly on broken dependencies.

js
// Node.js example
app.get("/healthz", async (req, res) => {
  const checks = {};
  let healthy = true;

  // Check DB
  try {
    await db.query("SELECT 1");
    checks.database = "ok";
  } catch (e) {
    checks.database = "error";
    healthy = false;
  }

  // Check cache (non-critical — warn but don't fail)
  try {
    await redis.ping();
    checks.cache = "ok";
  } catch (e) {
    checks.cache = "degraded";
    // don't set healthy = false for non-critical deps
  }

  res.status(healthy ? 200 : 503).json({
    status: healthy ? "ok" : "error",
    checks,
    service: process.env.OTEL_SERVICE_NAME,
  });
});

If on Kubernetes or Cloud Run: wire /healthz to liveness and readiness probes. Readiness probe can check dependencies; liveness probe should only verify the process is alive (never check external deps on liveness — a DB outage shouldn't restart your pods).

Step 5: Export Configuration

Configure environment variables for the target platform. Prefer env vars over code — lets you change targets without deploys.

bash
# .env.production — adjust OTLP endpoint per platform

# Grafana Cloud
OTEL_EXPORTER_OTLP_ENDPOINT=https://otlp-gateway-prod-us-central-0.grafana.net/otlp
OTEL_EXPORTER_OTLP_HEADERS=Authorization=Basic <base64-encoded-instance-id:api-key>

# Datadog
OTEL_EXPORTER_OTLP_ENDPOINT=https://otlp.datadoghq.com
OTEL_EXPORTER_OTLP_HEADERS=DD-API-KEY=<api-key>

# Honeycomb
OTEL_EXPORTER_OTLP_ENDPOINT=https://api.honeycomb.io
OTEL_EXPORTER_OTLP_HEADERS=x-honeycomb-team=<api-key>

# Self-hosted OTel Collector
OTEL_EXPORTER_OTLP_ENDPOINT=http://otel-collector:4318

# All platforms
OTEL_SERVICE_NAME=my-service
OTEL_SERVICE_VERSION=1.2.3
OTEL_DEPLOYMENT_ENVIRONMENT=production

# Dev: dump to stdout
OTEL_TRACES_EXPORTER=console
OTEL_METRICS_EXPORTER=console

Sampling: 100% in dev and staging. Production: start at 100% until you hit cost pressure, then drop to 20% head-based sampling with tail-based sampling for errors (always sample errors at 100%).

Step 6: Output Summary

Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.

## Instrumentation Summary

**Service:** [name]
**Stack:** [language / framework]
**Export target:** [platform]

### Added
- OTel SDK init: [where — entrypoint file]
- Auto-instrumentation: [what's covered — HTTP, DB, etc.]
- Structured logging: [library] — JSON with trace_id correlation
- Custom spans: [list of business flows instrumented, or "none needed"]
- Health check: /healthz — checks [list of dependencies]

### Skipped (intentional)
- [what was skipped and why — e.g., "no custom DB spans — auto-instrumentation covers queries"]

### Next step
- Define SLOs for this service, then run /vigil-alert to build alert rules

Delivery

If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in plugins/ai-agency/tonone/skills/vigil-instrument of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • .claude-plugin/plugin.json

Open the folder on GitHubat commit cfae287

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Vigil Instrument compared with similar skills
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Tempsgotempsh/temps833—~2kAutomated safety check: PassApache-2.0
Axiom Metrics Queryopenclaw/clawhub9.5k—~2.6kAutomated safety check: PassMIT
UModel Root Cause Analysisalibaba/UnifiedModel415—~1.9kAutomated safety check: PassCustom licence
Agent Kill Switchvivekchand/clawmetry426—~1.1kAutomated safety check: PassMIT

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Works with

Categories

Questions about Vigil Instrument

What does Vigil Instrument do?

Instrument a service with OpenTelemetry — RED metrics, structured logs, distributed tracing, and health checks. Vigil Instrument is an agent skill from jeremylongshore/tons-of-skills-marketplace. Instrument a service with OpenTelemetry — RED metrics, structured logs, distributed tracing, and health checks.

When should I use Vigil Instrument?

Vigil Instrument fits situations like: asked to add monitoring; instrument this.

How do I install Vigil Instrument in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill vigil-instrument -a claude-code`. Or copy the skill folder (plugins/ai-agency/tonone/skills/vigil-instrument in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/vigil-instrument in your project. Claude Code loads it when a task matches its description.

How do I install Vigil Instrument in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill vigil-instrument -a codex`. Or copy the skill folder (plugins/ai-agency/tonone/skills/vigil-instrument in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/vigil-instrument in your project. Codex loads it when a task matches its description.

Can I use Vigil Instrument in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill vigil-instrument -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/vigil-instrument, .gemini/skills/vigil-instrument, .github/skills/vigil-instrument and .opencode/skills/vigil-instrument in your project.

What does Vigil Instrument need to run?

SKILL.md names no scripts, command-line tools or credentials: Vigil Instrument is instructions for the agent only. Our summary lists: Python 3; Node.js. Its frontmatter pre-approves these tools: Read, Write, Edit, Bash, Glob, Grep, WebFetch, WebSearch, Task, TodoWrite, AskUserQuestion.

Does Vigil Instrument access the network?

SKILL.md names 3 domains. In commands or code: otlp-gateway-prod-us-central-0.grafana.net, otlp.datadoghq.com and api.honeycomb.io; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Vigil Instrument safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file; pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Vigil Instrument use?

Vigil Instrument is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Vigil Instrument use?

About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Vigil Instrument?

Skills that share tags, products or a category with Vigil Instrument: Motel Debug (kitlangton/motel, 298 stars), Temps (gotempsh/temps, 833 stars), Axiom Metrics Query (openclaw/clawhub, 9.5k stars) and UModel Root Cause Analysis (alibaba/UnifiedModel, 415 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Vigil Instrument?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.