Vercel Optimize Audit
vercel-labs/agent-skills
Runs a metrics-first audit of a deployed Vercel project, gating investigations on real signals to produce ranked, citation-backed cost and performance recommendations.
A skill your agent uses when building production services, pipelines, or automation that needs to be debugged, monitored, or audited.
Install Structured Logging And Observability
$ npx skills add aiming-lab/MetaClaw --skill structured-logging-and-observability -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install aiming-lab/MetaClaw structured-logging-and-observability --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/aiming-lab/MetaClaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/memory_data/skills/structured-logging-and-observability .claude/skills/structured-logging-and-observability && rm -rf skills-srcUse ~/.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/
Install the "structured-logging-and-observability" agent skill from https://github.com/aiming-lab/MetaClaw/tree/main/memory_data/skills/structured-logging-and-observability into .claude/skills/structured-logging-and-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "structured-logging-and-observability", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/aiming-lab/MetaClaw/tree/main/memory_data/skills/structured-logging-and-observabilityType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add aiming-lab/MetaClaw --skill structured-logging-and-observability -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install aiming-lab/MetaClaw structured-logging-and-observability --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiming-lab/MetaClaw.git skills-src && mkdir -p .agents/skills && cp -r skills-src/memory_data/skills/structured-logging-and-observability .agents/skills/structured-logging-and-observability && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "structured-logging-and-observability" agent skill from https://github.com/aiming-lab/MetaClaw/tree/main/memory_data/skills/structured-logging-and-observability into .agents/skills/structured-logging-and-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "structured-logging-and-observability", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aiming-lab/MetaClaw --skill structured-logging-and-observability -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install aiming-lab/MetaClaw structured-logging-and-observability --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiming-lab/MetaClaw.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/memory_data/skills/structured-logging-and-observability .cursor/skills/structured-logging-and-observability && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "structured-logging-and-observability" agent skill from https://github.com/aiming-lab/MetaClaw/tree/main/memory_data/skills/structured-logging-and-observability into .cursor/skills/structured-logging-and-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "structured-logging-and-observability", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/aiming-lab/MetaClaw.git --path memory_data/skills/structured-logging-and-observability--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add aiming-lab/MetaClaw --skill structured-logging-and-observability -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install aiming-lab/MetaClaw structured-logging-and-observability --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiming-lab/MetaClaw.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/memory_data/skills/structured-logging-and-observability .gemini/skills/structured-logging-and-observability && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "structured-logging-and-observability" agent skill from https://github.com/aiming-lab/MetaClaw/tree/main/memory_data/skills/structured-logging-and-observability into .gemini/skills/structured-logging-and-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "structured-logging-and-observability", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install aiming-lab/MetaClaw structured-logging-and-observabilityInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add aiming-lab/MetaClaw --skill structured-logging-and-observability -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/aiming-lab/MetaClaw.git skills-src && mkdir -p .github/skills && cp -r skills-src/memory_data/skills/structured-logging-and-observability .github/skills/structured-logging-and-observability && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "structured-logging-and-observability" agent skill from https://github.com/aiming-lab/MetaClaw/tree/main/memory_data/skills/structured-logging-and-observability into .github/skills/structured-logging-and-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "structured-logging-and-observability", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aiming-lab/MetaClaw --skill structured-logging-and-observability -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install aiming-lab/MetaClaw structured-logging-and-observability --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiming-lab/MetaClaw.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/memory_data/skills/structured-logging-and-observability .opencode/skills/structured-logging-and-observability && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "structured-logging-and-observability" agent skill from https://github.com/aiming-lab/MetaClaw/tree/main/memory_data/skills/structured-logging-and-observability into .opencode/skills/structured-logging-and-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "structured-logging-and-observability", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
structured-logging-and-observability- GitHub stars
- 3.5k
- Token cost
- ~247 tokens
- SKILL.md length
- 65 words
- Files
- 1
- Skills in repo
- 36
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when building production services, pipelines, or automation that needs to be debugged, monitored, or audited.
- Building production services
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Automation that needs to be debugged
What it does
Structured Logging And Observability is an agent skill from aiming-lab/MetaClaw. Use this skill when building production services, pipelines, or automation that needs to be debugged, monitored, or audited. Add structured logs, metrics, and health checks before shipping any service.
Its SKILL.md is about 250 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in DevOps & Cloud, covering Observability. The repository describes itself as: 🦞 Just talk to your agent — it learns and EVOLVES 🧬. The licence is MIT.
When your agent uses it
- Building production services
- Automation that needs to be debugged
Example prompts
- “/structured-logging-and-observability”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 922caf3. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
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 python).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
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
Structured Logging And Observability loads about 247 tokens when it runs. Until then it costs about 60 tokens; SKILL.md has 65 words of instructions outside code blocks.
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 passed
The automated check found no risky patterns in SKILL.md.
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 aiming-lab/MetaClaw at commit 922caf3, republished under its MIT licence (© aiming-lab). 65 words, ~247 tokens.
Download SKILL.mdSave it as
.claude/skills/structured-logging-and-observability/SKILL.md (or your agent's skills folder).- name
- structured-logging-and-observability
- description
- Use this skill when building production services, pipelines, or automation that needs to be debugged, monitored, or audited. Add structured logs, metrics, and health checks before shipping any service.
- category
- automation
Structured Logging and Observability
Log levels:
DEBUG: detailed diagnostic (off in production)INFO: normal operation milestonesWARNING: recoverable unexpected stateERROR: operation failed, action needed
Structured logs (JSON) over free-form text:
import structlog
log = structlog.get_logger()
log.info("request_complete", method="POST", path="/api/data", status=200, latency_ms=42)Metrics to expose: request rate, error rate, latency (p50/p95/p99), queue depth.
Health check endpoint: /health returning {"status": "ok"} — required for load balancers.
Anti-pattern: Logging only on error; you can't diagnose what you didn't observe.
© aiming-lab, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in memory_data/skills/structured-logging-and-observability of aiming-lab/MetaClaw.
Open the folder on GitHubat commit 922caf3
Compare with similar skills
Structured Logging And Observability next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Structured Logging And Observability this skillaiming-lab/MetaClaw | 3.5k | — | ~247 | Automated safety check: Pass | MIT | |
| Vercel Optimize Auditvercel-labs/agent-skills | 32k | 9 repos | ~4.3k | Automated safety check: Pass | None | |
| Kubeshark Installerkubeshark/kubeshark | 12k | — | ~3.6k | Automated safety check: Notes | Apache-2.0 | |
| Kubeshark KFL2 Filter Referencekubeshark/kubeshark | 12k | — | ~3.6k | Automated safety check: Pass | Apache-2.0 | |
| KubeSphere ServiceMesh Managerkubesphere/kubesphere | 17k | — | ~2.4k | Automated safety check: Pass | Custom licence | |
| Kubernetes Network Root Cause Analysiskubeshark/kubeshark | 12k | — | ~5.3k | Automated safety check: Pass | Apache-2.0 |
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Related
Categories
Questions about Structured Logging And Observability
What does Structured Logging And Observability do?
A skill your agent uses when building production services, pipelines, or automation that needs to be debugged, monitored, or audited. Structured Logging And Observability is an agent skill from aiming-lab/MetaClaw. Use this skill when building production services, pipelines, or automation that needs to be debugged, monitored, or audited.
When should I use Structured Logging And Observability?
Structured Logging And Observability fits situations like: building production services; automation that needs to be debugged.
How do I install Structured Logging And Observability in Claude Code?
Run `npx skills add aiming-lab/MetaClaw --skill structured-logging-and-observability -a claude-code`. Or copy the skill folder (memory_data/skills/structured-logging-and-observability in aiming-lab/MetaClaw) into .claude/skills/structured-logging-and-observability in your project. Claude Code loads it when a task matches its description.
How do I install Structured Logging And Observability in Codex?
Run `npx skills add aiming-lab/MetaClaw --skill structured-logging-and-observability -a codex`. Or copy the skill folder (memory_data/skills/structured-logging-and-observability in aiming-lab/MetaClaw) into .agents/skills/structured-logging-and-observability in your project. Codex loads it when a task matches its description.
Can I use Structured Logging And Observability 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 aiming-lab/MetaClaw --skill structured-logging-and-observability -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/structured-logging-and-observability, .gemini/skills/structured-logging-and-observability, .github/skills/structured-logging-and-observability and .opencode/skills/structured-logging-and-observability in your project.
What does Structured Logging And Observability need to run?
SKILL.md names no scripts, command-line tools or credentials: Structured Logging And Observability is instructions for the agent only. Our summary lists: Python 3.
Does Structured Logging And Observability access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Structured Logging And Observability safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Structured Logging And Observability use?
Structured Logging And Observability is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Structured Logging And Observability use?
About 247 tokens (SKILL.md is roughly 988 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 Structured Logging And Observability?
Skills that share tags, products or a category with Structured Logging And Observability: Vercel Optimize Audit (vercel-labs/agent-skills, 32k stars), Kubeshark Installer (kubeshark/kubeshark, 12k stars), Kubeshark KFL2 Filter Reference (kubeshark/kubeshark, 12k stars) and KubeSphere ServiceMesh Manager (kubesphere/kubesphere, 17k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Structured Logging And Observability?
aiming-lab (a GitHub organization) maintains it in aiming-lab/MetaClaw, which has 3,458 GitHub stars. The repository holds 36 skills in this directory. The repository was last updated on June 7, 2026.
Source: aiming-lab/MetaClaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.