Agent Kill Switch
vivekchand/clawmetry
Give the human an off switch and a cost meter for the coding agents on this machine, using ClawMetry.
Python observability patterns including structured logging, metrics, and distributed tracing.
Install Python Observability
$ npx skills add wshobson/agents --skill python-observability -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install wshobson/agents python-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/wshobson/agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/python-development/skills/python-observability .claude/skills/python-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 "python-observability" agent skill from https://github.com/wshobson/agents/tree/main/plugins/python-development/skills/python-observability into .claude/skills/python-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "python-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/wshobson/agents/tree/main/plugins/python-development/skills/python-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 wshobson/agents --skill python-observability -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install wshobson/agents python-observability --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wshobson/agents.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/python-development/skills/python-observability .agents/skills/python-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 "python-observability" agent skill from https://github.com/wshobson/agents/tree/main/plugins/python-development/skills/python-observability into .agents/skills/python-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "python-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 wshobson/agents --skill python-observability -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install wshobson/agents python-observability --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wshobson/agents.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/python-development/skills/python-observability .cursor/skills/python-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 "python-observability" agent skill from https://github.com/wshobson/agents/tree/main/plugins/python-development/skills/python-observability into .cursor/skills/python-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "python-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/wshobson/agents.git --path plugins/python-development/skills/python-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 wshobson/agents --skill python-observability -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install wshobson/agents python-observability --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wshobson/agents.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/python-development/skills/python-observability .gemini/skills/python-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 "python-observability" agent skill from https://github.com/wshobson/agents/tree/main/plugins/python-development/skills/python-observability into .gemini/skills/python-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "python-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 wshobson/agents python-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 wshobson/agents --skill python-observability -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/wshobson/agents.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/python-development/skills/python-observability .github/skills/python-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 "python-observability" agent skill from https://github.com/wshobson/agents/tree/main/plugins/python-development/skills/python-observability into .github/skills/python-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "python-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 wshobson/agents --skill python-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 wshobson/agents python-observability --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wshobson/agents.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/python-development/skills/python-observability .opencode/skills/python-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 "python-observability" agent skill from https://github.com/wshobson/agents/tree/main/plugins/python-development/skills/python-observability into .opencode/skills/python-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "python-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
python-observability- GitHub stars
- 40k
- Token cost
- ~1.8k tokens
- SKILL.md length
- 380 words
- Files
- 2 (incl. references)
- Skills in repo
- 142
- Repo updated
- First seen
- Licence
- MIT
At a glance
Python observability patterns including structured logging, metrics, and distributed tracing.
- Works in 4 steps: Structured Logging → The Four Golden Signals → Correlation IDs → …
- Implementing metrics collection
- SKILL.md covers When to Use This Skill, Core Concepts, Quick Start and Fundamental Patterns, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Python Observability is an agent skill from wshobson/agents. Python observability patterns including structured logging, metrics, and distributed tracing. Use when adding logging, implementing metrics collection, setting up tracing, or debugging production systems.
Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/details.md`).
It sits in DevOps & Cloud, covering Observability. It works with Python. The repository describes itself as: Multi-harness agentic plugin marketplace for Claude Code, Codex, Cursor, OpenCode, GitHub Copilot, Google Antigravity, and Pi. The licence is MIT.
When your agent uses it
- Implementing metrics collection
- Setting up tracing
- Debugging production systems
Example prompts
- “/python-observability”
Requirements
- Python 3
Workflow steps
4 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 46891e7. 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
Python Observability loads about 1.8k tokens when it runs, and up to ~3k if it reads all its reference files. Until then it costs about 56 tokens; SKILL.md has 380 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 wshobson/agents at commit 46891e7, republished under its MIT licence (© wshobson). 380 words, ~1,775 tokens.
Download SKILL.mdSave it as
.claude/skills/python-observability/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- python-observability
- description
- Python observability patterns including structured logging, metrics, and distributed tracing. Use when adding logging, implementing metrics collection, setting up tracing, or debugging production systems.
Python Observability
Instrument Python applications with structured logs, metrics, and traces. When something breaks in production, you need to answer "what, where, and why" without deploying new code.
When to Use This Skill
- Adding structured logging to applications
- Implementing metrics collection with Prometheus
- Setting up distributed tracing across services
- Propagating correlation IDs through request chains
- Debugging production issues
- Building observability dashboards
Core Concepts
1. Structured Logging
Emit logs as JSON with consistent fields for production environments. Machine-readable logs enable powerful queries and alerts. For local development, consider human-readable formats.
2. The Four Golden Signals
Track latency, traffic, errors, and saturation for every service boundary.
3. Correlation IDs
Thread a unique ID through all logs and spans for a single request, enabling end-to-end tracing.
4. Bounded Cardinality
Keep metric label values bounded. Unbounded labels (like user IDs) explode storage costs.
Quick Start
import structlog
structlog.configure(
processors=[
structlog.processors.TimeStamper(fmt="iso"),
structlog.processors.JSONRenderer(),
],
)
logger = structlog.get_logger()
logger.info("Request processed", user_id="123", duration_ms=45)Fundamental Patterns
Pattern 1: Structured Logging with Structlog
Configure structlog for JSON output with consistent fields.
import logging
import structlog
def configure_logging(log_level: str = "INFO") -> None:
"""Configure structured logging for the application."""
structlog.configure(
processors=[
structlog.contextvars.merge_contextvars,
structlog.processors.add_log_level,
structlog.processors.TimeStamper(fmt="iso"),
structlog.processors.StackInfoRenderer(),
structlog.processors.format_exc_info,
structlog.processors.JSONRenderer(),
],
wrapper_class=structlog.make_filtering_bound_logger(
getattr(logging, log_level.upper())
),
context_class=dict,
logger_factory=structlog.PrintLoggerFactory(),
cache_logger_on_first_use=True,
)
# Initialize at application startup
configure_logging("INFO")
logger = structlog.get_logger()Pattern 2: Consistent Log Fields
Every log entry should include standard fields for filtering and correlation.
import structlog
from contextvars import ContextVar
# Store correlation ID in context
correlation_id: ContextVar[str] = ContextVar("correlation_id", default="")
logger = structlog.get_logger()
def process_request(request: Request) -> Response:
"""Process request with structured logging."""
logger.info(
"Request received",
correlation_id=correlation_id.get(),
method=request.method,
path=request.path,
user_id=request.user_id,
)
try:
result = handle_request(request)
logger.info(
"Request completed",
correlation_id=correlation_id.get(),
status_code=200,
duration_ms=elapsed,
)
return result
except Exception as e:
logger.error(
"Request failed",
correlation_id=correlation_id.get(),
error_type=type(e).__name__,
error_message=str(e),
)
raisePattern 3: Semantic Log Levels
Use log levels consistently across the application.
| Level | Purpose | Examples |
|---|---|---|
DEBUG | Development diagnostics | Variable values, internal state |
INFO | Request lifecycle, operations | Request start/end, job completion |
WARNING | Recoverable anomalies | Retry attempts, fallback used |
ERROR | Failures needing attention | Exceptions, service unavailable |
# DEBUG: Detailed internal information
logger.debug("Cache lookup", key=cache_key, hit=cache_hit)
# INFO: Normal operational events
logger.info("Order created", order_id=order.id, total=order.total)
# WARNING: Abnormal but handled situations
logger.warning(
"Rate limit approaching",
current_rate=950,
limit=1000,
reset_seconds=30,
)
# ERROR: Failures requiring investigation
logger.error(
"Payment processing failed",
order_id=order.id,
error=str(e),
payment_provider="stripe",
)Never log expected behavior at ERROR. A user entering a wrong password is INFO, not ERROR.
Show full SKILL.md (147 more words)Show less
Pattern 4: Correlation ID Propagation
Generate a unique ID at ingress and thread it through all operations.
from contextvars import ContextVar
import uuid
import structlog
correlation_id: ContextVar[str] = ContextVar("correlation_id", default="")
def set_correlation_id(cid: str | None = None) -> str:
"""Set correlation ID for current context."""
cid = cid or str(uuid.uuid4())
correlation_id.set(cid)
structlog.contextvars.bind_contextvars(correlation_id=cid)
return cid
# FastAPI middleware example
from fastapi import Request
async def correlation_middleware(request: Request, call_next):
"""Middleware to set and propagate correlation ID."""
# Use incoming header or generate new
cid = request.headers.get("X-Correlation-ID") or str(uuid.uuid4())
set_correlation_id(cid)
response = await call_next(request)
response.headers["X-Correlation-ID"] = cid
return responsePropagate to outbound requests:
import httpx
async def call_downstream_service(endpoint: str, data: dict) -> dict:
"""Call downstream service with correlation ID."""
async with httpx.AsyncClient() as client:
response = await client.post(
endpoint,
json=data,
headers={"X-Correlation-ID": correlation_id.get()},
)
return response.json()Detailed worked examples and patterns
Detailed sections (starting with ## Advanced Patterns) live in references/details.md. Read that file when the navigation summary above is insufficient.
Best Practices Summary
- Use structured logging - JSON logs with consistent fields
- Propagate correlation IDs - Thread through all requests and logs
- Track the four golden signals - Latency, traffic, errors, saturation
- Bound label cardinality - Never use unbounded values as metric labels
- Log at appropriate levels - Don't cry wolf with ERROR
- Include context - User ID, request ID, operation name in logs
- Use context managers - Consistent timing and error handling
- Separate concerns - Observability code shouldn't pollute business logic
- Test your observability - Verify logs and metrics in integration tests
- Set up alerts - Metrics are useless without alerting
© wshobson, 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 (references) in plugins/python-development/skills/python-observability of wshobson/agents.
- SKILL.md
- references/details.md
Open the folder on GitHubat commit 46891e7
Compare with similar skills
Python 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 |
|---|---|---|---|---|---|---|
| Python Observability this skillwshobson/agents | 40k | — | ~1.8k | Automated safety check: Pass | MIT | |
| Agent Kill Switchvivekchand/clawmetry | 424 | — | ~1.1k | Automated safety check: Pass | MIT | |
| Clawmetry Selfcheckvivekchand/clawmetry | 424 | — | ~515 | Automated safety check: Pass | MIT | |
| New Pluginapache/skywalking-python | 219 | — | ~3.3k | Automated safety check: Pass | Apache-2.0 | |
| Speculative Namingsgl-project/sglang | 37k | 2 repos | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| Logfire Instrumentationbasicmachines-co/basic-memory | 4.1k | — | ~2.3k | Automated safety check: Pass | AGPL-3.0 |
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Related
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Questions about Python Observability
What does Python Observability do?
Python observability patterns including structured logging, metrics, and distributed tracing. Python Observability is an agent skill from wshobson/agents. Python observability patterns including structured logging, metrics, and distributed tracing.
When should I use Python Observability?
Python Observability fits situations like: implementing metrics collection; setting up tracing; debugging production systems.
How do I install Python Observability in Claude Code?
Run `npx skills add wshobson/agents --skill python-observability -a claude-code`. Or copy the skill folder (plugins/python-development/skills/python-observability in wshobson/agents) into .claude/skills/python-observability in your project. Claude Code loads it when a task matches its description.
How do I install Python Observability in Codex?
Run `npx skills add wshobson/agents --skill python-observability -a codex`. Or copy the skill folder (plugins/python-development/skills/python-observability in wshobson/agents) into .agents/skills/python-observability in your project. Codex loads it when a task matches its description.
Can I use Python 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 wshobson/agents --skill python-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/python-observability, .gemini/skills/python-observability, .github/skills/python-observability and .opencode/skills/python-observability in your project.
What does Python Observability need to run?
SKILL.md names no scripts, command-line tools or credentials: Python Observability is instructions for the agent only. Our summary lists: Python 3.
Does Python 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 Python 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 Python Observability use?
Python 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 Python Observability use?
About 1.8k tokens (SKILL.md is roughly 7.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.3k tokens, read only when the agent opens those files.
What are the alternatives to Python Observability?
Skills that share tags, products or a category with Python Observability: Agent Kill Switch (vivekchand/clawmetry, 424 stars), Clawmetry Selfcheck (vivekchand/clawmetry, 424 stars), New Plugin (apache/skywalking-python, 219 stars) and Speculative Naming (sgl-project/sglang, 37k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Python Observability?
wshobson (a GitHub user) maintains it in wshobson/agents, which has 40,254 GitHub stars. The repository holds 142 skills in this directory. The repository was last updated on October 5, 2026.
Source: wshobson/agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.