Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/jartan-llc/grimoire/python-observabilitynpx skills add Jartan-LLC/grimoire --skill python-observabilitygit clone --depth 1 https://github.com/Jartan-LLC/grimoireWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00019 | $0.01829 |
| Opus 5 | $0.00010 | $0.00915 |
| Sonnet 5 | $0.00004 | $0.00366 |
| Haiku 4.5 | $0.00002 | $0.00183 |
Grade A, and why
python-observability scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
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()
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 246 lines · 19 tokens per session scan A 539d4b1ed934
python-observability is a skill published in the GitHub repository Jartan-LLC/grimoire (2 stars, last pushed 14d ago), licensed MIT. It adds 19 tokens to every session and 1,829 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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