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 rules/eliornl/rolemule/logging-patternsgit clone --depth 1 https://github.com/eliornl/rolemuleWhat 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 | $0.00000 | $0.01969 |
| Opus 5 | $0.00000 | $0.00984 |
| Sonnet 5 | $0.00000 | $0.00394 |
| Haiku 4.5 | $0.00000 | $0.00197 |
Grade A, and why
logging-patterns 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 yesterday.
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 — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Logging Patterns
Logger Setup
import logging
from utils.logging_config import get_structured_logger
logger = logging.getLogger(__name__) # standard logger
slog = get_structured_logger(__name__) # structured logger with helpers
Structured Logger Helpers
# Agent lifecycle
slog.log_agent_start("job_analyzer", workflow_id)
slog.log_agent_complete("job_analyzer", workflow_id, duration_ms=1234.5)
slog.log_agent_error("job_analyzer", workflow_id, error, duration_ms=500.0)
# External API
slog.log_external_api_call(service="gemini", operation="generate", duration_ms=500.0, success=True)
# Database
slog.log_db_operation("SELECT", "users", duration_ms=5.0, rows_affected=1)
# Cache
slog.log_cache_hit("job_analysis", cache_key)
slog.log_cache_miss("user_profile", user_id)
# Auth / security events
slog.log_login_success(email, auth_method="local")
slog.log_login_failure(email, reason="invalid_password", attempts_remaining=3)
slog.log_account_lockout(email, duration_seconds=900)
slog.log_registration(email, auth_method="local")
slog.log_password_reset_request(email)
slog.log_password_reset_complete(email)
slog.log_password_change(email)
slog.log_token_refresh(email)
slog.log_oauth_login(email, provider="google", is_new_user=True)
Performance Decorator
from utils.logging_config import log_execution_time
@log_execution_time(message="Job analysis took {duration_ms}ms")
async def analyze_job(self, state): ...
Request Context Correlation
from utils.logging_config import set_request_context, clear_request_context
tokens = set_request_context(request_id="abc123", user_id="uuid", session_id="uuid")
try:
# all logger.info/error calls here include request_id, user_id automatically
logger.info("Processing request")
finally:
clear_request_context(tokens)
Workflow Context Pattern (always use try/finally)
context_tokens = set_request_context(session_id=session_id, user_id=str(user_id))
try:
final_state = await self.workflow.ainvoke(initial_state)
return self._state_to_dict(final_state)
finally:
clear_request_context(context_tokens) # must run even on exception
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.
- yesterday First seen · 211 lines · 1,969 tokens per session scan A 5100db892412
logging-patterns is a cursor rule published in the GitHub repository eliornl/rolemule (37 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,969 tokens. 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 2026-08-30.
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