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/julien777z/agent-sync-action/python-resource-managementnpx skills add julien777z/agent-sync-action --skill python-resource-managementgit clone --depth 1 https://github.com/julien777z/agent-sync-actionWhat 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 | $0.00037 | $0.01567 |
| Opus 5 | $0.00018 | $0.00783 |
| Sonnet 5 | $0.00007 | $0.00313 |
| Haiku 4.5 | $0.00004 | $0.00157 |
Grade A, and why
python-resource-management scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
return await conn.fetch(query, *args) Copies of this mod
1 near-identical copy found in the catalogue:
- python-resource-management — 86% identical, 17 lines differ
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 Resource Management
Manage resources deterministically using context managers. Resources like database connections, file handles, and network sockets should be released reliably, even when exceptions occur.
When to Use This Skill
- Managing database connections and connection pools
- Working with file handles and I/O
- Implementing custom context managers
- Building streaming responses with state
- Handling nested resource cleanup
- Creating async context managers
Core Concepts
1. Context Managers
The with statement ensures resources are released automatically, even on exceptions.
2. Protocol Methods
__enter__/__exit__ for sync, __aenter__/__aexit__ for async resource management.
3. Unconditional Cleanup
__exit__ always runs, regardless of whether an exception occurred.
4. Exception Handling
Return True from __exit__ to suppress exceptions, False to propagate them.
Quick Start
from contextlib import contextmanager
@contextmanager
def managed_resource():
resource = acquire_resource()
try:
yield resource
finally:
resource.cleanup()
with managed_resource() as r:
r.do_work()
Fundamental Patterns
Pattern 1: Class-Based Context Manager
Implement the context manager protocol for complex resources.
class DatabaseConnection:
"""Database connection with automatic cleanup."""
def __init__(self, dsn: str) -> None:
self._dsn = dsn
self._conn: Connection | None = None
def connect(self) -> None:
"""Establish database connection."""
self._conn = psycopg.connect(self._dsn)
def close(self) -> None:
"""Close connection if open."""
if self._conn is not None:
self._conn.close()
self._conn = None
def __enter__(self) -> "DatabaseConnection":
"""Enter context: connect and return self."""
self.connect()
return self
def __exit__(
self,
exc_type: type[BaseException] | None,
exc_val: BaseException | None,
exc_tb: TracebackType | None,
) -> None:
"""Exit context: always close connection."""
self.close()
# Usage with context manager (preferred)
with DatabaseConnection(dsn) as db:
result = db.execute(query)
# Manual management when needed
db = DatabaseConnection(dsn)
db.connect()
try:
result = db.execute(query)
finally:
db.close()
What ships with it
2 files 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 · 37 tokens per session scan A 1df95a3b8bd6
python-resource-management is a skill published in the GitHub repository julien777z/agent-sync-action (5 stars, last pushed 3d ago), licensed MIT. It adds 37 tokens to every session and 1,567 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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