OpenAI Agents SDK is a Python framework for building workflows in which multiple AI agents use tools, delegate tasks, follow safety checks, and involve people when needed. Developers use it to create and monitor agent, sandbox, realtime, and voice applications.
Borrowing it
Nothing to install: this file belongs to openai/openai-agents-python. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/openai/openai-agents-python/main/.agents/skills/sensitive-logging-audit/SKILL.mdgit clone --depth 1 https://github.com/openai/openai-agents-pythonWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/openai/openai-agents-python/sensitive-logging-audit)<a href="https://agentmods.dev/skills/openai/openai-agents-python/sensitive-logging-audit"><img src="https://agentmods.dev/badge/skills/openai/openai-agents-python/sensitive-logging-audit/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/openai/openai-agents-python/sensitive-logging-audit"><img src="https://agentmods.dev/badge/skills/openai/openai-agents-python/sensitive-logging-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
What 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 5.1 | $0.00025 | $0.00853 |
| Opus 5.5 | $0.00010 | $0.00341 |
| Sonnet 5.5 | $0.00005 | $0.00171 |
| Haiku 4.5 | $0.00003 | $0.00085 |
Grade A, and why
sensitive-logging-audit 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 1mo 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sensitive Logging Audit
Objective
Find candidate output sinks, trace their values manually, fix demonstrated leaks at shared runtime boundaries, and prove redaction with adversarial tests.
The collector is only a syntax-based search aid. It does not resolve Python aliases or control flow, certify policy guards, or prove that an absent candidate is safe.
Workflow
1. Establish the review surface
- Work in the current checkout and preserve unrelated changes.
- Read
src/agents/_debug.py,src/agents/logger.py, and the affected callers. - Treat exception messages, arguments, tracebacks, causes, contexts, notes, names, URLs, and arbitrary values as potentially sensitive.
- Read the Python redaction validation matrix.
Run the collector tests, then collect candidates:
uv run python .agents/skills/sensitive-logging-audit/scripts/test_inventory.py
uv run python .agents/skills/sensitive-logging-audit/scripts/inventory_logging.py \
--format json --output /tmp/sensitive-logging-candidates.json
The report intentionally contains no policy, safe, or guard classification.
2. Supplement the collector with source search
The collector does not follow assignments such as emit = logger.error. Search the source directly and inspect aliases, callbacks, wrappers, and reflective dispatch:
rg -n '\.(debug|info|warning|warn|error|exception|critical|fatal|log)\b' src/agents
rg -n '\b(print|pprint|pp|warn|warn_explicit|write|writelines|print_exc|print_exception)\b' src/agents
rg -n 'DONT_LOG_(MODEL|TOOL)_DATA|log_(model|tool|model_and_tool)_action' src/agents
Do not turn collector coverage or a textual guard into a security conclusion. Trace producers and callers.
3. Classify manually
Assign each reviewed path one disposition:
model: model requests, responses, Realtime events, or derived values.tool: tool arguments, outputs, MCP data, tool events, or derived values.model+tool: either class may reach the sink.operational: demonstrated to contain only non-sensitive SDK metadata.intentional-output: explicitly user-facing output rather than diagnostics.uncertain: source tracing is incomplete.
What ships with it
4 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.
- 1mo ago Changed · -34 tokens per session eb292b63ee61
- 1mo ago First seen · 79 lines · 59 tokens per session scan A 8bf2b53052c7
sensitive-logging-audit is a skill published in the GitHub repository openai/openai-agents-python (30,339 stars, last pushed today), licensed MIT. It adds 25 tokens to every session and 853 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-30.
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