sensitive-logging-audit
The skill helps audit and fix sensitive data leaks in Python SDK logging, exceptions, and telemetry by collecting candidate sinks, manually tracing values…
Identity proven by GitHub's verified organisation badge (openai)
- Agents
- Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode
- License
- MIT (permissive)
- Pin
- 125efa0pinned Oct 9, 2026
- Context cost
- about 970 tokenswhen the skill loads
- GitHub stars
- 29,916on openai/openai-agents-python
- Scripts
- 2 scripts
What it does
“Audit or fix sensitive-data exposure in Python SDK diagnostics, exceptions, logging, and telemetry.”
Generated summary from the skill's own text
The skill helps audit and fix sensitive data leaks in Python SDK logging, exceptions, and telemetry by collecting candidate sinks, manually tracing values, applying redaction logic at runtime boundaries, and validating fixes with tests.
When to use it
- Reviewing SDK diagnostics for credential or model data exposure
- Implementing data redaction in debug logs and error handlers
- Validating that sensitive information respects logging policy flags
- Testing exception chains and telemetry for unintended data leakage
When not to
- Applying automatic, policy-agnostic data redaction without manual review
- Using collector results alone as proof of safe logging practices
Install
These commands install from the publisher's repository and need no account.
skills CLI
npx skills add openai/openai-agents-python --skill sensitive-logging-auditInstalls from the repository's default branch, which may have changed since the commit we checked.
GitHub CLI 2.90 or later
gh skill install openai/openai-agents-python sensitive-logging-audit@125efa029b4bfd84238bd2c4fd69c3406f802663Installs the commit we checked (125efa0).
Copy by hand
Download the folder at 125efa0 and put it in .claude/skills/sensitive-logging-audit for Claude Code and in .agents/skills/sensitive-logging-audit for Codex, Cursor, Gemini CLI, GitHub Copilot and OpenCode.
Copy for your agent
A prompt for any coding agent. It installs this skill from GitHub at the pinned commit, copies the files as they are, and runs nothing unless you ask.
Prompt
Install the agent skill "sensitive-logging-audit" by OpenAI. Source, pinned to commit 125efa029b4bfd84238bd2c4fd69c3406f802663: https://github.com/openai/openai-agents-python/tree/125efa029b4bfd84238bd2c4fd69c3406f802663/.agents/skills/sensitive-logging-audit 1. Download that folder at exactly this commit. Do not use the default branch. 2. Copy every file unchanged, keeping the folder structure, into .claude/skills/sensitive-logging-audit/ for Claude Code and .agents/skills/sensitive-logging-audit/ for Codex, Cursor, Gemini CLI, GitHub Copilot and OpenCode. Create the folders if they do not exist. 3. Do not edit the files, and do not run any script in the folder unless I ask you to. 4. When you are done, list the files you copied and where you put them. Listing and checks: https://www.agentnative.dev/skills/openai/sensitive-logging-audit-openai-agents-python
SKILL.md at the pin
--- name: sensitive-logging-audit description: Audit or fix sensitive-data exposure in Python SDK diagnostics, exceptions, logging, and telemetry. --- # 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](references/redaction-validation.md). Run the collector tests, then collect candidates: ```bash 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: ```bash 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. Record evidence in the audit report. The script does not validate or inherit dispositions. ### 4. Fix runtime boundaries Before changing runtime behavior, use `$implementation-strategy`. - Check the relevant `_debug.DONT_LOG_MODEL_DATA` and `_debug.DONT_LOG_TOOL_DATA` flags before formatting or inspecting sensitive values. - Redact mixed model/tool values when either flag disables data logging. - In redacted mode, emit a fixed message and omit sensitive `args`, `extra`, and `exc_info`. - Build diagnostic-only context lazily so redacted mode never reads it. - Preserve useful diagnostics when sensitive-data logging is explicitly enabled. - Keep logging failure from changing fallback, cleanup, event, rejection, or cancellation behavior. - For MCP URLs, remove credentials, query parameters, and fragments in diagnostic mode; never use sanitized names as a substitute for fixed redacted messages. ### 5. Prove caller behavior Add tests at every changed caller boundary. Inspect the complete `LogRecord`, not only rendered text. Test both redacted policies, diagnostic mode, hostile objects, exception chains, and the caller's observable fallback or cleanup behavior as applicable. ### 6. Re-run and close out Re-run the collector, the manual searches, focused tests, and applicable repository gates. Use `$code-change-verification` for runtime or test changes and `$pr-draft-summary` when required. Report candidate counts as search coverage only. Lead with confirmed leaks fixed, retained intentional output, reviewed uncertainty, and verification results. Never report a clean collector result as proof that no sensitive logging path exists.
From openai/openai-agents-python at 125efa0, shown as plain text under its license: MIT (permissive). The license text is in LICENSE.
View SKILL.md on GitHub at 125efa0Files
- SKILL.md4.0 KB · eb292b63ee61
- agents/
- openai.yaml244 B · fd2c32c0d428
- references/
- redaction-validation.md4.3 KB · 98b66dc4e6ba
- scripts/
- inventory_logging.py12 KB · 6ee59175780a
- test_inventory.py5.7 KB · c9b1871ab60e
5 files, 26 KB in total. Each file has a sha256 in the pin's manifest; the first 12 characters are shown.
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