菜单
Detect and anonymize PII (SSN, cards, emails, phones, names) and business data (companies, revenue, costs, pricing). Use when handling sensitive files or user requests anonymization. Check-only or full anonymization with 5 strategies (mask/hash/pseudo/token/mixed). GDPR/HIPAA aware.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Diagnose an agent FAILURE and prescribe the cheapest/earliest guardrail to add NEXT — then scaffold it ready to paste. Craft-on-the-fly, friction-driven, minimal: grow a harness to fit the real failure mode instead of designing one upfront. Sibling of /pick-model (picks the model) and /pick-workflow (picks the topology); this picks the GUARDRAIL. Domain-general — any agentic task, not just code. Use when 'agent keeps failing at X', 'it hallucinated/broke the contract/wasted tokens', 'what guardrail do I add', 'harness for X', 'how do I stop it doing Y', or when starting a new agentic task and you want a minimal starter harness. Recommends + scaffolds; does NOT run the task.
Resume session from CONTEXT-llm.md. Use when resuming work, loading saved context, continuing a previous session. Triggers include "load context", "resume session", "continue where I left off".
Decide HOW work should EXECUTE — linear vs parallel fan-out, sub-agents vs teams vs dynamic Workflow, and the per-step seam — instead of defaulting to linear+single-model. Produces an execution design, does NOT run the task. Delegates the per-step model+effort call to a model-picker skill when the environment has one. Use when authoring/challenging a skill or agent, AND mid-task when deciding how to run real work — 'should I fan this out', 'should this fan out', 'parallel or linear', 'sub-agents or workflow', 'per-step seam', 'execution topology', 'execution architecture'. For a trivial few-item call, early-exit to linear cheaply rather than a full analysis.
Save session to CONTEXT-llm.md with conversation summary. Use when saving work, checkpointing progress, preserving session state. Triggers include "save context", "save session", "checkpoint", "save my progress".
Challenge, push back, play devil's advocate on AI output or intentions. Use when: challenge this, are you sure, push back, prove it, what if you're wrong, devil's advocate, stress test, poke holes, second opinion, sanity check, too confident, really?, question this decision, avant de commencer, je veux faire X, aide-moi à décider. Modes: route (default — any invocation whose first word isn't a recognized subcommand, including a free-text description), forward (before starting), anchor (committed too fast), verify (facts wrong?), framing (wrong problem?), deep (full devil's advocate in fresh context).
Capture a session's verbatim "meat" — the exact phrasing of constraints, rejected paths, corrections, live reasoning, learnings, pivots, open questions — into CHECKPOINT-*-llm.md, plus a synthesized summary of state. Use before /clear on a long reasoning session, when exact wording matters, or when saving a session you'll resume cold. Triggers: "checkpoint", "save the meat", "capture verbatim", "checkpoint before clear".
| name | anonymize-doc |
| description | Detect and anonymize PII (SSN, cards, emails, phones, names) and business data (companies, revenue, costs, pricing). Use when handling sensitive files or user requests anonymization. Check-only or full anonymization with 5 strategies (mask/hash/pseudo/token/mixed). GDPR/HIPAA aware. |
| allowed-tools | ["Read","Bash","Write"] |
| model | sonnet |
| context | main |
Detect and anonymize PII + sensitive business data using ML-powered detection (Scrubadub + spaCy NER).
SKILL_DIR="$(dirname "$(realpath "$0")" 2>/dev/null || echo /home/mat/dev/agent-skills/dstoic/skills/anonymize-doc)"
pip install -q -r "$SKILL_DIR/requirements.txt" && python -m spacy download -q en_core_web_sm 2>/dev/null
Question: "Check for PII/business data (detection only) or anonymize?"
python "$SKILL_DIR/scripts/detect.py" <file_path>Ask strategy:
| Strategy | Use Case | Reversible | GDPR |
|---|---|---|---|
mask | Max privacy, redaction | No | ✅ Full |
hash | Analytics, tracking | No | ✅ Full |
pseudo | Demos, case studies | Yes | ⚠️ Partial |
token | Financial, vault-backed | Yes | ⚠️ Partial |
mixed | Complex docs (auto per severity) | Mixed | ⚠️ Partial |
Run: python "$SKILL_DIR/scripts/anonymize.py" <file_path> --strategy <choice>
Outputs: <file>-anonymized.<ext> + <file>-audit-log.json
.gitignore has *-audit-log.jsonSee reference.md for entity types, severity tiers, compliance details.
See examples.md for before/after samples.