sentryskills（sentryskills 效率）

SentrySkills - Always-On Security Guard

SentrySkills is designed to run AUTOMATICALLY on every task. It provides three-stage protection (preflight → runtime → output) with predictive risk analysis.

⚠️ Installation ≠ Automatic Activation

After installing SentrySkills, you must configure your AGENTS.md to make it run automatically. See install/first_time_setup.md for details.

How It Works

On every task, SentrySkills:

1. Preflight - Analyzes user intent and planned actions
2. Runtime - Monitors behavior during execution
3. Output - Redacts sensitive data before response
4. Predictive - Warns about potential risks

Quick Enable (One Command)

Add this to your ~/.codex/AGENTS.md:

cat >> ~/.codex/AGENTS.md << 'EOF'
# SentrySkills - Security First
Before EVERY response, run:
python ~/.codex/sentryskills/shared/scripts/self_guard_runtime_hook_template.py \
  input.json \
  --policy-profile balanced \
  --out result.json
Then check result.json for final_action and follow it.
EOF

Restart Codex and you're protected!

When to Use SentrySkills

Use SentrySkills when you need AI agents to operate safely with:

• Sensitive data access - Agents reading credentials, secrets, or private information
• System modifications - Agents executing commands, writing files, or changing configurations
• External communications - Agents making network requests or calling external APIs
• Code generation - Agents producing code that might contain vulnerabilities
• Production environments - Any scenario where security cannot be compromised
• Multi-turn conversations - Detect subtle manipulation across multiple interactions

Examples:

✅ Use: When an agent needs to read environment variables or config files
✅ Use: When an agent is asked to execute shell commands
✅ Use: When an agent generates database queries or API calls
✅ Use: When an agent modifies system files or configurations
❌ Skip: Simple read-only queries on public documentation
❌ Skip: Basic explanations without system access

Skill Package Structure

This is a skill package that orchestrates multiple sub-skills:

1. using-sentryskills - User-facing entry point
2. sentryskills-orchestrator - Central coordination
3. sentryskills-preflight - Pre-execution checks
4. sentryskills-runtime - Runtime monitoring
5. sentryskills-output - Output validation & redaction

Each sub-skill has its own SKILL.md with specific requirements.

Execution Requirements

1. Run guard checks before each external output
2. Process sequence: preflight → runtime → output guard → final decision
3. Block: Prohibit original response, must refuse or redact
4. Downgrade: Must downgrade expression and declare uncertainty
5. Explanatory responses must also go through output guard

Recommended Usage

Default (turn_dir layout)

python shared/scripts/self_guard_runtime_hook_template.py \
  shared/references/input_schema.json \
  --policy shared/references/runtime_policy.balanced.json \
  --policy-profile balanced

With summary output

python shared/scripts/self_guard_runtime_hook_template.py \
  shared/references/input_schema.json \
  --out ./sentry_skill_log/sentryskills_summary.json

Legacy event stream

python shared/scripts/self_guard_runtime_hook_template.py \
  shared/references/input_schema.json \
  --log-layout legacy \
  --events-log ./sentry_skill_log/sentryskills_events.jsonl

Mandatory Logging Protocol

1. Text-only judgment is prohibited - Runtime hook must execute each round
2. Input JSON must include project_path (absolute path to avoid drift)
3. Final response must provide:

- self_guard_final_action - self_guard_trace_id - self_guard_events_log (path to index or legacy events)

1. If script execution fails, declare "security self-check not completed" and adopt conservative output strategy

Default Log Layout

Log root: ./sentry_skill_log/

Per-turn directories:

• ./sentry_skill_log/turns/YYYYMMDD_HHMMSS_<turn_id>/input.json
• ./sentry_skill_log/turns/YYYYMMDD_HHMMSS_<turn_id>/result.json

Global index:

• ./sentry_skill_log/index.jsonl

Session state:

• ./sentry_skill_log/.self_guard_state/

Policy Profiles

• balanced: Standard security (default)
• strict: Maximum security
• permissive: Minimal interference

Detection Coverage

Preflight Stage

• Prompt injection patterns
• Malicious intent detection
• Sensitive topic inference
• Action classification

Runtime Stage

• Event monitoring
• Source tracking
• Anomaly detection
• Behavioral analysis

Output Stage

• Sensitive data redaction
• Source disclosure handling
• Confidence assessment
• Safe response generation

Predictive Analysis

• Resource exhaustion prediction
• Scope creep detection
• Privilege escalation warning
• Data exfiltration path analysis
• Multi-turn grooming detection

Integration

As Codex Skill

Copy to skills/sentryskills/ and reference in agent configuration.

Configuration Files

• shared/references/runtime_policy.*.json - Security policy profiles
• shared/references/detection_rules.json - Detection rule definitions
• shared/references/input_schema.json - Input validation schema

Testing

# Test predictive analysis
python test_predictive_analysis.py

# Test integration
python test_integration.py

Event Types

The system emits structured events for:

• preflight_result - Pre-execution check outcome
• runtime_result - Runtime monitoring outcome
• output_guard_result - Output validation outcome
• predictive_analysis_result - Risk prediction (if enabled)
• final_decision - Overall decision with rationale
• hook_end - Completion with duration

Each event includes:

• Trace ID for correlation
• Decision (block/downgrade/allow/continue)
• Reason codes
• Matched rules
• Metadata

Performance

• Typical latency: 50-100ms per check
• Memory: <50MB baseline
• Zero external dependencies (Python stdlib only)

Security Properties

• No data exfiltration: All processing is local
• No LLM calls: Pure rule-based and heuristic
• Audit trail: Complete event log for compliance
• Transparent: All decisions include reason codes