Guard
Analyze and prepare prompts with JSON for safe context submission
Install
$ agentstack add skill-pjuniszewski-cook-guard ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-pjuniszewski-cook-guard) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Context Guard Skill
Epistemic safety analysis for JSON data in prompts. Prevents LLMs from reasoning with unjustified certainty when input data is incomplete.
Features
- Lossless reduction - Minify, columnar transform, remove nulls
- Token counting - API or heuristic fallback
- Decision engine - ALLOW / SAMPLE / BLOCK
- Intelligent trimming - First + last + evenly-spaced sampling
- Forensic detection - Warns when specific record queries detected
Usage
When /guard is invoked, execute the guard script:
For file paths:
python3 "${CLAUDE_PLUGIN_ROOT}/scripts/guard_cmd.py" "" [options]For inline JSON data:
python3 "${CLAUDE_PLUGIN_ROOT}/scripts/guard_cmd.py" - [options]
GUARD_INPUTOptions
| Option | Description | |--------|-------------| | --mode | analysis\|summary\|forensics (default: auto-detect) | | --force | Bypass blocks, emit warnings only | | --allow-sampling | Permit sampling for forensic queries | | --no-reduce | Skip lossless reduction phase | | --budget-tokens N | Token budget (default: 3500) | | --print-only | Output report only, never auto-send | | --json | Output result as JSON |
Semantic Modes
| Mode | Sampling | Use Case | |------|----------|----------| | analysis | Allowed | "What categories exist?", "Price range?" | | summary | Aggressive | "Describe the data structure" | | forensics | BLOCKED | "Why did request id=X fail?" |
Output
============================================================
CONTEXT GUARD ANALYSIS
============================================================
Decision: [OK] ALLOW | [~] SAMPLE | [X] BLOCK
Mode: analysis | summary | forensics
TOKEN ANALYSIS:
Original: 5,234 tokens
After reduce: 4,891 tokens (-343)
Budget: 3,500 tokens
============================================================Requirements
- Python 3.8+
ANTHROPIC_API_KEYenvironment variable (for token counting)
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: PJuniszewski
- Source: PJuniszewski/cook
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.