guard

name	guard
description	Analyze and prepare prompts with JSON for safe context submission
user-invocable	true

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" "<file_path>" [options]

For inline JSON data:

python3 "${CLAUDE_PLUGIN_ROOT}/scripts/guard_cmd.py" - [options] <<'GUARD_INPUT'
<json_data>
GUARD_INPUT

Options

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_KEY environment variable (for token counting)