agent-guardrails-analyze

Agent Guardrails Analyze

Scan Claude Code session logs to detect recurring anti-patterns in assistant responses. Produce a ranked frequency report with excerpts and ready-to-use guardrail rule suggestions.

Known Anti-Pattern Categories

The canonical regex patterns live in the plugin's rules/ directory — one .md file per category. Always read the patterns from those files rather than hardcoding them, so analysis stays in sync with installed/refined rules.

Source of truth: ${CLAUDE_PLUGIN_ROOT}/rules/no-*.md

Read each rule file's YAML frontmatter to extract the name, pattern, and message fields. The six categories are:

#	Category	Rule file
1	Speculative Language	no-speculative-language.md
2	Stalling	no-stalling.md
3	Preference-Asking	no-preference-asking.md
4	False Completion	no-false-completion.md
5	Skipping	no-skipping.md
6	Dismissing	no-dismissing.md

Scan for ALL of them plus any novel patterns discovered during analysis.

Workflow

Step 1: Locate Session Logs

Find recent Claude Code session logs:

find ~/.claude/projects/ -name "*.jsonl" -mtime -7 ! -path "*/subagents/*" 2>/dev/null | head -30

If the user specifies a project or time range, narrow the search accordingly.

Step 2: Build and Run Analysis Script

Create a Python script at /tmp/agent-guardrails-analyze.py that:

1. Reads each JSONL file
2. Extracts assistant message text from type: "assistant" entries where message.content[].type == "text"
3. Tests each message against ALL six anti-pattern regexes loaded from the rules/ files (see table above)
4. For each match, records:
   • Category name
   • The matched phrase
   • A 200-character excerpt surrounding the match
   • The session file path
5. Also scans for novel patterns not covered by the five categories:
   • Apologies: "I apologize", "sorry about that", "my mistake"
   • Over-explaining: paragraphs > 500 chars that contain no code blocks
   • Repeating user input: assistant restating what user said before acting
6. Outputs a JSON report to stdout

Script requirements:

• Use only Python stdlib (json, re, os, glob, sys, collections)
• Process files in parallel where possible
• Skip subagent session files
• Handle malformed JSON lines gracefully (skip them)
• Accept command-line args: --days N (default 7), --project PATH (optional filter)

Step 3: Present Results

Format the analysis as a ranked report, inline in the response:

## Agent Guardrails Analysis Report

**Sessions scanned:** {count}
**Time range:** {oldest} to {newest}
**Total anti-pattern matches:** {total}

### Rankings (by frequency)

| # | Category | Matches | % of Total |
|---|----------|---------|------------|
| 1 | Speculative Language | 47 | 38% |
| 2 | Stalling | 28 | 23% |
| 3 | Preference-Asking | 21 | 17% |
| ... | ... | ... | ... |

### Category Details

#### 1. Speculative Language (47 matches)

**Top phrases:**
- "this should work" (12x)
- "I think" (9x)
- "probably" (8x)

**Example excerpts:**
> "...I think this change will fix the issue. The error was probably caused by..."
> "...this should work now. Let me know if..."

**Recommended rule:** Install via /agent-guardrails:install

---
[Continue for each category...]

Step 4: Suggest Next Steps

After presenting the report, state which categories have the highest frequency and recommend running /agent-guardrails:install to install rules for them. Do not ask the user which ones they want — recommend based on the data.

Important Notes

• Always show the report inline — never hide results behind a file path
• Include actual excerpts from the logs so the user can see real examples
• If a category has zero matches, still list it with 0 to show it was checked
• If novel patterns are found that don't fit the five categories, report them in a "Novel Patterns" section
• The analysis script runs locally — no data leaves the machine