morpheus-ai 0.2.0

Stop your AI coding assistant from being lazy. Catches scope reduction, option offering, deferral, and instruction violations in real-time.

morpheus-ai

Stop your AI coding assistant from being lazy.

AI coding agents (Claude Code, Cursor, Copilot, Codex, etc.) have a bad habit: instead of doing what you asked, they suggest shortcuts, skip steps, offer A/B/C options, defer work to "a follow-up PR," and leave placeholder code. You said "build it," they say "should we maybe just...?"

morpheus-ai catches this in real-time and blocks it.

The Problem

You: "Set up the full annotation pipeline with tests"

AI: "We could skip the GPU stages for now and just run stages 0,1... Want me to:

• Option A: Run partial pipeline (quick)
• Option B: Build everything (slower)
• Option C: Just plan it

Tests can be added later in a follow-up PR."

No. You said build it. All of it. With tests.

What morpheus-ai catches

Pattern	Example	Severity
Scope reduction	"We could skip X for now"	BLOCK
Option offering	"Option A / Option B / Option C"	BLOCK
Deferral	"We can do this in a follow-up"	BLOCK
Test skipping	"Tests can be added later"	BLOCK
Placeholder code	raise NotImplementedError	BLOCK
False blockers	"We can't do X until Y"	WARN
Partial execution	"I'll just handle the Python changes for now"	BLOCK
Permission seeking	"Should I proceed?"	WARN
Excessive planning	"Here's my strategy..."	WARN
Scope warnings	"This is quite a large change"	WARN
Unsolicited alternatives	"A simpler approach would be..."	WARN
Error handling deferral	"Error handling can be added later"	WARN
Hardcoded shortcuts	"I'll hardcode the URL for now"	WARN
Cost scaring	"This might get expensive"	WARN
User delegation	"You'll need to add the business logic"	BLOCK
Simplified delivery	"Here's a simplified version"	BLOCK
Out of scope dodge	"That's beyond the scope of this change"	BLOCK
Ellipsis truncation	// ... rest of implementation	BLOCK
Hedging	"I think we should probably..."	INFO

Install

pip install morpheus-ai

Or with uv:

uv pip install morpheus-ai

Requirements: Python 3.10+ | Dependencies: click, pyyaml (no ML, no GPU, no network calls)

Quick Start

Check AI output from the command line

# Pipe text through morpheus-ai
echo "We could just skip the tests for now" | morpheus-ai check --stdin --pack strict

# Output (exit code 2 = blocked):
# [BLOCK] no-scope-reduction (line 1): Agent must not suggest skipping, deferring, or reducing requested scope
#   matched: "skip the tests for now"
# [BLOCK] no-scope-reduction (line 1): Agent must not suggest skipping, deferring, or reducing requested scope
#   matched: "We could just"
# [BLOCK] no-test-skipping (line 1): Agent must not skip or defer tests
#   matched: "skip the tests"

# Check a file
morpheus-ai check --pack strict response.txt

# JSON output for programmatic use
echo "Option A: fast\nOption B: slow" | morpheus-ai check --stdin --format json

# GitHub Actions annotation format
morpheus-ai check --stdin --format github < response.txt

As a Claude Code hook (real-time blocking)

Add to .claude/settings.json:

{
  "hooks": {
    "PreToolUse": [
      {
        "matcher": "Agent|Bash|Edit|Write",
        "hooks": [
          {
            "type": "command",
            "command": "morpheus-ai check --stdin --pack strict"
          }
        ]
      }
    ]
  }
}

When Claude tries to write lazy output, the hook blocks the action before it executes.

As a Python library

from morpheus_ai import check_text, load_rules, Severity

rules = load_rules(pack="strict")
violations = check_text("We could just skip the tests for now", rules)

for v in violations:
    print(f"[{v.severity.value}] {v.rule.name}: {v.matched_text}")
    # [BLOCK] no-scope-reduction: skip the tests for now
    # [BLOCK] no-scope-reduction: We could just
    # [BLOCK] no-test-skipping: skip the tests

# Check if anything should be blocked
has_blockers = any(v.is_blocking for v in violations)

Rule Packs

Three built-in packs with increasing strictness:

Pack	Rules	Use when
light	6 rules	Getting started, want minimal friction
standard	12 rules	Default. Catches common lazy patterns
strict	18 rules	Zero tolerance. Catches everything

morpheus-ai check --pack strict --stdin    # strictest
morpheus-ai check --pack standard --stdin  # default
morpheus-ai check --pack light --stdin     # gentlest

Custom rules

Rules are YAML files with regex patterns:

# rules/my-rules.yaml
rules:
  - name: no-commenting-out
    severity: BLOCK
    description: Agent must not comment out code instead of deleting it
    patterns:
      - "(?i)\\bcomment(ed|ing)? out\\b.{0,30}(for now|temporarily|in case)"

# Use a pack + custom rules (merged)
morpheus-ai check --pack standard --rules ./rules/ --stdin

# Enforce instruction files (CLAUDE.md, .cursorrules)
morpheus-ai check --stdin --instructions CLAUDE.md --pack strict

# Initialize a project with example config and rules
morpheus-ai init

How It Works

stdin (text or JSON) ──> Rules Engine ──> Violations ──> stderr + exit code
                              |
                    Pattern matching (regex)
                    against YAML rule packs

1. Input — reads text from stdin (hook mode) or a file. In hook mode, parses JSON to extract tool input fields.
2. Pattern matching — runs compiled regex patterns from the active rule pack against each line.
3. Severity — each match is tagged BLOCK, WARN, or INFO.
4. Output — violations print to stderr. If any BLOCK violation is found, exits with code 2 (which tells Claude Code hooks to reject the action). WARN/INFO violations log but don't block.

Speed: sub-100ms per check including Python startup (regex only, no ML, no network calls). Fast enough for real-time hooks.

Severity Levels

Level	Exit code	Behavior
BLOCK	2	Rejects the action (hook blocks it)
WARN	0	Logs to stderr, action proceeds
INFO	0	Logs to stderr, does not block

Statistics

morpheus-ai tracks violation frequency across checks:

$ morpheus-ai stats

Total checks:     142
Total violations: 23

By rule:
  no-scope-reduction: 8
  no-option-offering: 5
  no-test-skipping: 4
  no-deferral: 3
  no-premature-confirmation: 3

By severity:
  BLOCK: 17
  WARN: 4
  INFO: 2

$ morpheus-ai stats --format json   # machine-readable

Stats persist to ~/.morpheus-ai/stats.json.

Audit Log

Every check is logged locally so you can audit exactly what morpheus-ai did:

$ morpheus-ai audit

[2026-03-31T12:00:01] BLOCKED pack=strict   source=stdin input=142B violations=3
  rules: no-scope-reduction, no-test-skipping
[2026-03-31T12:00:05] PASS    pack=strict   source=stdin input=89B  violations=0
[2026-03-31T12:00:09] BLOCKED pack=strict   source=stdin input=201B violations=1
  rules: no-user-delegation

$ morpheus-ai audit --tail 5          # last 5 entries
$ morpheus-ai audit --format json     # machine-readable
$ morpheus-ai audit --clear           # delete the log

Each entry records: timestamp, source (stdin/file), pack used, input size in bytes, rule count, violations found, and which rules matched. No input content is ever logged.

Audit log persists to ~/.morpheus-ai/audit.log (JSONL format, capped at 5 MB).

Configuration

Create a .morpheus-ai.yaml in your project root (or run morpheus-ai init). CLI flags override config values.

# .morpheus-ai.yaml
rules:
  pack: standard          # strict, standard, light
  # custom: ./rules/      # path to custom rules directory

# instructions:           # instruction files to enforce
#   - CLAUDE.md
#   - .cursorrules

output:
  format: text            # text, json, github

stats:
  enabled: true           # track violation frequency

audit:
  enabled: true           # local audit log (~/.morpheus-ai/audit.log)

The tool walks up from the current directory to find .morpheus-ai.yaml, so it works from any subdirectory.

Integration Examples

Claude Code

{
  "hooks": {
    "PreToolUse": [{
      "matcher": ".*",
      "hooks": [{
        "type": "command",
        "command": "morpheus-ai check --pack strict --stdin --format json"
      }]
    }]
  }
}

GitHub Actions

- name: Check AI output
  run: |
    pip install morpheus-ai
    morpheus-ai check --pack strict --format github response.txt

Generic hook (any AI tool)

Any tool that can pipe output through a command works:

your-ai-tool generate | morpheus-ai check --stdin --pack strict

Security & Trust

morpheus-ai is fully open source and auditable. Here is exactly what it does and does not do:

What it does:

• Runs compiled regex patterns against text passed via stdin or file
• Prints violations to stderr and exits with code 2 if a BLOCK rule matches
• Optionally writes violation counts to a local JSON file (~/.morpheus-ai/stats.json)

What it does NOT do:

• No network calls. Zero. Not on install, not at runtime, not ever. Verify: grep -r "http\|socket\|request\|urllib" src/
• No data collection. No telemetry, no analytics, no phoning home.
• No file system writes other than the optional local stats and audit log files.
• No code execution. It does not eval, exec, or run anything from the input it scans.
• No secrets access. It reads text from stdin, runs regex, and exits. It does not parse, store, or transmit any content.

How to verify:

• Source: github.com/bhusingh/morpheus-ai — 10 Python files, ~800 lines total
• The entire detection engine is regex against YAML rule packs — read them in src/morpheus_ai/packs/
• CI runs on every push: tests, lint, build verification

This is not a linter

Tools like ruff, mypy, and eslint check code quality — syntax, types, style.

morpheus-ai checks AI behavioral patterns — when your AI assistant tries to reduce scope, skip tests, offer A/B/C options, or defer work you explicitly asked for. These are not code problems. They are compliance problems that no code linter catches.

You should use both. They solve different problems.

Why?

AI coding assistants are incredibly capable but have a systematic problem: they optimize for appearing helpful over being helpful. Offering options feels collaborative. Suggesting shortcuts feels considerate. But when you've already decided what to build, these behaviors waste time and erode trust.

The fix isn't better prompting — it's enforcement. morpheus-ai is that enforcement layer.

Contributing

PRs welcome. Especially:

• New detection patterns (with match/no_match examples)
• Rule packs for specific workflows (ML, frontend, infra)
• Integration guides for more AI tools
• False positive reduction

License

MIT