Update Stop Gate

Update Stop Gate

Add a new stall-detection rule to the autonomy classifier. Two files must be updated in sync.

Files

Procedure

1. Read both files

Read ~/.claude/autonomy-rules.md and ~/.claude/hooks/stop-gate.py in parallel. Note:

• The last lettered classifier check (to know what letter comes next)
• The structure of existing rule bullets in the "WHAT DOES NOT REQUIRE ASKING" section

2. Generalize from the user's example

The user gives a specific incident. Extract the class of behavior, not the specific case. Ask:

• What cognitive error causes this? (e.g., "silencing the symptom feels like fixing the problem")
• What are 5-8 concrete manifestations across different domains?
• What is the ONE narrow exception where this behavior is legitimate?

Never use the user's specific variable names, function names, or project details in the rule.

3. Scrub for internal/proprietary language

Before writing anything, scan the user's description for company-specific terms (product names, internal tool names, team names, proprietary library names). The rules files must be generic — they contain NO employer-specific language. Replace any such terms with generic equivalents.

4. Add rule to autonomy-rules.md

Insert a new bullet in the "WHAT DOES NOT REQUIRE ASKING" section, following this exact pattern:

- **{Bold header}** — {one-sentence description of the behavior class and the cognitive error}. All of these are stalls:
  - {Concrete manifestation 1}
  - {Concrete manifestation 2}
  - {Concrete manifestation 3} 
  - {4-8 total manifestations, each a specific observable action}
  - The ONLY time {this behavior} is legitimate: {narrow exception with specific conditions that must ALL be met}

Place it logically near related existing rules (e.g., a new "bad fix quality" rule goes near "evidence-free dismissal" and "minimum-viable-effort evasion").

5. Add classifier check to stop-gate.py

In the build_classifier_prompt() function, find the last lettered stall check and add the next one. Follow this exact pattern:

  ({next_letter}) {Header} stall — ALL of:
    - The last assistant message {observable trigger condition}
    - {Specific evidence of the bad behavior in the message}
    - {What is MISSING from the message that should be there}
    - {Why it's plausibly within Claude's ability to do better}

The classifier check must be:

• Mechanically evaluable from the assistant message text
• Based on observable signals (what's present AND what's absent)
• 3-5 bullet points (not more — the classifier prompt is already long)

6. Verify

6a. Python syntax: Run: python3 -c "import py_compile; py_compile.compile('$HOME/.claude/hooks/stop-gate.py', doraise=True)"

If it fails, fix the syntax error (usually an unescaped quote or bad indentation in the f-string).

6b. No format constraint. Fast mode sends the full autonomy-rules.md to Haiku — no extraction, no regex, no special formatting required. Write rules in whatever markdown format is clearest for humans. Both fast and full mode see every rule automatically.

Golden Rules

1. Generalize, never overfit. The user's example is ONE instance. The rule must catch the class across all domains.
2. Rules must be OSS-safe. No company names, product names, internal tool names, or proprietary terms. Ever.
3. Both files or neither. Never update autonomy-rules.md without updating the classifier check in stop-gate.py, or vice versa. They must stay in sync.
4. Cognitive error first. Every rule header includes WHY the agent makes this mistake. Without the "why," the classifier can't distinguish the bad behavior from a legitimate action.
5. Narrow escape hatches. The "ONLY time X is legitimate" clause must have specific conditions, not vague qualifiers. "When you've investigated and confirmed the root cause is outside your control" is good. "When appropriate" is not.
6. Verify Python syntax. The classifier prompt is inside an f-string. Curly braces in the prompt must be doubled ({{ / }}). Always compile-check after editing.