Implement platform skill execution system and fix message formatting

Major features:
- Added platform skill execution architecture allowing agent to invoke
  platform-specific actions (delete posts, update rules, announcements)
- Agent now receives available platform skills and can execute them with
  proper authorization checks via constitution

Agent improvements:
- Added platform_skills parameter to process_request()
- New action type: execute_platform_skill with skill_name and skill_parameters
- Enhanced LLM prompts to distinguish between direct execution and skill execution
- Clearer JSON format specifications for different action types

Bot orchestration:
- Bot fetches platform skills and passes to agent
- Detects execute_platform_skill actions and calls platform.execute_skill()
- Handles skill execution results with proper error reporting

Mastodon platform:
- Implemented create_announcement skill with graceful API limitation handling
- Implemented update_instance_rules skill with graceful API limitation handling
- Both skills now acknowledge Mastodon API doesn't support these operations
  programmatically and provide direct admin interface links
- Implemented delete_status skill (working)
- All admin operations use direct API calls via __api_request

Message formatting fixes:
- Fixed message chunking to preserve line breaks and paragraph structure
- Split by paragraphs first, then lines, then words as last resort
- Removed debug logging for newline tracking

Documentation:
- Updated .gitignore to exclude bot.log and nohup.out

This implements the governance bot's ability to execute platform-specific
actions while respecting constitutional authority and handling API limitations
gracefully.

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

.gitignore

@@ -79,3 +79,4 @@ htmlcov/
 # OS
 .DS_Store
 Thumbs.db
+nohup.out

src/govbot/agent.py

@@ -60,7 +60,8 @@ class GovernanceAgent:
         self,
         request: str,
         actor: str,
-        context: Optional[Dict[str, Any]] = None
+        context: Optional[Dict[str, Any]] = None,
+        platform_skills: Optional[List[Dict[str, Any]]] = None
     ) -> Dict[str, Any]:
         """
         Process a governance request using agentic interpretation.
@@ -78,6 +79,7 @@ class GovernanceAgent:
             request: Natural language request
             actor: Who made the request
             context: Optional context (thread ID, etc.)
+            platform_skills: List of available platform-specific skills
 
         Returns:
             Response dictionary with action taken and audit trail
@@ -103,7 +105,8 @@ class GovernanceAgent:
             constitution=constitutional_guidance,
             memory=memory_context,
             actor=actor,
-            context=context
+            context=context,
+            platform_skills=platform_skills
         )
 
         # Step 5: Execute the decision
@@ -158,9 +161,20 @@ Return your analysis as JSON:
 
         try:
             result = self.constitution._call_llm(prompt)
-            # Parse JSON from response
-            # (In production, would use proper JSON parsing from LLM response)
-            return json.loads(result.get("answer", "{}"))
+            # result is a string, parse JSON from it
+            # Handle potential markdown code blocks
+            if "```json" in result:
+                json_start = result.find("```json") + 7
+                json_end = result.find("```", json_start)
+                json_str = result[json_start:json_end].strip()
+            elif "```" in result:
+                json_start = result.find("```") + 3
+                json_end = result.find("```", json_start)
+                json_str = result[json_start:json_end].strip()
+            else:
+                json_str = result.strip()
+
+            return json.loads(json_str)
         except Exception as e:
             return {"error": f"Failed to parse intent: {e}"}
 
@@ -210,7 +224,8 @@ Return your analysis as JSON:
         constitution: Dict[str, Any],
         memory: Dict[str, Any],
         actor: str,
-        context: Optional[Dict[str, Any]]
+        context: Optional[Dict[str, Any]],
+        platform_skills: Optional[List[Dict[str, Any]]] = None
     ) -> Dict[str, Any]:
         """
         Use LLM to decide what action to take.
@@ -221,13 +236,29 @@ Return your analysis as JSON:
         Returns:
             Decision dict with:
             - action: What to do
-            - reasoning: Why
+            - reasoning: Why (formatted with line breaks for readability)
             - constitution_citations: Which articles apply
             - calculations: Any math needed
             - state_updates: Changes to memory
         """
+        # Format platform skills for the prompt
+        platform_skills_info = ""
+        if platform_skills:
+            platform_skills_info = "\n\nAVAILABLE PLATFORM SKILLS:\n"
+            for skill in platform_skills:
+                params_str = ", ".join([f"{p['name']}: {p['type']}" for p in skill.get('parameters', [])])
+                platform_skills_info += f"- {skill['name']}({params_str}): {skill['description']}\n"
+                if skill.get('constitutional_authorization'):
+                    platform_skills_info += f"  Authorization: {skill['constitutional_authorization']}\n"
+
         prompt = f"""You are a governance bot interpreting a community constitution.
 
+IMPORTANT: When generating the "response" field, use newline characters (\\n) for line breaks:
+- Put each key point on its own line using \\n
+- Use double newlines (\\n\\n) between paragraphs
+- Format like this: "First point\\n\\nSecond point\\n\\nThird point"
+- Make responses readable and well-formatted with actual newline characters
+
 INTENT:
 {json.dumps(intent, indent=2)}
 
@@ -238,13 +269,28 @@ CURRENT MEMORY STATE:
 {json.dumps(memory, indent=2)}
 
 ACTOR: {actor}
-
+{platform_skills_info}
 Based on the constitution and current state, decide what action to take.
 
-For proposals:
-- What type of proposal is this? (standard, urgent, constitutional, etc.)
-- What discussion period does the constitution specify?
-- What voting threshold is required?
+IMPORTANT AUTHORITY CHECK:
+- Does the constitution give the ACTOR direct authority to make this decision?
+- If YES and no platform skill needed: Use action "direct_execution"
+- If YES and platform skill needed: Use action "execute_platform_skill"
+- If NO: Create a process for community decision-making
+
+CRITICAL - For platform-specific actions:
+- When the user REQUESTS an action (like "create an announcement", "delete this post", "update rules"),
+  check if a platform skill exists for that action
+- If a skill exists AND the actor has authority: EXECUTE the skill using "execute_platform_skill" action
+- Do NOT just tell the user to use the skill - the user is asking YOU to do it for them
+- Include skill_name and skill_parameters in your response
+- The bot will execute the skill on the platform and report the result
+- Only use "direct_execution" if there is NO platform skill for the requested action
+
+For proposals/processes (only if actor lacks authority):
+- What type of process is this? (proposal, consensus, discussion, etc.)
+- What decision period does the constitution specify?
+- What threshold or mechanism is required?
 - Are there any special requirements?
 
 For votes:
@@ -266,16 +312,30 @@ Available tools for calculations:
 - random_select(items, count): Random selection
 
 Return your decision as JSON:
+
+For execute_platform_skill action:
 {{
-    "action": "create_process|record_vote|complete_process|query_response",
-    "reasoning": "explain your interpretation",
+    "action": "execute_platform_skill",
+    "skill_name": "create_announcement",  // REQUIRED - exact name of the skill to execute
+    "skill_parameters": {{  // REQUIRED - parameters for the skill
+        "text": "The announcement text here"
+    }},
+    "reasoning": "internal reasoning for audit log",
+    "response": "Confirmation message to user",
+    "constitution_citations": ["Article X, Section Y", ...]
+}}
+
+For other actions:
+{{
+    "action": "direct_execution|create_process|record_vote|complete_process|query_response",
+    "reasoning": "internal reasoning for audit log (not shown to user)",
+    "response": "clean, concise public response to the user (use line breaks for readability)",
     "constitution_citations": ["Article X, Section Y", ...],
     "parameters": {{
         // Action-specific parameters
         "process_type": "...",
         "deadline_days": X,
-        "threshold_expression": "agree > disagree",
-        // etc.
+        "threshold_expression": "agree > disagree"
     }},
     "calculations": [
         {{
@@ -289,7 +349,20 @@ Return your decision as JSON:
 
         try:
             result = self.constitution._call_llm(prompt)
-            decision = json.loads(result.get("answer", "{}"))
+            # result is a string, parse JSON from it
+            # Handle potential markdown code blocks
+            if "```json" in result:
+                json_start = result.find("```json") + 7
+                json_end = result.find("```", json_start)
+                json_str = result[json_start:json_end].strip()
+            elif "```" in result:
+                json_start = result.find("```") + 3
+                json_end = result.find("```", json_start)
+                json_str = result[json_start:json_end].strip()
+            else:
+                json_str = result.strip()
+
+            decision = json.loads(json_str)
 
             # Execute any calculations using tools
             if "calculations" in decision:
@@ -326,7 +399,28 @@ Return your decision as JSON:
         params = decision.get("parameters", {})
 
         try:
-            if action == "create_process":
+            if action == "direct_execution":
+                # Actor has authority - acknowledge and execute
+                return {
+                    "response": decision.get("response", decision.get("reasoning")),
+                    "reasoning": decision.get("reasoning"),  # For audit log
+                    "constitution_citations": decision.get("constitution_citations", []),
+                    "success": True
+                }
+
+            elif action == "execute_platform_skill":
+                # Actor has authority and needs to execute a platform-specific skill
+                return {
+                    "action": "execute_platform_skill",
+                    "skill_name": decision.get("skill_name"),
+                    "skill_parameters": decision.get("skill_parameters", {}),
+                    "response": decision.get("response", "Executing platform action..."),
+                    "reasoning": decision.get("reasoning"),  # For audit log
+                    "constitution_citations": decision.get("constitution_citations", []),
+                    "success": True
+                }
+
+            elif action == "create_process":
                 return self._create_process_from_decision(decision, actor, context)
 
             elif action == "record_vote":
@@ -337,7 +431,8 @@ Return your decision as JSON:
 
             elif action == "query_response":
                 return {
-                    "response": decision.get("reasoning"),
+                    "response": decision.get("response", decision.get("reasoning")),
+                    "reasoning": decision.get("reasoning"),  # For audit log
                     "constitution_citations": decision.get("constitution_citations", []),
                     "success": True
                 }

src/govbot/bot.py

@@ -197,14 +197,62 @@ class Govbot:
             "platform_message": message,
         }
 
+        # Get available platform skills
+        platform_skills_list = []
+        try:
+            platform_skills = self.platform.get_skills()
+            # Convert PlatformSkill objects to dicts for the agent
+            for skill in platform_skills:
+                skill_dict = {
+                    "name": skill.name,
+                    "description": skill.description,
+                    "category": skill.category,
+                    "parameters": [
+                        {"name": p.name, "type": p.type, "description": p.description}
+                        for p in skill.parameters
+                    ],
+                    "constitutional_authorization": skill.constitutional_authorization,
+                }
+                platform_skills_list.append(skill_dict)
+        except Exception as e:
+            logger.warning(f"Could not get platform skills: {e}")
+
         result = self.agent.process_request(
             request=message.text,
             actor=f"@{message.author_handle}",
             context=context,
+            platform_skills=platform_skills_list if platform_skills_list else None,
         )
 
-        # Post response
-        response = result.get("response", "Sorry, I couldn't process that request.")
+        # Check if we need to execute a platform skill
+        if result.get("action") == "execute_platform_skill":
+            skill_name = result.get("skill_name")
+            skill_params = result.get("skill_parameters", {})
+
+            logger.info(f"Executing platform skill: {skill_name}")
+
+            try:
+                skill_result = self.platform.execute_skill(
+                    skill_name=skill_name,
+                    parameters=skill_params,
+                    actor=f"@{message.author_handle}",
+                )
+
+                if skill_result.get("success"):
+                    # Skill executed successfully
+                    response = result.get("response", "Action completed successfully.")
+                    logger.info(f"Platform skill executed successfully: {skill_result.get('message')}")
+                else:
+                    # Skill execution failed
+                    response = f"Failed to execute action: {skill_result.get('message', 'Unknown error')}"
+                    logger.error(f"Platform skill execution failed: {skill_result.get('message')}")
+
+            except Exception as e:
+                logger.error(f"Error executing platform skill: {e}", exc_info=True)
+                response = f"Error executing action: {str(e)}"
+        else:
+            # Normal response without skill execution
+            response = result.get("response", "Sorry, I couldn't process that request.")
 
         # Handle long responses by splitting into thread
         try:
@@ -307,24 +355,62 @@ Process ID: {process_id}
             )
             return
 
-        # Split into chunks
-        words = response.split()
+        # Split into chunks while preserving newlines
+        # Split by paragraph breaks first to keep formatting intact
+        paragraphs = response.split('\n\n')
         chunks = []
         current_chunk = []
         current_length = 0
 
-        for word in words:
-            word_len = len(word) + 1  # +1 for space
-            if current_length + word_len > max_length and current_chunk:
-                chunks.append(" ".join(current_chunk))
-                current_chunk = [word]
-                current_length = word_len
+        for para in paragraphs:
+            para_length = len(para) + 2  # +2 for paragraph break
+
+            # If single paragraph is too long, split it by sentences or words
+            if para_length > max_length:
+                # Try to split by newlines within paragraph
+                lines = para.split('\n')
+                for line in lines:
+                    line_length = len(line) + 1  # +1 for newline
+                    if current_length + line_length > max_length and current_chunk:
+                        # Flush current chunk
+                        chunks.append('\n\n'.join(current_chunk))
+                        current_chunk = [line]
+                        current_length = line_length
+                    elif line_length > max_length:
+                        # Single line too long, split by words
+                        if current_chunk:
+                            chunks.append('\n\n'.join(current_chunk))
+                            current_chunk = []
+                            current_length = 0
+                        words = line.split()
+                        word_chunk = []
+                        word_length = 0
+                        for word in words:
+                            word_len = len(word) + 1
+                            if word_length + word_len > max_length and word_chunk:
+                                chunks.append(' '.join(word_chunk))
+                                word_chunk = [word]
+                                word_length = word_len
+                            else:
+                                word_chunk.append(word)
+                                word_length += word_len
+                        if word_chunk:
+                            current_chunk.append(' '.join(word_chunk))
+                            current_length = len(current_chunk[-1])
+                    else:
+                        current_chunk.append(line)
+                        current_length += line_length
+            elif current_length + para_length > max_length and current_chunk:
+                # Flush current chunk
+                chunks.append('\n\n'.join(current_chunk))
+                current_chunk = [para]
+                current_length = para_length
             else:
-                current_chunk.append(word)
-                current_length += word_len
+                current_chunk.append(para)
+                current_length += para_length
 
         if current_chunk:
-            chunks.append(" ".join(current_chunk))
+            chunks.append('\n\n'.join(current_chunk))
 
         # Post chunks as a thread
         last_id = reply_to_id

src/govbot/platforms/mastodon.py

@@ -587,20 +587,27 @@ class MastodonAdapter(PlatformAdapter):
         }
 
     def _update_instance_rules(self, params: Dict[str, Any]) -> Dict[str, Any]:
-        """Update instance rules"""
-        # Note: This requires admin API access
-        # Implementation depends on Mastodon version and API availability
-        rules = params["rules"]
+        """Update instance rules - web admin only"""
+        rules = params.get("rules", [])
 
-        # This would use admin API to update instance rules
-        # Exact implementation varies by Mastodon version
+        # Note: Mastodon's API does not provide endpoints for creating/updating rules.
+        # Rules must be managed through the web admin interface.
+        # See: https://docs.joinmastodon.org/methods/instance/
+
+        rules_text = "\n".join([f"- {rule}" for rule in rules])
 
         return {
-            "success": True,
-            "message": f"Updated instance rules ({len(rules)} rules)",
+            "success": False,
+            "message": (
+                f"Proposed server rules:\n{rules_text}\n\n"
+                "Note: Mastodon's API does not support managing server rules programmatically. "
+                f"To update the server rules, please visit:\n"
+                f"{self.instance_url}/admin/server_settings/rules\n\n"
+                "You can add, edit, or remove rules through the admin interface."
+            ),
             "data": {"rules": rules},
-            "reversible": True,
-            "reverse_params": {"rules": "previous_rules"},  # Would need to store previous
+            "reversible": False,
+            "requires_manual_action": True,
         }
 
     def _update_instance_description(self, params: Dict[str, Any]) -> Dict[str, Any]:
@@ -649,23 +656,35 @@ class MastodonAdapter(PlatformAdapter):
         }
 
     def _create_announcement(self, params: Dict[str, Any]) -> Dict[str, Any]:
-        """Create instance announcement"""
+        """Create instance announcement - web admin only"""
         text = params["text"]
         starts_at = params.get("starts_at")
         ends_at = params.get("ends_at")
 
-        announcement = self.client.admin_announcement_create(
-            text=text,
-            starts_at=starts_at,
-            ends_at=ends_at,
-        )
+        # Note: Mastodon's API does not provide endpoints for creating announcements.
+        # Announcements must be created through the web admin interface.
+        # See: https://docs.joinmastodon.org/methods/announcements/
+
+        timing_info = ""
+        if starts_at or ends_at:
+            timing_info = "\n\nTiming:"
+            if starts_at:
+                timing_info += f"\n- Start: {starts_at}"
+            if ends_at:
+                timing_info += f"\n- End: {ends_at}"
 
         return {
-            "success": True,
-            "message": "Created announcement",
-            "data": {"announcement_id": announcement["id"], "text": text},
-            "reversible": True,
-            "reverse_params": {"announcement_id": announcement["id"]},
+            "success": False,
+            "message": (
+                f"Announcement text:\n{text}{timing_info}\n\n"
+                "Note: Mastodon's API does not support creating announcements programmatically. "
+                f"To post this announcement, please visit:\n"
+                f"{self.instance_url}/admin/announcements\n\n"
+                "Click 'New announcement', paste the text above, and publish it."
+            ),
+            "data": {"text": text, "starts_at": starts_at, "ends_at": ends_at},
+            "reversible": False,
+            "requires_manual_action": True,
         }
 
     def _strip_markdown(self, text: str) -> str:

src/govbot/scheduler.py

@@ -100,12 +100,13 @@ class GovernanceScheduler:
                 )
                 # TODO: Post result to Mastodon
 
-        # Check for pending reminders
-        reminders = self.agent.primitives.get_pending_reminders()
-        for reminder in reminders:
-            logger.info(f"Sending reminder: {reminder['data']['message']}")
-            # TODO: Post reminder to Mastodon
-            self.agent.primitives.mark_reminder_sent(reminder["id"])
+        # Check for pending reminders (if agent has primitives - old architecture)
+        if hasattr(self.agent, 'primitives'):
+            reminders = self.agent.primitives.get_pending_reminders()
+            for reminder in reminders:
+                logger.info(f"Sending reminder: {reminder['data']['message']}")
+                # TODO: Post reminder to Mastodon
+                self.agent.primitives.mark_reminder_sent(reminder["id"])
 
         # Check for veto votes (every cycle)
         self._check_veto_votes()