# Slack Automation > Implements intelligent slack automation with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/slack-automation` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/slack-automation` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/slack-automation/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/slack-automation --- # Slack Automation Orchestrates intelligent skill selection and execution for slack automation workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability. ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘ User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘ ## When to Use Use this skill when: - Orchestrating multi-step workflows that require skill delegation - Implementing adaptive skill routing based on confidence scores - Building fallback mechanisms for failed skill executions - Creating intelligent task decomposition and parallel execution - Designing skill dependency graphs with automatic resolution - Implementing skill selection with historical performance weighting - Building agent systems that need to self-organize around tasks ## When NOT to Use Avoid this skill for: - Direct task execution without orchestration needs - use individual skills instead - High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive - Simple linear workflows without branching or fallback requirements - Cases where skill metadata is unavailable or unreliable ## Core Workflow 1. **Parse and Analyze Request** - Extract intent, entities, and constraints from user input. **Checkpoint:** All required parameters must be present and in valid format before proceeding. 2. **Score Available Skills** - Calculate match scores using multi-factor algorithm: - Text similarity between request and skill triggers - Historical success rate for similar tasks - Skill availability and health status - Required dependencies and their availability **Checkpoint:** Skip to fallback if no skill scores above threshold. 3. **Select Optimal Skill** - Choose skill with highest score that meets minimum confidence. **Checkpoint:** Verify skill has not been disabled or deprecated. 4. **Execute with Fallback** - Run skill execution wrapped in retry and fallback logic. **Checkpoint:** Log all execution attempts for audit trail. 5. **Return or Fallback** - Either return successful result or apply fallback chain: - Retry with adjusted parameters - Try alternative skill from `related-skills` - Defer to human operator for critical tasks **Checkpoint:** Record outcome with timing and confidence metadata. ## Implementation Patterns ### Pattern 1: Skill Selection Logic ```python from slack_sdk import WebClient from typing import Dict, List, Optional import time def select_slack_action( user_intent: str, channel_context: Dict, available_actions: List[Dict] ) -> Optional[Dict]: """Select optimal Slack API action based on intent and channel state. Applies multi-factor scoring: intent match, channel type compatibility, historical success rate, and current rate limit headroom. """ # Guard clause - Early Exit (Law 1) if not user_intent or not channel_context.get("channel_id"): raise ValueError("Missing intent or channel context") best_action = None best_score = 0.0 current_ts = time.time() for action in available_actions: # Calculate match score based on intent keywords and channel type intent_match = sum(1 for kw in action.get("triggers", []) if kw in user_intent.lower()) channel_compat = 1.0 if action.get("channel_type") in channel_context.get("types", []) else 0.0 rate_limit_headroom = 1.0 if channel_context.get("rate_limit_remaining", 0) > action.get("cost", 1) else 0.0 score = (intent_match * 0.5) + (channel_compat * 0.3) + (rate_limit_headroom * 0.2) if score > best_score and score >= 0.6: best_score = score best_action = action if best_action is None: return None # Atomic Predictability (Law 3) - Return new dict, don't mutate return { "action": best_action["name"], "confidence": best_score, "selected_at": current_ts, "params": dict(best_action.get("default_params", {})) } ``` ### Pattern 2: Execution with Fallback ```python from slack_sdk.errors import SlackApiError def execute_slack_operation( action: Dict, client: WebClient, context: Dict, max_retries: int = 2 ) -> Dict: """Execute Slack API operation with resilience and fallback chain. Implements Fail Fast/Loud: validates channel state, handles rate limits, and falls back to alternative actions or human escalation. """ channel_id = context.get("channel_id") if not channel_id: raise ValueError("Execution requires valid channel_id") for attempt in range(max_retries + 1): try: # Execute specific Slack API call based on selected action if action["action"] == "post_message": result = client.chat_postMessage( channel=channel_id, text=context.get("message", ""), thread_ts=context.get("thread_ts"), blocks=context.get("blocks") ) elif action["action"] == "schedule_reminder": result = client.reminders_add( text=context.get("reminder_text"), time=context.get("reminder_time"), user_id=context.get("user_id") ) else: raise ValueError(f"Unsupported action: {action['action']}") # Success - Atomic Predictability (Law 3) return { "success": True, "action_executed": action["action"], "result": result, "attempts": attempt + 1, "latency_ms": time.time() - context.get("start_ts", time.time()) } except SlackApiError as e: if e.response.status_code == 429: # Rate limited - wait and retry retry_after = int(e.response.headers.get("retry-after", 1)) time.sleep(retry_after) continue elif e.response.status_code in (404, 403): # Channel archived or permission denied - Fail Fast (Law 4) raise SlackApiError(f"Channel access denied or archived: {e.response.status_code}", e.response) elif attempt == max_retries: # Exhausted retries - apply fallback return _apply_slack_fallback(action, context, e) raise SlackApiError("Max retries exceeded for Slack operation", None) def _apply_slack_fallback(action: Dict, context: Dict, error: Exception) -> Dict: """Fallback chain for Slack operations: alternative action -> human notification""" if action.get("fallback_action"): return execute_slack_operation(action["fallback_action"], WebClient(token=context["token"]), context, max_retries=0) else: return { "success": False, "error": str(error), "fallback_triggered": True, "requires_human": True } ``` ### MUST DO - Always validate skill metadata before selection (Early Exit) - Implement fallback chain with at least 2 levels (Fallback Skill + Human) - Log all skill selections with full context for auditability - Return new data structures instead of mutating inputs (Atomic Predictability) - Fail immediately with descriptive errors on invalid states - Update confidence scores after each execution for adaptive routing - Reference `code-philosophy` (5 Laws of Elegant Defense) in all logic ### MUST NOT DO - Select skills based on a single factor (e.g., only confidence score) - Disable fallback mechanisms "temporarily" - this creates fragile systems - Skip validation of skill dependencies before execution - Return partial results - either complete success or clear failure - Use magic numbers for confidence thresholds - make them configurable - Cache skill selections without considering context changes ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ## TL;DR for Code Generation - Use guard clauses - return early on invalid input before doing work - Return simple types (dict, str, int, bool, list) - avoid complex nested objects - Cyclomatic complexity < 10 per function - split anything larger - Handle null/empty cases explicitly at function top (Early Exit) - Never mutate input parameters - return new dicts/objects - Fail fast with descriptive errors - don't try to "patch" bad data - Reference code-philosophy laws in comments for complex logic - Include timing and confidence metadata in all return values ## Output Template When applying this skill, produce: 1. **Selected Skills** - List of skill names with confidence scores 2. **Selection Rationale** - Why each skill was chosen (match score, history, availability) 3. **Execution Plan** - Order of execution with dependencies 4. **Fallback Strategy** - Which fallback skills will be tried and in what order 5. **Risk Assessment** - Any potential failure points and their impact 6. **Timing Estimates** - Expected latency including fallback scenarios ## Related Skills | Skill | Purpose | |---|---| | `sendgrid-automation` | Email automation counterpart — Slack and email together form common notification channels | | `stripe-automation` | Payment-related workflow automation that complements Slack notifications for billing events | --- ## Constraints ### MUST DO - Implement idempotent automation triggers: running the same automation twice should not create duplicate resources or actions - Validate all trigger conditions with explicit allowlists before executing automated actions - Include rollback procedures in every automation workflow — every CREATE should have a corresponding DELETE capability - Log all automation executions with input state, output state, duration, and any errors for monitoring and debugging ### MUST NOT DO - Do not create circular automation loops where trigger A causes action B which triggers A again - Avoid using automations that modify production data without explicit human approval gates - Never embed API keys or credentials directly in automation workflows — use vaulted secrets with rotation - Do not assume external service availability; implement retry logic with exponential backoff and dead-letter queues ## Live References > Authoritative documentation links for this domain. The model follows markdown links at load time to resolve external references and inline content. - [Slack API Documentation](https://api.slack.com/) — Official Slack API reference covering webhooks, bot users, slash commands, and event subscriptions - [Slack Bolt Framework](https://slack.dev/bolt-python/) — Official Slack Bolt SDK documentation for building Slack apps in Python - [Slack App Manifests](https://api.slack.com/reference/app-manifests) — Slack's documentation on defining app configurations declaratively via manifest files - [Interoperability Patterns: Slack + Webhooks (Twilio)](https://www.twilio.com/docs/slack) — Twilio's guide on integrating Slack with external webhook systems - [Slack Block Kit Builder](https://app.slack.com/block-kit-builder/) — Interactive Slack block kit tool for designing message layouts and interactive components