[Blocked: llm, qm] Botspot bound llm queue

CLAUDE.md

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+
 # Botspot Development Guide
 
 ## Build & Test Commands

dev/workalong_bound_llm_queue/rework_protocol.md

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+This is a protocol for refactoring / reworking code components with claude-code or other
+ai tools.
+The main goal of this protocol is to ensure the codebase doesn't bloat and grow riddled
+with duplicated code during AI refactoring / reworks.
+
+# Pre-rework
+
+- Commit. Make sure everything is committed
+    - Optional: Create a new branch for the rework
+- Optional: Check tests, if present.
+
+# Simple protocol:
+
+If the task is clear, go directly to coding phase
+
+# Complex protocol:
+
+If there's a complex task - first, read spec.md or ask user for a list of requirements
+
+- After that, evaluate the current solution on the match with specs
+    - Which additional featuers were implemented that are not present in the spec
+        - What is the likely reason were they added
+        -
+    - Which components / features explicitly listed in the spec are missing
+        - How difficult it is to add this
+    - write to workalong.md
+    - proceed to coding
+
+## Coding:
+
+- Before coding, lay out a plan to the user in clear terms.
+    - Which components / features will be added
+    - Which modified
+    - Which moved / removed
+    - Make an explicit enumeration for user to specify which steps are approved and
+      which are declined
+    - Each item should be formulated in as simple terms as possible, 1 line maximum per
+      item, not longer than a few words
+- Always remove duplicate code or alternative / previous implementations of the same
+  feature
+    - After making a change, call git diff and make sure file is in a desired target
+      state and the changes you planned are correctly reflected
+- proceed with implementing each item one by one and track progress with checkboxes in
+  workalong.md
+    - [x] item 1 - keep to original item descriptions, DO NOT ADD SUB-ITEMS. List which
+      files were affected for this feature.
+
+AI Issue resolution
+
+1) Failed deletions
+   Sometimes AI applier fails to delete code according to instructions
+   This results in really confusing situations with duplicate functions / methods
+   present in multiple places across the codebase
+   To mitigate that
+
+- Check file diff after each modifications and make sure it reflects the changes you've
+  made
+

dev/workalong_bound_llm_queue/spec.md

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+# Bound LLM Queue
+
+- **Main Feature**: A queue that auto-parses messages with LLM Utils and requests missing fields, triggered by a single bind action.
+- **Demo Bot**: Send `/bind` in a chat; every message after is auto-queued, parsed by LLM (e.g., into a contact form), and prompts for missing fields if needed.
+- **Useful Bot**: **People Bot (Enhanced)** - Bind it to a chat; it auto-parses all messages (or forwarded ones) into contacts, stores them, and asks for missing details—all hands-off after `/bind`.
+
+## Developer Notes
+
+For llm queue - first of all, this is a combination of other components - bind, llm utils and queue. But on top of that - also new features enabled by that.
+
+1) ingest -> load all past messages (via telethon client), process them with llm and add to queue. Then some of those messages would have missing mandatory fields -> and we will have procedures to fix that.
+
+2) procedure 1 - /fix_records (or some other name - invent better) command to initiate a process to go through records one by one and fill missing data.
+
+3) procedure 2 - scheduled auto-reminders to ask user about one unfilled item.
+
+4) One more thing - part of these mechanics already implemented in oibot - /Users/petrlavrov/work/projects/outstanding-items-bot