name: update-review-queue description: >- Syncs and maintains the PR review queue file (tmp/review-queue.md) by checking GitHub statuses, moving PRs between sections, detecting unaddressed comments, and discovering new review requests. Use when the user asks to update reviews, check review queue, sync PRs, see what needs review, or mentions "review queue", "update reviews", "what PRs need review", "/update-review-queue". Do not use for performing actual code reviews (use pr-review skill), creating PRs (use create-pr skill), or Jira ticket management.
Peter Rezikov CrazyPit
CrazyPit
/ solver.scala
Created
November 4, 2015 07:43
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| /** | |
| * Created by petrrezikov on 27.07.15. | |
| */ | |
| package ru.innopolis.crazypit.SokobanSolver | |
| import scala.collection.mutable.HashMap | |
| class SokobanSolver(val map: Array[Array[Field]]) { |
CrazyPit
/ cycle_1_20260313_111404.log
Created
March 13, 2026 09:34
Observer Agent — cycle log example (processes 3 PRs: graduates merged, checks CI, responds to reviewer)
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| ============================================================ | |
| Agent started — model: Claude 4.6 Opus | |
| ============================================================ | |
| Illartadingetcreated`.the MN-3189 ticket to find all comments with `Status: PR created`. | |
| → [mcp] | |
| Ifound ts with `Status: PR createdR created`. Let me check the state of all 3 PRs in parallel. | |
| → [shell] $ gh pr view 49775 --json state,mergedAt --repo sdtechdev/spree-jiffyshirts |
CrazyPit
/ cycle_1_20260313_115209.log
Created
March 13, 2026 09:34
Optimizer Agent — cycle log example (selects address_spec.rb, 12-pass optimization, creates PR)
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| ============================================================ | |
| Agent started — model: Claude 4.6 Opus | |
| ============================================================ | |
| Ill startng the optimizationtimization skill and gathering state from Jira in parallel. | |
| → [read] /Users/cpwork/j/spree-jiffyshirts-spec-optimizer/.cursor/skills/jiffy-optimize-spec/SKILL.md | |
| → [mcp] |
CrazyPit
/ localy_smart_globally_wrong.md
Created
April 5, 2026 08:18
One of the core problems in LLM-driven software development is not that models are simply “bad at coding.” It is that they are often extremely strong at local optimization inside the current task frame, while being much weaker at preserving system intent, architectural boundaries, and domain-level coherence.
This creates a very specific failure mode.
An LLM can often:
- continue a design direction very convincingly,
- find subtle edge cases,
- reverse-engineer unfamiliar code quickly,
CrazyPit
/ localy_smart_globably_wrong_proofread.md
Created
April 5, 2026 08:47
One of the core problems in LLM-driven software development is not that models are simply "bad at coding." It is that they are often extremely strong at local optimization inside the current task frame, while being much weaker at preserving system intent, architectural boundaries, and domain-level coherence.
This creates a very specific failure mode.
An LLM can often:
- continue a design direction very convincingly,
- find subtle edge cases,