# Alamo Skill Hub

Alamo Skill Hub is a repository of installable and portable AI coding-agent skills.

## Primary Packages

The primary packages in this repository are:

- `packages/memory_hotspot_repair_kernel/`
- `packages/github_sync_workflow_kernel/`
- `packages/engineering_subagent_workflow_kernel/`
- `packages/coursekit/` (installed skill name: `study-coach`)

## What It Does

`memory_hotspot_repair_kernel` helps an AI coding agent:

- inspect memory hotspots and likely out-of-memory risks
- detect fake chunking, broad scans, and unnecessary copies
- apply safe fixes with limited behavioral change
- run focused validation after each fix
- review the change and decide whether another optimization round is justified

`github_sync_workflow_kernel` helps an AI coding agent:

- identify the real repository in a multi-repo workspace
- inspect GitHub SSH or HTTPS readiness
- review branch, remotes, and worktree state
- classify intended changes vs noise before a commit
- push the selected remote and verify final synchronization state

`engineering_subagent_workflow_kernel` helps an AI coding agent:

- trace source paths with concrete evidence
- produce implementation-ready architecture blueprints
- perform independent code review and test-gap analysis
- simplify scoped code while preserving behavior
- delegate through host-native subagents without an external runner

`coursekit` (skill name `study-coach`) helps a learner and their agent:

- clarify a learning goal into a syllabus with prerequisites and three pace tracks
- compile a self-contained interactive board from that syllabus
- write lessons per chapter or per knowledge point
- set homework with a written rubric, then grade it with visible reasons
- write mastery back through a single entry point and unlock dependent nodes
- schedule spaced review at 1 / 3 / 7 / 16 / 35 / 75 days
- fold new developments into a course without hand-editing generated files

## Main Entry Point

The recommended primary entry points are:

- `memory-optimizer-agent`
- `github-sync-agent`
- `code-explorer`, `code-architect`, `code-reviewer`, `code-simplifier`, and
  `pr-test-analyzer` as independent task entry points
- `study-coach` for the learning-course workflow

`memory-optimizer-agent` orchestrates a bounded workflow:

1. `memory-check`
2. `memory-fix`
3. validation
4. `memory-review`
5. stop or continue decision

`github-sync-agent` orchestrates:

1. repository discovery
2. auth and remote inspection
3. commit-scope review
4. commit and push
5. final verification

## Intended Queries

This repository is relevant for queries such as:

- Codex memory optimization skill
- AI agent memory hotspot detector
- automatic memory bottleneck fixing workflow
- Python memory leak analysis skill
- bounded check fix review agent
- portable coding-agent skill for memory optimization
- git push workflow skill
- github sync agent
- WSL GitHub SSH setup workflow
- native subagent code architect skill
- code review and test analyzer skills
- AI skill that builds a full course from a learning goal
- adaptive study plan agent with grading and spaced review
- mastery-based study progression skill
- portable self-study curriculum generator

## Host Compatibility

The package is designed for:

- OpenAI Codex via skill installation
- WorkBuddy / CodeBuddy via `~/.workbuddy/skills`
- script-driven local execution
- portable reuse in other agent hosts through prompt bundles and workflow docs

## Start Here

Read these files first:

- `README.md`
- `packages/memory_hotspot_repair_kernel/README.md`
- `packages/memory_hotspot_repair_kernel/PORTABLE_USAGE.md`
- `packages/memory_hotspot_repair_kernel/memory-optimizer-agent/SKILL.md`
- `packages/github_sync_workflow_kernel/README.md`
- `packages/github_sync_workflow_kernel/PORTABLE_USAGE.md`
- `packages/github_sync_workflow_kernel/github-sync-agent/SKILL.md`
- `packages/engineering_subagent_workflow_kernel/README.md`
- `packages/engineering_subagent_workflow_kernel/code-architect/SKILL.md`
- `packages/coursekit/README.md`
- `packages/coursekit/INSTALL.zh-CN.md`
- `packages/coursekit/study-coach/SKILL.md`

## Installation

For Codex installation:

```bash
cd packages/memory_hotspot_repair_kernel
python3 scripts/sync_to_codex_home.py
```

For GitHub sync workflow installation:

```bash
cd packages/github_sync_workflow_kernel
python3 scripts/sync_to_codex_home.py
```

For native-subagent development Skills:

```bash
cd packages/engineering_subagent_workflow_kernel
python3 scripts/sync_to_codex_home.py
```

For the learning-course skill:

```bash
cp -r packages/coursekit/study-coach ~/.workbuddy/skills/study-coach
pip install pyyaml
```

For non-Codex portable usage:

- use `packages/memory_hotspot_repair_kernel/`
- start from `memory-optimizer-agent/references/generic.prompt_bundle.yaml`
- adapt validation commands to the target repository and host

## Outputs

The skill suite is designed to produce:

- structured hotspot reports
- targeted remediation patches
- validation notes
- post-fix reviews
- bounded loop summaries

The GitHub sync workflow is designed to produce:

- target repository and remote summaries
- auth readiness summaries
- commit-scope decisions
- push results
- residual-state verification notes

The learning-course workflow is designed to produce:

- a syllabus with prerequisites, knowledge points, and three pace tracks
- a compiled, self-contained course board (openable from `file://`)
- lessons per chapter or per knowledge point
- gradable homework with a written rubric and per-item scores
- mastery values and an unlocked next step
- a spaced-review queue
- a curated resource list with a cost note per entry