aberson

Corpus Hub contributor since 2026-10-21

aberson's work was selected for the XERJ Corpus Hub, the open reference memory AI coding agents search before they write code. When an agent meets the same problem, it gets this implementation back as a worked example, with the author and the repository attached.

1corpus

1selected commit

7files

530lines

★ 2GitHub stars on repo

Totals across every corpus @aberson appears in; a commit selected for two corpora counts once. Files and lines count the selected commits; each corpus indexes the whole repository at its pinned commit.

What an agent can do with the skill-mesh corpus

aberson/skill-mesh: Provider-neutral skill pipeline (Claude, GitHub Copilot/GPT, OpenAI Codex) for autonomous plan -> build -> review -> ship: 57 skills (54…

Look up how skill-mesh implements agentic-workflows, claude, claude-code, with answers cited to file and line.
Reuse a worked example: the commit “Restore reviewed Codex deep route with bounded validation exception” (7 files · Python).
Answer from skill-mesh's real Python source instead of memory (AI, prompts and agents).

Example questions

Where does skill-mesh handle “Restore reviewed Codex deep route with bounded validation exception”?
How does skill-mesh implement agentic-workflows?
What does tests do in skill-mesh?

Paste it into your AI agent. It clones the repo at the pinned commit and indexes it on your machine.

Search the GitHub repository aberson/skill-mesh with XERJ (https://hub.xerj.org/corpus/aberson--skill-mesh).
Run these in a shell, in order, and check each result before the next:
1. xerj --help  (success: it prints usage). If xerj is missing: curl -fsSL https://xerj.org/get | sh
2. git clone https://github.com/aberson/skill-mesh skill-mesh && git -C skill-mesh checkout 2e7f3250965d9b7da04fa72edd72d587463f4a4d
3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data
4. xerj autoindex ./skill-mesh --url http://127.0.0.1:9200 --prefix skill-mesh --progress plain
5. xerj autoindex map --url http://127.0.0.1:9200
Then answer my question from the indexed files and cite the ax_path of every hit. Query syntax: https://xerj.org/llms.txt
Licence: MIT. Keep the copyright notice and credit the authors.

corpus aberson--skill-mesh · AI, prompts and agents
Related corpora: griptape, agno, halilogia/GravityGuard, MMALI3287/vibewatt
AI search and retrieval with XERJ · Case study: reference coding for agents

Selected work

GitHub ★ 2

aberson/skill-mesh

2e7f325096 Restore reviewed Codex deep route with bounded validation exception

selected commit: 7 files · 530 lines · Python · tests, tools

The corpus is the whole repository at the pinned commit, not only the files of the selected commit.

selected · in licence reviewcorpus aberson--skill-meshAIMITagentic-workflowsclaudeclaude-codedeveloper-toolsllmpython agreed 2026-10-21

Not installable yet: this corpus is not in the hub registry until its licence review passes, so xerj corpus add and xerj ask cannot fetch it. It becomes installable when this card says live in the hub.

[![XERJ Corpus Hub contributor](https://hub.xerj.org/contributors/aberson/badge.svg)](https://hub.xerj.org/contributors/aberson)

Included with the author's permission (email, 2026-10-21), under each repository's own licence, with attribution. Want it removed? Reply to the email or open an issue on xerj-org/xerj.