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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Skills for AI agents
| Date | Stars |
|---|---|
| 2026-07-31 | 270 |
| 2026-08-06 | 270 |
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# pskoett-ai-skills A collection of skills for AI agents. Follows the [Agent Skills specification](https://agentskills.io/specification). This repository is my personal skill testing ground. ## Philosophy Every skill in this collection is built around a philosophy — a principle that addresses a specific failure mode in how agents work today. `plan-interview` is about collaborative planning: before codebase exploration starts, user and agent run a structured interview to align on constraints, scope, risk, and success criteria — and to surface whether a preparatory refactor should come before the main change. `intent-framed-agent` makes execution intent explicit so scope drift becomes visible. `context-surfing` monitors context quality and exits cleanly before degradation corrupts output. `verify-gate` runs compile, test, and lint checks so the agent doesn't need you to tell it the output was wrong if a test can. `self-healing` turns mid-task failures into verified, reusable artifacts instead of swept-under-the-rug retries. `simplify-and-harden` uses the peak context at end-of-task for a focused quality and security review. `self-improvement` turns repeated mistakes into durable rules that persist across sessions. The common thread: agents have peak context at specific moments — after planning, mid-execution, at completion, after learning — and these skills are designed to exploit those peaks. Each skill encodes a philosophy that agents struggle to internalize on their own, turning it into a structured workflow they can follow reliably. If you want to improve agent output over time, you need two loops, not one. The inner loop catches failures during a running session: the agent detects a problem, verifies its work against machine signals, and — with `self-healing` — recovers, files the verified fix as a reusable artifact, and continues, without you touching anything. The outer loop closes gaps across sessions: you capture where the agent failed, figure out what knowledge was missing, and encode it somewhere the agent can reach next time. `learning-aggregator` reads accumulated learnings across sessions and surfaces patterns. `harness-updater` encodes those patterns as permanent rules in project instruction files. `eval-creator` turns promoted rules into regression tests. `pre-flight-check` surfaces all of this at the start of the next session — closing the loop. The knowledge gaps get smaller with every cycle as it compounds. `skill-pipeline` ties these pieces together by classifying the task and routing it through the right combination at the right depth. One skill sits outside the two loops by design: `control-session-orchestrator` is an orchestration-layer skill, not an inner/outer-loop step. It runs *above* the pipeline — coordinating multi-agent, multi-session work from a Codex, Copilot, or agent-app control session — and is invoked directly rather than routed by `skill-pipeline`. ## Install Install as a Claude Code plugin from this repo's marketplace. Run each command from inside Claude Code: 1. Register this repo as a plugin marketplace: ``` /plugin marketplace add pskoett/pskoett-skills ``` 2. Install the plugin from that marketplace: ``` /plugin install pskoett-ai-skills@pskoett-skills ``` 3. Reload so skills, agents, and hooks register: ``` /reload-plugins ``` This installs the full bundle: skills, audit agents, and hooks. ### Codex The same bundle now ships as a repo-local Codex plugin from `plugin/`. 1. Open this repository in Codex. 2. Restart Codex after pulling the latest repo state so it reloads repo marketplaces. 3. Open the plugin directory, choose the `pskoett skills` marketplace, and install `pskoett-ai-skills`. Codex reads the marketplace from `.agents/plugins/marketplace.json` and the plugin manifest from `plugin/.codex-plugin/plugin.json`. ### GitHub Copilot CLI The same bundle ships as a Copilot CLI plugin nested under `plugin/.copilot-plugin/`, reusing the shar
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matched fp:a9ad2dc1f7c7e69d, llm:Repository name and description: 'pskoett-ai-skills' with description 'Skills for AI agents' (no README provided).