Agent Skills

I want to evaluate and, if it fits, integrate a technique from recent AI research into my project.

## Technique: Agent Skills
AI agent skills: Why they succeed — and what causes them to crash

Drop-in applicable empirical study on AI agent skill library design (AlphaSignal deep-dive · 2026-08-23). 8,135 trial records across Terminal-Bench 2.0 and SkillsBench. Core finding: skills work as procedural anchors (stabilizing execution), not knowledge repositories. Procedural anchoring accounts for 65.7% of successes vs only 4.5% from explicit knowledge injection. Distilled runbook skills beat raw workflow memory by +6.06 pp. Annotation impact: 74.6% success with success/failure hints vs 40.0% without — a 34.6 pp gap. Catalog scaling: precision drops from 29.6% (5 skills) to 3.3% (100 skills), but task success remains stable (~37-39%) because related distractors provide partial procedural support. Fix: two-level retrieval (domain bucket → strict trigger rule). Directly applicable to any production agent system, RAG pipeline, multi-step LLM workflow, or coding agent.

What the reference demo shows: Gradio demo with three sub-tabs — (1) Skill Format Demo: raw workflow memory vs distilled runbook side-by-side with success rate bar chart (+6.06pp); (2) Annotation Impact: 74.6% vs 40% visualization + correct distillation pipeline diagram; (3) Catalog Scale & 2-Level Retrieval: precision/success dual-axis chart + interactive two-level retrieval demo (domain bucket → strict trigger).

## Reference implementation (Vector Ridge Labs, POC-023)
- Live demo: https://osoman2-gradio-pocs.hf.space/?poc=POC-023
- Source code: https://huggingface.co/spaces/osoman2/Gradio_pocs/tree/main/agent_skills
Original source: AlphaSignal deep-dive · 2026-08-23

## My project
<Describe your stack, the problem you want to solve and where this technique would sit. Example: "Python FastAPI RAG service over 50k PDFs, Postgres + pgvector, OpenAI embeddings, p95 latency budget 800 ms.">

## What I need from you
1. Read the reference source code and explain in at most 5 bullets how the technique works and what it would change compared to my current approach.
2. Decide whether it fits my case. If it does not, say why and stop there.
3. Propose the smallest viable integration: files or modules to touch, new dependencies, configuration.
4. Implement it behind a flag or as a separate function so it can be compared side by side with what I have today.
5. Add an evaluation script that measures task success rate, number of steps and token cost per task, before and after, on my own data.
6. List the risks (latency, cost, failure modes) and what to monitor once it is in production.

Constraints: keep my existing stack and conventions, avoid heavy dependencies unless the gain is clear, and reference the file in the demo source whenever you reuse its logic.