Run summit 2023 workshops
Skill hiteshbandhu/skills-i-use/skills/ai-engineer-talks/run-summit-2023-workshops
Drop-in skills and plugins for your AI development workflows
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Runs AI Engineer Summit 2023 workshop playbooks — foundations (101), inference (201), product patterns beyond inference (Rest of the Owl), and Anthropic prompt engineering principles. Use when onboarding to AI engineering, designing inference stacks, or improving task prompts; or when the user says "summit 2023 workshop", "AI engineering 101", "inference workshop", "prompt principles Karina".
SKILL.md
2.7 KB, as published. Nobody here has run it
Run Summit 2023 workshops
Action playbook from four AI Engineer Summit 2023 workshops. Do not summarize talks — pick a workflow and execute it.
Supporting files (read when needed):
- workflows.md — workflows A–D (steps, deliverables, stop conditions)
- source-index.md — src-NNN → talk learnings in ingest-into-skills
Optional deliverables: {SKILL_OUTPUT_DIR}/run-summit-2023-workshops/ — see skills-i-use OUTPUT.md.
Step 0 — Pick workflow
What is the user trying to do?
├─ Baseline AI engineering literacy (models, APIs, apps) → A [src-003]
├─ Inference serving, latency, cost, deployment → B [src-004]
├─ Product architecture beyond the model (LUI, eval, ops) → C [src-001]
└─ Task prompt design (Claude / API best practices) → D [src-002]
Stop summarizing once a workflow is identified — run its checklist in workflows.md.
Install
cp -r skills/run-summit-2023-workshops ~/.claude/skills/
cp -r skills/run-summit-2023-workshops ~/.cursor/skills/
cp -r skills/run-summit-2023-workshops ~/.codex/skills/
Source corpus: ingest-into-skills playlists/ai-engineer-summit-2023-workshops/.
Cross-cutting rules
| Rule | Source |
|---|---|
| AI engineering = systems around models, not prompting alone | [src-003 @ 7:17] |
| Inference is necessary but not sufficient for product value | [src-001 @ 0:25] |
| Prompts steer attention; use task keywords, context, examples | [src-002 @ 0:01:33] |
| Structure: context first, clear task, coherent logical flow | [src-002 @ 6:43] |
| Prefer inference-time compute over retraining when possible | [src-002 @ 1:53] |
Output to user
- Name the workflow (A–D) and deliverable
- Save artifacts under
./skill-outputs/run-summit-2023-workshops/when requested - Do not auto-commit
Invocation examples
@run-summit-2023-workshops walk me through AI engineering 101 topics
design our inference layer using the 201 workshop
improve task prompts using Anthropic principles
Gives 0 of the 12 instructions most prompt engineering skills give
Counted across 563 of the 626 authors here whose files we hold, read 2026-08-06
- ask at most three clarifying questionsin 22 of 563, across 15 files
- respond in the user input languagein 14 of 563, across 9 files
- preserve the original intentin 13 of 563, across 11 files
- Establish baseline metrics and collect representative examplesin 12 of 563, across 2 files
- Identify failure modes and prioritize high-impact fixesin 12 of 563, across 2 files
- Apply prompt and workflow improvements with measurable goalsin 12 of 563, across 2 files
- Roll back quickly if quality or safety metrics regressin 12 of 563, across 2 files
- validate changes with tests and roll out in controlled stagesin 12 of 563, across 2 files
- generate quantitative baseline performance reportsin 12 of 563, across 2 files
- create representative test scenariosin 12 of 563, across 2 files
- treat prompts as codein 12 of 563, across 5 files
- test prompts on diverse inputsin 12 of 563, across 8 files
Said here and by no other author read
- pick one workflow and execute it
- read workflows file when needed
- read source index file when needed
- save artifacts under skill outputs directory when requested
- name the workflow and deliverable to the user
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once.