Run summit 2023 workshops
Skill hiteshbandhu/skills-i-use/skills/ai-engineer-talks/run-summit-2023-workshops
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".From its SKILL.md
npx -y skills add hiteshbandhu/skills-i-use --skill run-summit-2023-workshopsAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
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SKILL.md
2.7 KB, 621 tokens by cl100k_base, 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
What ships with it: 3 files
5.4 KB alongside SKILL.md
- README.md934 B
- source-index.md1010 B
- workflows.md3.5 KB
Gives 0 of the 12 instructions most prompt engineering skills give in 621 tokens
Counted across 542 of the 575 authors here whose files we hold, read 2026-09-06
- Provide few-shot examples for complex tasksin 17 of 542, across 16 files
- Ask clarifying questions if information is ambiguousin 16 of 542, across 14 files
- Output a complete optimized prompt for the userin 15 of 542, across 9 files
- Validate structured outputs against schemasin 15 of 542, across 13 files
- Analyze the draft prompt for intent and gapsin 14 of 542, across 8 files
- Detect project tech stack from local filesin 14 of 542, across 8 files
- Recommend a model based on task scopein 13 of 542, across 7 files
- Present results in the specified output formatin 13 of 542, across 7 files
- Match intent and scope to ECC componentsin 13 of 542, across 7 files
- Ask one question at a timein 13 of 542, across 12 files
- Respond in the same language as the user inputin 12 of 542, across 6 files
- Ask up to three clarification questions if context is missingin 11 of 542, across 5 files
Said here and by no other author read
- Identify user goal to select workflow A through D
- Execute the checklist from the selected workflow
- Name the workflow and deliverable to the user
- Prioritize inference-time compute over model retraining
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.