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talk-groetzinger-skills-everywhere
Skills Everywhere: Pipelining Knowledge Your Engineers Can Read and Your Agents Can Use
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Skills Everywhere: Pipelining Knowledge Your Engineers Can Read and Your Agents Can Use
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Use when the user asks about Christopher Batey's talk 'Building Product Teams in the Age of AI: What We Had to Relearn Every Quarter' (Latent Space, 2026) — including questions about running AI-assisted product engineering teams, his three pillars (path to production at AI speed, training/evaluating AI-enabled engineers, designing workflow for parallel change), ADR-first workflows with agents, why review becomes the bottleneck, the producer 'black box' (harness/host/model), vanity metrics vs adoption, two-to-four-person sub-streams, one-complex-task-at-a-time, 'you build it, you run it, you drive adoption', or applying his approach to current work.
Answers questions about, retrieves verbatim quotes from, explains concepts from, and summarizes key arguments in Birgitta Böckeler's talk "State of Play: AI Coding Assistants" (AI Native Dev conference, 2026). Use when the user asks about the last 12 months in AI coding assistants, the Opus 4.5 moment, LLM statelessness, context window and attention trade-offs, choosing the right model for a task, the ecosystem around models, or her Thoughtworks/Martin Fowler-site writing on AI-assisted software delivery.
Use when the user asks about Patrick Debois's talk "Coding Agents Don't Scale Themselves. Neither Do Your Teams. The Rise of Agent Enablement." — including questions about agent enablement teams, the three pillars (Enablement, Platform, Governance), the Context Development Lifecycle applied to org charts, AI product engineers, agent KPIs like turns-per-task, harnesses and shared context libraries, fixing the system vs. fixing the code, the barrel mental model, continuous learning as the next CI/CD, or how VPs / team leads / platform teams should scale AI coding agents across an org.
Answers questions about Brian Douglas's talk on training AI on your own code. Use when a user asks about Brian Douglas's pipeline for capturing agent sessions, extracting skills from traces, fine-tuning small local models, tapes/steros tooling, SFT vs DPO decisions, or wants to apply his agent telemetry and training data approach to their own work with Claude Code, QLoRA, or parallel agents.
Use when the user asks about Tammuz Dubnov's talk "When Our PM Started Writing Code: What Merge Rate Taught Us About AI Adoption" — including questions about what "AI-native" means, harness engineering, merge rate as an AI-adoption metric, non-technical contributors (PMs, designers) opening pull requests, PR fatigue, the ~74% merge rate / ~84% zero-dev-touch numbers from Autonomy AI, why Uber/Microsoft's AI spend isn't translating to velocity, Shopify as a positive example, Calamarous Coding, feature-flag-driven developer autonomy, or applying his framework to the user's own engineering org.
Use when the user asks about Dave Farley's talk "Vibe Coding — Is this really the best we can do?" — including questions about vibe coding, agentic programming, AI-generated tests, BDD-style executable specifications as prompts, problem-specific DSLs, why natural language is insufficient as a programming language, the three properties of programming languages (formal grammar / unambiguous intent / deterministic execution), the three problems AI programming creates (precise specification, verification, incrementalism), fifth-generation programming, AI as compiler, or applying Farley's continuous-delivery-style approach to working with AI coding agents.
| name | talk-groetzinger-skills-everywhere |
| description | Skills Everywhere: Pipelining Knowledge Your Engineers Can Read and Your Agents Can Use |
Groetzinger argues that frontier models are already capable enough for business value — the real bottleneck is context engineering, and skills are the durable, harness-portable, model-portable investment. He walks through two Cisco patterns: (1) a support-side pipeline that converts curated knowledge-base articles into agent skills with change-severity-gated human review, and (2) a developer-side pattern of shipping an evaluation framework to eight globally distributed teams as an installable skill instead of an onboarding meeting. The unifying discipline is evals as unit tests for agents and a cultural reflex of asking "is this a skill?" before writing anything down anywhere else.
outline.md to locate the relevant section, then read that section of transcript.md.transcript.md. Never put quotation marks around paraphrased content.transcript.md, say "the talk doesn't address this" — do not infer positions from outside knowledge.transcript.md and flag the likely intended term in square brackets only if the user asks for clarification.For any question about what the speaker said, did, or argued:
outline.md first to find the relevant section(s).transcript.md.transcript.md. Do not paraphrase the speaker's words while presenting them as a quote.When the user asks "how would Groetzinger tackle X?" or wants the talk's framework applied to their own situation:
outline.md → "Named frameworks / concepts" to find the relevant framework (e.g. KB-to-skill pipeline, evals-as-unit-tests, semantic versioning of context, "is this a skill?" reflex).transcript.md for the speaker's exact wording.When the user asks to "audit", "score", "review", "grade", "check", or "gap-analyse" their current setup against the talk's framework — or describes their situation and asks where they're falling short:
outline.md → "Named frameworks / concepts" to locate the dimensions: single source of truth, evals as unit tests, JSONL dataset schema, environment-aware eval scripts, change-severity-gated human review, paired skill+README with deterministic sync to other systems, semantic versioning of context, and the "is this a skill?" cultural reflex.transcript.md and quote it verbatim when stating what "good" looks like in that dimension.When the user asks the skill to "draft", "generate", "give me a starting", "show me an example of", or "produce" an artifact he described — a skill, an eval dataset in JSONL, a paired README, a markdown-to-Confluence sync script, the KB-article-change pipeline:
outline.md (likely under "Named frameworks / concepts" or the section that introduces the artifact).transcript.md carefully — capture every constraint he mentions.[not from talk — added as a starting placeholder]).When the user's current work touches on themes Groetzinger addressed (even if the user hasn't asked about the talk):
transcript.md — one quote is usually enough.Likely triggers: discussions of documentation drift between wikis and agent context; debates about model choice / cost vs. capability; teams setting up agent evals; scaling a process across many distributed engineering teams; choosing where to invest given fast-changing tooling.
When the user wants to understand a concept Groetzinger covered (skills, evals as agent unit tests, the KB-to-skill pipeline with change-severity gating, JSONL vs JSON for datasets, semantic versioning of context, the README-to-Confluence sync):
outline.md → "Terminology glossary".transcript.md.quotes.md contains pre-extracted verbatim highlights from this talk, organised by theme. When formulating answers, check quotes.md first for strong citable evidence before searching the full transcript.md.