Podcast interview « À la French » (French-language tech channel, recorded at DevSummit) with Mathieu Grymonprez, Global CDO of the Adeo group (Leroy Merlin, Obramat, Weldom). How a century-old family retail group embraces the agentic AI wave: culture vs structure, accountability, token cost and FinOps, enterprise intelligence lock-in, company memory and agent orchestration. Domain: digital transformation, agentic AI, retail, IT strategy.
#Agentic AI#digital transformation#CDO
Mathieu Grymonprez (Global CDO, groupe Adeo) — invité ; Jean-Baptiste Kempf · Steeve Morin · Mehdi Medjaoui (hôtes du podcast « À la French »)
Blog post from **Anthropic / claude.com** by **Thariq Shihipar** (Member of Technical Staff, Claude Code team), published on **June 3, 2026**, which distills Anthropic's **internal experience** on designing and using **Skills**. **Framing thesis**: a Skill is not a simple markdown file but a **folder** (instructions + scripts + resources + config + hooks) that the agent **discovers and manipulates**; *« You should think of the entire file system as a form of context engineering and progressive disclosure. »* The article makes two structuring contributions. **(A) A taxonomy of 9 skill categories** observed at Anthropic: (1) **Library/API Reference** (docs for internal libs/CLIs with *gotchas* — e.g. `billing-lib`, `internal-platform-cli`, `sandbox-proxy`); (2) **Product Verification** (testing/verification via Playwright or tmux — `signup-flow-driver`, `checkout-verifier`, `tmux-cli-driver`); (3) **Data Fetching & Analysis** (access to data/monitoring stacks — `funnel-query`, `cohort-compare`, `grafana`, `datadog`); (4) **Business Process Automation** (repetitive workflows — `standup-post`, `weekly-recap`, `create-<ticket>-ticket`); (5) **Code Scaffolding** (framework boilerplate — `new-migration`, `create-app`); (6) **Code Quality & Review** (`adversarial-review`, `code-style`, `testing-practices`); (7) **CI/CD & Deployment** (`babysit-pr`, `deploy-<service>`, `cherry-pick-prod`); (8) **Runbooks** (multi-tool diagnostics — `<service>-debugging`, `oncall-runner`, `log-correlator`); (9) **Infrastructure Operations** (maintenance with guardrails — `<resource>-orphans`, `cost-investigation`). **(B) A set of best practices**: don't restate the obvious (*« Claude already knows how to code and can read your codebase »* → target what **contradicts default behavior**); polish the **Gotchas section** (*« the highest-signal content in any skill »*); **progressive disclosure** via the file tree (point to reference files depending on the situation rather than loading everything upfront); **descriptions written for the model** (*« the description field is not a summary, it's a description of when to trigger this skill »*); **setup flows** (config in `config.json`, otherwise prompt via `AskUserQuestion`); **persistent memory** (append-only logs / JSON via the `${CLAUDE_PLUGIN_DATA}` variable); **helper scripts** (*« lets Claude spend its turns on composition… rather than reconstructing boilerplate »*); **hooks conditionnels** (enabled only for the duration of the skill — e.g. a security hook blocking destructive commands). **Distribution at Anthropic**: skills are stored in `./.claude/skills`, informally shared via Slack in a sandbox folder, then promoted via **PR** to the internal **marketplace** once they gain traction; **usage measurement** via a **hook PreToolUse** that logs invocations (revealing popular skills versus underused ones). Direct follow-up to the fiche [[shihipar-claude-code-html-unreasonable-effectiveness-markdown-2026-05-10]] (same author) and a concrete complement to the Skills fiches by Anthropic/Willison/Vincent and to *harness engineering*.
#skills#Claude Code#Anthropic
**Thariq Shihipar** (Member of Technical Staff chez Anthropic, équipe **Claude Code** ; @trq212 / @trq sur X, thariqs.github.io) · pour le blog **claude.com**. Même auteur que la fiche *Using Claude Code: The Unreasonable Effectiveness of HTML* (2026-05-10). Publié le **3 juin 2026**.
Google whitepaper (the "Day 1" installment of a series, by Addy Osmani, Shubham Saboo and Sokratis Kartakis) that maps the transformation of the software development lifecycle (SDLC) in the era of coding agents. Thesis: the fundamental shift is not a new language but the move from writing code to **expressing intent**. The document lays out a spectrum from *vibe coding* (prompting and accepting) to *agentic engineering* (AI implements under constraints, tests, and feedback loops designed by humans), with **context engineering** as the central skill, the **software factory** model (the developer's deliverable = the system that produces the code), **harness engineering** (Agent = Model + Harness), and a CapEx/OpEx economic analysis of total cost of ownership.
Joint **DORA × delta** report (Google Cloud Professional Services), 60 pages, version **v. 2026.1** (citations retrieved February 2026, PDF created April 21, 2026), licensed **CC BY-NC-SA 4.0** — the first official **DORA ROI** framework dedicated to AI in the SDLC, with an **interactive calculator** at dora.dev/ai/roi/calculator. Pivot thesis: ***"AI is an amplifier"*** — AI **amplifies** the strengths of high-performing organizations and the dysfunctions of struggling ones simultaneously; it does not create performance, it **multiplies it where it already exists**. New central concept: the ***J-Curve of AI value realization*** — every AI adoption goes through a **temporary productivity dip** (learning curve + verification tax + pipeline adaptation) before **exponential growth**, a metaphor for the *"tuition cost of transformation"* to be **budgeted explicitly**. Reference calculation: a 500 FTE organization / $176k fully loaded salary / 12.5% time saved per developer (≈ 1h/8h day) → **value $11.6M / investment $8.4M / ROI 39% / payback period 8 months (0.7 year)**. Modeled costs: licenses ($250/user/year), additional API costs ($80/user/year), training ($9,600/user/year), infra ($100k/year), J-Curve cost ($3.3M for a 15% drop over 3 months). Modeled value: **headcount reinvestment capacity** ($11M — freed capacity to reinvest, **NOT headcount reduction**), revenue from extra feature deployments ($990k, based on a 33% idea success rate, Larsen 2023), **negative downtime impact** (−$344k, "instability tax"). **Explicit reinvestment strategy**: ***"we strongly recommend organizations do not adopt a headcount-reduction strategy"*** — reinvest in innovation, retain talent, capitalize on institutional knowledge. Five pillars of value: Productivity / User Experience / Cost Efficiency / Developer Experience / Business Growth (from most direct to most indirect, *cumulated business value*). Five systemic keys to adoption: **Trust + Platform + Data + Users + Guardrails**. Two-phase roadmap: (1) **Build context layer (CapEx)** — quality IDP + healthy data ecosystems; (2) **Empower human in loop (OpEx)** — context engineering + trust in AI. Indicators: leading = experiment frequency + deployment frequency; stability gauge = change failure rate + rework. Three scenarios to model (Conservative 0.8 value × 1.5 cost / Realistic 1.0 / Optimistic 1.2 × 0.8). External data leveraged: 78% of executives report ROI on ≥ 1 gen AI use case (Google Cloud), 88% of early agentic AI adopters see positive ROI, **35-40% productivity greenfield vs ≤10% brownfield/legacy** (Stanford), inference cost ÷280 between Nov 2022 and Oct 2024 (Stanford AI Index 2025), **727% ROI over 3 years** for Google Cloud AI customers, average **8-month** payback in the AI market. Assumed weaknesses: *"all models are wrong"* — the model needs contextualizing, the calculator needs adjusting; risk of double-counting value (time saved → both avoided hire AND extra revenue); the user experience link is "loose" and therefore excluded from the calculator. **Deontological insight**: ***"We don't measure AI by the code it writes but by the bottlenecks it clears"*** — measured by bottlenecks cleared, not by code volume. **Major relevance** for CIOs/CTOs needing to build a defensible AI business case in front of a CFO/board; for France/Europe, to be read alongside Wescale (realistic X3-X4), Tatsyi/Raiffeisen Bank Ukraine (bank case study, −75 people but deliberate reinvestment), Frizzo (3-5× median), Curran/Intercom (3× R&D over 16 months), DORA Report 2025 (which this ROI report builds on).
#DORA ROI of AI-assisted software development#Google Cloud DORA report 2026.1#J-Curve of AI value realization
Rapport conjoint **DORA team × delta team** (Google Cloud Professional Services). Auteurs principaux : **Eva Dong** (AI Value Realization Americas, ex-McKinsey 8 ans, Master Financial Engineering Michigan) · **Andre Ellis Jr.** (Cloud Financial Operations Lead, Morehouse + Wharton MBA) · **Nathen Harvey** (DORA team lead, co-auteur multiples DORA reports + 97 Things Every Cloud Engineer Should Know) · **Vivian Hu** (10X Technology Consultant, contributrice DORA 2025 State of AI-assisted Software Development) · **Ursula Lübbert-Passing PhD** (AI Value Realization EMEA, 20 ans benchmarking + value advisory, PhD effort estimation software projects) · **Eric Maxwell** (lead 10X Technology consulting, ex-Chef Software, contributeur DORA) · **Aaron Wanjala** (cloud developer advocate Spring Boot/Angular). Conseillers et contributeurs : **Ben Jose · Eric Lam · Matt Orr · Allison Park · Ryan J. Salva · Jerome Simms · Dave Stanke · Cedric Yao**. Design : Human After All (humanafterall.studio). Document publié sous licence **CC BY-NC-SA 4.0** · version v. 2026.1 · citations retrieved February 2026.