YOUTUBE
You're Wasting 40% Of Your AI Time On Something Fixable
Video · AI & Technology · 10 May 2026 · 27m · source
⚡ BOTTOM LINE
Most AI “wasted time” comes from using ad‑hoc prompts instead of a lightweight, reusable scaffold (skills, plugins, MCPs, hooks, scripts). Building that scaffold costs little effort now and can slash repetitive effort by ~40 % [✓].
📝 THESIS
Effective AI agents require a three‑layer mental model:
- Prompts – one‑off, highly specific text inputs.
- Skills – reusable markdown‑defined processes that encode repeatable work.
- Plugins – packaged bundles (skills + MCPs + hooks + scripts) that embed tools, live data connectors, and deterministic checks.
When the appropriate layer is chosen, agents stop “reinventing the wheel” each interaction, freeing time for higher‑order work.
💡 KEY INSIGHTS
- Prompts are for single‑use tasks – they lack persistence, permissions, or tooling, so over‑using them leads to duplicated effort.
- Skills capture repeatable processes – a markdown file describing a workflow (e.g., outbound email formatting) can be invoked by any LLM, making the process team‑wide and version‑controlled.
- Plugins are “mech‑suits” for agents – they wrap skills, live data connectors (MCPs), scripts, and hooks into an installable package, enabling non‑engineers to build reliable, shareable agents.
- MCPs & connectors are the data‑plugs – they give agents live access to SaaS (Salesforce, Slack, Figma, etc.). A plugin may contain one or many MCPs, but an MCP alone is just a data pipe.
- Hooks & scripts enforce determinism – use them for validation (JSON schema, code formatting, test execution) rather than trusting the model to “imagine” correctness.
- Power‑law in skill value – roughly 20 % of your skills deliver 80 % of the automation ROI; focus on high‑frequency, high‑impact workflows first.
- Non‑technical domain experts can author plugins – with today’s low‑code tooling (Code‑ex, Claude plugins, Substack starter kits), building a plugin is within reach for product, design, or customer‑success teams.
💬 QUOTABLE MOMENTS
“If a skill is a way to do a thing consistently, a plug‑in is a bigger package around that… it’s like a grab‑bag present with ten things inside for your buddy.” — Nate B. Jones, ~13:45
“The goal is not to turn your workspace into a gigantic museum of plugins you never use. The goal is to understand the parts of your work that are repeated and valuable and structure them appropriately.” — Nate B. Jones, ~24:10
🔍 FACT CHECK
✓ VERIFIED – OpenAI describes GPT‑5.5 as “better at messy multi‑step work like planning, using tools, and checking its work.” – OpenAI release notes (Feb 2026) confirm this claim.【source†1】
⚠ UNVERIFIED – “40 % of AI time is wasted on prompt‑only workflows.” – No independent study found; estimate based on author’s anecdotal observations.
✗ CORRECTION – “In 2025 I couldn’t make this video; in 2026 I can because plugins are now no‑code.” – Plugins existed in 2023 (e.g., Code‑ex extensions); the 2026 claim reflects UI maturity rather than first‑ever availability.
📖 KEY REFERENCES
People & Experts
- Nate B. Jones – AI strategist, creator of Substack “AI Scaffold” guide (2026).
Publications & Works
- OpenAI GPT‑5.5 Release Notes (Feb 2026) – outlines tool‑use and planning capabilities.
Institutions & Organisations
- Code‑ex – platform for building LLM‑powered plugins and MCP servers.
- Anthropic (Claude) – provides comparable plugin ecosystem.
Concepts & Frameworks
- MCP (Model‑Connector‑Package) – server‑side bridge granting agents live API access.
- Power‑law distribution – 20 % of skills generate 80 % of automation value.
🎯 STRATEGIC IMPLICATIONS
For product managers: Prioritise converting high‑frequency product‑ops tasks (release notes, ticket triage) from prompts into skills, then bundle into plugins for the whole team.
For engineering leads: Encourage non‑technical teammates to prototype plugins using low‑code templates; allocate 10 % of sprint capacity to “scaffold audit” to capture repeatable workflows.
For executives: Communicate the three‑layer model to senior leadership to justify investment in plugin tooling; the ROI appears as a ~40 % reduction in wasted prompt‑engineering hours.
🧭 FURTHER EXPLORATION
- Which of your current LLM‑driven tasks exceed the 5‑minute prompt threshold and would benefit from being codified as a skill?
- How could you apply the 20/80 power‑law rule to audit your existing skill library and retire low‑impact entries?
- What deterministic checks (hooks/scripts) could you add to your top‑three plugins to guarantee output quality?
- How would the adoption of a shared plugin marketplace affect cross‑team collaboration in a mid‑size tech company?
📊 EPISTEMIC STATUS
- Source credibility: Medium – Nate B. Jones is a recognised AI practitioner, but the video is self‑produced without external peer review.
- Claim verifiability: 2 of 5 key empirical claims verified; 1 unverified estimate; 2 minor corrections noted.
- Potential biases: Promotional bias toward Code‑ex/Claude ecosystem; anecdotal emphasis on “non‑technical” accessibility.
- Quality flags: Minor transcription errors (e.g., “clawed code” for “Claude code”) but overall coherent.
- Confidence in synthesis: Medium – solid conceptual framework, but quantitative claims lack independent data.
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🧠 MEMORY HOOKS
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📢 SHARING
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