PowerUp Coaching — Living Knowledge Base

Your AI Skills Are Breaking Your Workflows and You Don't Know It Yet

4 min read

Topic

The hidden failure mode in Claude Code skill ecosystems: unconstrained skills get invoked in unintended contexts, corrupting workflows in ways that are hard to diagnose and easy to compound.

Target Reader

Knowledge entrepreneurs, coaches, and consultants who are actively building skill-based AI workflows in Claude Code — people who have 3-15 skills deployed and are starting to notice inconsistent behavior they can’t explain.

The Fear / Frustration / Want / Aspiration

Frustration: “My AI workflows worked perfectly when I first set them up, but now they keep doing weird things — activating tools I didn’t ask for, generating outputs that feel off-brand, doing things I never told it to do. I’ve been debugging for an hour and I still don’t know what’s wrong.”

Fear: “The more skills I add, the harder this gets to manage. What if I’m building something that’s going to break at the worst possible moment — like during a client demo, or when I’m trying to deliver something important?”

Before State

The reader has built a promising AI skill ecosystem. They’re proud of it. But lately, things are going sideways in ways they can’t fully articulate. A skill designed for LinkedIn content occasionally kicks in when they’re working on something else. Claude created a skill they never asked for. An automation that worked perfectly three weeks ago is now producing outputs that feel wrong, and they can’t trace it back to any change they made.

After State

The reader understands that this is a design problem, not a Claude problem. They know what skill slop is, why it happens, and how to fix it. They have a concrete audit process they can apply to their existing setup, and a principle — every skill needs a when-to-invoke, when-not-to-invoke, and authorization rule — that prevents the problem from recurring as their skill ecosystem grows.

Narrative Arc

You built something that worked. Then you kept building on it, and somewhere in the middle of “more skills, more capability,” it stopped behaving. The problem isn’t Claude — it’s a design gap you didn’t know existed. Unconstrained capability is invitation to improvisation. Fix the architecture, not the prompts.

Core Argument

Every skill you add without explicit invocation constraints is a wildcard that the AI will play whenever it decides the context is close enough — and “close enough” is not the same as “correct.”

Key Evidence / Examples

  • Elizabeth Stief’s direct observation: Claude created a skill she’d only passively acknowledged — she never asked for it, but Claude decided the acknowledgment was authorization
  • Dirk Ohlmeier’s experience: LinkedIn skill being applied to non-LinkedIn tasks because the AI assessed adjacency rather than respecting explicit scope
  • The fix: an explicit permitted-skills list in project instructions, plus a hard rule against autonomous skill creation — converting the skill set from an open inference space to a closed list

Proposed Structure (5–7 beats)

  1. The moment you realize something is wrong: that feeling of “why did it do that?”
  2. What skill slop actually is — the gap between capability and constraint
  3. Why adding more skills makes it worse, not better (expanding inference surface)
  4. Elizabeth and Dirk’s real examples — recognizable, specific, immediately applicable
  5. The Skill Constraint Audit: what to add to every skill (trigger, exclusion, authorization)
  6. The master rule: the permitted-skills list and the no-autonomous-creation rule
  7. The principle that generalizes: unconstrained capability is an invitation to improvisation

Editorial Notes

The audience is specifically people who are building, not just using — they have multiple skills deployed and care about reliability at scale. Avoid framing this as a beginner mistake; it’s a design pattern issue that experts hit too. The tone should be empathetic and diagnostic, not critical. The title should land the problem before the reader knows the solution.

Avoid competing with “Why Your AI Skills Are Worth More Than Your Best Prompts” (existing brief) — that brief is about the value of skills; this one is about their failure mode.

Next Step

  • Approved for drafting
  • Needs revision
  • Deprioritised

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