A good Claude Code skill passes four checkpoints every time it runs:

  • It triggers on the right prompt and only the right prompt.
  • It loads its full instruction set correctly.
  • It follows its steps in order.
  • It produces structured output that matches a defined contract.

Most skills fail at least one. Many fail three.

TL;DR: The production bar for Claude Code skills has four checkpoints: reliable triggering, correct loading, step adherence, and consistent output. Most community skills fail the triggering checkpoint (vague description) and the output checkpoint (no output contract). At Agent Engineer Master, we call a skill that passes only vibes checks a "prompt in a trenchcoat."

What does "prompt in a trenchcoat" actually mean?

A "prompt in a trenchcoat" is a SKILL.md file that looks like a skill but behaves like a plain prompt. It has frontmatter, a description, and numbered steps. What it lacks is structural integrity: consistent, predictable output across every invocation. Any skill that passes vibes checks but fails the four production checkpoints earns this label.

There are over 400,000 Claude Code skills indexed across public repositories (SkillKit / GitHub, 2026). Statistically, most of them are vibes with a file extension.

Signs you are looking at a prompt in a trenchcoat:

  • The description says "This skill helps you with code review" instead of "Runs a code review against the project's criteria. Invoke on any PR diff or file set."
  • The steps end with "Use your best judgment for the final output" instead of a defined format.
  • There is no "Does NOT Produce" section.
  • The SKILL.md contains 400 lines of pasted domain knowledge with no reference files.

The difference between a prompt and a skill is not the file format. It is whether the system produces the same quality output on the 50th invocation as the first.

"The failure mode isn't that the model is bad at the task, it's that the task wasn't specified tightly enough. Almost every production failure traces back to an ambiguous instruction." — Simon Willison, creator of Datasette and llm CLI (2024)

In our bar checks at Agent Engineer Master, the most common failure is not the instruction quality. It is the trigger description. A skill that runs correctly when invoked explicitly but never fires automatically has failed checkpoint one before the user even noticed. LangChain's 2024 survey found that 51% of teams already have agents in production, yet performance quality remains the top barrier to wider adoption (LangChain, 2024). The bottleneck is not model capability. It is specification quality.

What are the four production checkpoints?

A production-grade skill passes all four checkpoints, and each can fail independently of the others. A skill can trigger reliably but load without its reference files. It can load correctly but skip steps in sequence. Test each checkpoint in isolation before assuming the full chain works.

  1. Checkpoint 1: Reliable triggering. The skill activates when the user's request matches the skill's intent. It does not activate when the request is adjacent but different. Testing across 650 activation trials showed that imperative descriptions achieve 100% activation while passive descriptions sit at 77% (AEM internal research, 2026). The gap is entirely in description wording. A description that says "Runs a security scan on any code diff" outperforms "Can help with security reviews."

  2. Checkpoint 2: Correct loading. When triggered, the skill loads its full instruction body and any reference files it needs, in the right order. Failures here are silent: the skill runs but without context it needed. Symptoms include steps that seem to be followed partially, outputs that lack domain-specific criteria, or inconsistent quality across invocations. Reference file order matters: Liu et al. at Stanford found accuracy drops by more than 30% when relevant content is placed in middle positions of long contexts rather than at the start or end (Stanford NLP Group / TACL, 2024).

  3. Checkpoint 3: Step adherence. Claude follows the steps in sequence without skipping, reordering, or collapsing them. Mediocre skills use prose paragraphs instead of numbered steps. Numbered steps have a compliance advantage: they carry implied sequence and completeness. A paragraph does not. If your skill uses prose instructions, Claude improvises the sequence. That improvisation produces variance. The AGENTIF benchmark found that even the best-performing LLM perfectly follows fewer than 30% of agentic instructions when they involve multi-step constraints (Qi et al., Tsinghua / NeurIPS, 2025).

  4. Checkpoint 4: Consistent output. The output matches a defined contract every time. What format does the skill produce? What sections must it include? What does it never include? A skill with no output contract produces whatever Claude thinks is appropriate. That varies by context, model version, and prompt phrasing. A skill with an explicit output contract produces a defined structure. Addy Osmani's benchmarks measured this directly: explicit output formats shift consistency from approximately 60% to over 95% (Google Chrome Engineering, 2024).

What does a mediocre skill look like in practice?

A mediocre skill has a vague description, no output contract, and no explicit criteria. It triggers inconsistently, produces different output on every run, and breaks on edge-case inputs. The comparison below shows the same code review skill in two forms: one that fails three checkpoints, one that passes all four.

A mediocre code review skill:

description: "Helps with code reviews and identifying issues in code."

Steps:

  1. Review the code
  2. Identify any issues
  3. Provide feedback

That is it. This skill:

  • Triggers inconsistently because the description is vague
  • Has no output contract, so format varies on every run
  • Has no criteria, so "identify issues" means different things across invocations
  • Has no explicit "Does NOT Produce" boundary

A production code review skill:

description: "Runs a structured code review against this project's criteria: security patterns, naming conventions, test coverage, and documentation completeness. Invoke on any diff, PR, or file set."

Steps:

  1. Read the project CLAUDE.md for team-specific criteria.
  2. Load references/review-checklist.md for the full 24-point checklist.
  3. Review the provided diff or files against each checklist item.
  4. Output a structured report: Summary (3 sentences), Critical Issues (numbered list), Minor Issues (numbered list), Positive Patterns (bullets).
  5. End with a pass/fail verdict and a one-line recommendation.

Does NOT Produce:

  • Rewritten code (provide comments only)
  • Architectural recommendations (that is a separate architecture-review skill)

The second version is 10x more specific. It passes all four checkpoints. The first is a prompt in a trenchcoat.

Why do most skills fail the production bar?

The honest answer: most skills are built for the builder's own use case, tested once or twice in favorable conditions, and published. The builder knows the implied context. The skill does not. Every assumption the builder carries in their head is an assumption the skill fails to encode. LangChain's 2024 State of AI Agents survey (1,300+ respondents) found that performance quality is the primary barrier to production deployment, cited more than twice as often as cost or safety concerns (LangChain, 2024).

Three failure modes we see repeatedly in commissioned skill work at AEM:

  • Implicit criteria. The skill says "review for quality" but quality means something specific to that team. The criteria are in the builder's head, not in the skill. Fix: encode the criteria in a reference file and load it explicitly in step one.

  • Missing edge case handling. The skill works perfectly on a standard input. It produces garbage on a 3,000-line diff, an empty file, or a non-code input. Fix: add a "Failure Modes" section to the SKILL.md that names the edge cases and the correct behavior in each.

  • Output format drift. The first 10 invocations produce consistent output. By invocation 50, the format has shifted because the description changed slightly or a different model version interpreted the output contract differently. Fix: add an explicit output template in the assets folder and reference it from the output step.

What the four checkpoints do not cover: domain correctness. A skill can pass all four checkpoints and still give wrong answers about your codebase. The checkpoints test structural integrity: consistent triggering, loading, sequencing, and output shape. They do not verify that the criteria in your reference file are correct, that the step logic is sound, or that edge cases in your specific domain are handled. A skill that reliably produces a structured code review can still miss a critical security pattern if that pattern is absent from the checklist. The checkpoints are a floor, not a ceiling. Gartner predicts over 40% of agentic AI projects will be canceled by end of 2027 (Gartner, June 2025). Passing the structural bar is necessary. It is not sufficient.

For a deeper look at the structural elements of a well-built skill, see What Goes in a SKILL.md File? and What Does the Description Field Do in a Claude Code Skill?. For what to avoid when building, see What Should I Avoid When Creating My First Skill?.

Frequently Asked Questions

The four-checkpoint framework is straightforward to audit: each checkpoint has a concrete test you can run in under ten minutes. Triggering and output consistency are the most common failure points; step adherence and loading failures are usually caught on the second or third invocation when output quality drops without obvious cause.

How do I test whether my skill passes all four checkpoints? Test each checkpoint in sequence. For triggering: invoke the skill via natural language ten times with varied phrasing. Count how many times it activates. For loading: add a step that confirms reference files were read and check the output. For step adherence: compare the output against your numbered steps in order. For output consistency: run the skill ten times on the same input and compare outputs.

Can a simple skill with 20 lines pass all four checkpoints? Yes. Checkpoint compliance is about structural completeness, not length. A 20-line skill with an imperative description, three numbered steps, and a one-line output format can pass all four. A 200-line skill with a vague description and no output contract fails checkpoint one before it even runs.

What is the minimum viable output contract? A single sentence naming the format and the required sections. "Output a markdown list with three sections: Critical Issues, Minor Issues, and Positive Patterns." That is enough to shift consistency from roughly 60% to over 90% on most tasks. LangChain's 2024 report noted the average agentic trace grew from 2.8 steps to 7.7 steps year-over-year, meaning output contract complexity is increasing as skills chain into longer workflows (LangChain, 2024).

Is a skill that only ever gets used via slash command worth optimizing for auto-triggering? Yes. Two reasons. First, explicit invocation requires you to remember which skill to call. As your library grows past 15 skills, you start forgetting what exists. Auto-triggering is how skills stay discoverable. Second, the description field is the only part of your skill that Claude reads before deciding whether to use it at all. A weak description signals a weak skill, even for explicit invocations.

What is the most important checkpoint to fix first? Checkpoint 1 (triggering) because it blocks the others. A skill that never activates automatically has no chance to fail at steps 2-4. Fix the description before auditing anything else.

Last updated: 2026-05-01