Skill Authoring — easy-cheese conventions

This document codifies the skill-authoring rules for easy-cheese, drawn from obra/superpowers (CSO principle, Iron Law template, pressure-test gate) and Matt Pocock's skills repo (size budget, authoring checklist). It is the canonical reference for anyone adding or revising a skill.

Description rule (CSO)

The description is the only thing the harness shows the model when choosing a skill. Optimize it for triggering, not explanation.

The rule: sentence 1 = capability; sentence 2 = "Use when [triggering conditions]". No workflow summary, no pipeline-position prose, no feature list.

Rationale: a description that summarizes the skill's workflow creates a shortcut the model takes instead of reading the SKILL.md body. The skill then silently degrades to its own one-line description.

Constraints:

• Maximum 1024 characters (Codex rejects longer descriptions).
• Third-person, present tense.
• Triggering conditions name concrete phrases the user might say.

Test: strip the description and ask: could a model choose this skill over all others based on these words alone? If not, the triggers are missing.

Size budget

Keep SKILL.md bodies lean. The goal is a body a model can read in one pass without losing the thread.

The budget: 3,600 estimated tokens for the SKILL.md body (frontmatter excluded). At this repo's measured prose density that's roughly 150 lines, but tokens are the gated unit, not lines — line count misleads on dense prose: all 16 skills in this repo pass Anthropic's 500-line rule, while 8 exceed our 3,600-token budget on the same bodies.

Enforcement: CI runs this as a shrink-only ratchet — .github/scripts/validate_skills.py, baselines recorded in .github/skill-budgets.json, regenerated via just update-skill-budgets. A skill already over budget when the ratchet was introduced is grandfathered at its recorded value and may only shrink from there; a skill at or under budget must stay under 3,600.

Anthropic's published Level-2 figures are 500 lines / under 5k tokens. easy-cheese deliberately sits tighter than that ceiling.

Practical budget:

• Push satellite detail — step-by-step sub-protocols, reference tables,

prompt templates, large examples — into named references/*.md files. The SKILL.md body points to them; it does not duplicate them.

• The references/ dir is the right home for: long rationalization tables,

output format templates, detailed sub-protocol steps, graph conventions.

• Splitting only helps if SKILL.md says when to read the reference file.

Reference files load only on an explicit link from the body — moving prose into references/ without a pointer just relocates the tokens off the visible budget rather than deferring them from the model's read.

Smell test: if the SKILL.md body has grown to the point where the Flow section is buried below a long Inputs section and three flag tables, it is time to factor.

Provenance: the budget originated as an adaptation of Matt Pocock's \<100-line cap for his skills repo, and has since been restated in tokens against this repo's own measured prose density.

Iron Law / Red Flags / Rationalization-table template

Discipline skills (skills that enforce a process, not just explain a technique) follow this three-part structure. Apply it in a ## Discipline section in the SKILL.md body, or in a references/<skill>-discipline.md satellite file when the body is already at budget.

Iron Law

One sentence. States the gate that must never be skipped. The Iron Law is descriptive, not aspirational: it names what the skill will refuse to do without.

Example shape:

Iron Law: No [output] without [prerequisite step] first.

Red Flags

A short list of signals that the Iron Law is about to be violated. These are the observable pre-rationalizations — the moment before a step gets skipped.

Example shape:

Red Flags — stop if you notice these:
- "The tests will obviously pass after this change."
- "I'll add the test in the next commit."
- [skill-specific patterns]

Rationalization table

A table enumerating the excuses an agent uses to skip the Iron Law step, with an explicit rebuttal for each. The table is adversarial by design: it assumes the model will reach for a rationalization under pressure.

Keep the table to the rationalizations actually observed in practice (5-10 rows maximum). An exhaustive list defeats the purpose.

Authoring review checklist

Before a skill ships, verify:

• Triggers present. The description names concrete phrases the user

might say (not capability prose).

• No time-sensitive information. Skill bodies must not embed version

numbers, dated pricing, or API endpoints that will rot. Point to docs instead.

• Concrete examples. At least one worked example exists — either

inline or in a references/ file.

• References one level deep. The SKILL.md body points to

references/*.md files; those files do not markdown-link further into their own sub-references. A partial head-style read of a reference file would silently miss the tail of a hidden second hop. A link to a reference file that is itself linked from a SKILL.md is a lateral citation, not a second hop, and passes.

• Discipline skills have the Iron Law section. Any skill that enforces

a gate or a loop carries the three-part template above.

• Dual-listed. The skill's directory appears in the

.claude-plugin/plugin.json skills array. The CI check tests/python/testpluginmanifest.py::testclaudepluginmanifestmatchestoplevel_skills enforces this.

Pressure-test-first authoring gate

Iron Law: no skill ships without a failing-baseline subagent run first.

Before writing a new skill body:

1. Construct a representative pressure scenario — a prompt that describes a

task the skill is meant to improve.

1. Run a subagent on that scenario without the skill active. Capture the

output as the baseline.

1. Identify the specific failure: what did the subagent do wrong, skip, or

misframe?

1. Write the skill body to address that specific failure.
2. Re-run the subagent with the skill active and confirm the failure is

corrected.

A skill whose body was never tested against a failing baseline may fix a problem the model does not actually have, or it may describe a workflow the model already follows without being told.

disable-model-invocation frontmatter (candidate — not yet applied)

Matt Pocock's skills repo uses disable-model-invocation: true in the frontmatter of pure-prompt skills (skills that execute immediately without re-prompting the model). The Claude Code frontmatter validator (.github/scripts/validate_skills.py) already allows this key.

Status: candidate-pending-harness-verification. Do not apply to any easy-cheese skill until the harness behavior is confirmed: what does the harness do when this key is set, and does it match the intended "execute immediately" semantics? Document the verification result here before applying.