Authoring Skills

authoring-skills

Intent

Turn a human's idea into a finished Agent Skill through a structured loop: interview the human for the raw material, draft against the anatomy contract, critique with a rubric of qualities no linter can check, and refine until the harness — not taste — says the skill works. The using-shakespii skill teaches how to drive the CLI; this skill decides what the new skill should say.

Inputs

• The idea: a problem statement, requirement, or repeated workflow the human wants captured as a skill.
• A writable parent directory for the new skill.
• Optional: raw material the human already has — notes, transcripts, a real worked example, memory excerpts.

Preconditions

• The shakespii CLI resolves (shakespii --version succeeds); setup lives in the using-shakespii skill's Preconditions.
• The using-shakespii skill is available — every CLI mechanic here (fix loop, eval runs, trigger measurement) delegates to it.
• A human is reachable for the interview, or the task prompt already supplies and approves the interview's answers.

Procedure

Phase 1 — Interview. Ask one question at a time, multiple-choice where the options are enumerable, until every anatomy section has raw material:

1. Intent: what problem, for whom, and what does a successful use look like?
2. Triggers: at least five real requests that should fire the skill, and at least three lookalikes that must not.
3. Inputs and preconditions: what the skill consumes; binaries, paths, and environment it assumes.
4. Procedure: walk one real occurrence of the workflow end to end.
5. Example: one real input with its real output — not an invented pair.
6. Failure modes: what has gone wrong when this was done by hand.

The interview ends when you can state the kebab-case name, the purpose, and the trigger list back and the human confirms them — or when the task prompt already supplied and approved all three. In a non-interactive run where the prompt leaves questions open, ask them all in one batch as your final output instead of guessing.

Phase 2 — Draft. Scaffold, then fill from the interview:

shakespii init <name>

Fill every scaffold section, replacing each placeholder token. Craft rules the linter cannot enforce:

• Freedom calibration: prescribe exactly where deviation breaks things (exact commands, exact formats); leave open where judgment beats prescription. A step that says "run these five commands in order" and a step that says "choose an appropriate threshold" should both survive the question "why this tight, why this loose?".
• Progressive disclosure: SKILL.md carries the loop; depth (rubrics, rule lists, long references) moves to references/ files linked where used.
• The description leads with its trigger situations — the ones the interview named — not with the skill's implementation.
• The Examples section transcribes the interview's real input→output pair.
• Anti-patterns come from the interview's failure modes.

Phase 3 — Critique. Two layers, in order:

1. A fresh-eyes pass against references/critique-rubric.md, fixing what it catches.
2. The lint fix loop, delegated to using-shakespii: shakespii lint <dir> --json, apply remediations, re-lint until exit 0, handle warnings explicitly.

Phase 4 — Refine. Author the eval suite, then let the harness judge:

1. Write evals/evals.json (at least three cases, each an in-skill behavior branch — happy path, refusal or error branches, variants; scope negatives belong in evals/triggers.json) following references/headless-eval-rules.md.
2. Write evals/triggers.json (at least sixteen labeled queries, with near-miss negatives on the boundary of any neighboring skill).
3. Gate with the harness — token spend confirmed with the human, or already approved in the task prompt:

shakespii test <dir> --run --triggers

4. On trigger misses, reword the description and re-measure with --fresh; stop once accuracy holds at or above 0.8 without regressing queries that already passed. The using-shakespii skill documents the loop's CLI semantics.

Phase 5 — Present. Hand the human the skill directory, its lint output, and its scenario and trigger results, plus any open questions. Do not install the skill anywhere; installation is a separate, explicitly approved act.

Output

• A new skill directory (SKILL.md, README.md, evals/evals.json, evals/triggers.json, optional references/) that lints clean, with recorded scenario and trigger results.
• A presentation of that evidence to the human. The skill is not installed.

Examples

The human says: "I want a skill that helps agents write good commit messages."

Interview (excerpt). Q: "What does a bad commit message look like in your repos — what specifically goes wrong?" A: "They describe the diff instead of the why; bodies restate the subject." Q: "Name three requests that should trigger this skill." A: "Write the commit message for this change; clean up my commit history wording; draft a PR-merge commit."

Draft (excerpt). The interview's answers become the description —

description: "Use when the user asks to write or improve a commit message or
commit-history wording — leads with the change's why, keeps the subject
imperative and under fifty characters, and never restates the subject in the
body."

— and the bad-message example from the interview becomes the worked example: input, a diff adding a retry wrapper around one HTTP call; output, subject "retry transient checkout-service timeouts" with a body explaining the incident that motivated it.

Anti-patterns

• Inventing interview answers instead of asking the human — or instead of reading them from a task prompt that already supplies them.
• Pasting the raw idea into every section; each anatomy section answers its own question.
• Stopping at lint exit 0: lint checks the contract, while the rubric and the eval runs check whether the content is any good.
• Eval expectations that need a mid-run human reply — the headless rules file shows how to reword them.
• Re-teaching CLI mechanics inline instead of delegating to using-shakespii.
• Installing the finished skill without an explicit approval.