Using Shakespii

using-shakespii

Intent

Teach an agent to drive the shakespii CLI — the deterministic lint and scaffold substrate for Agent Skills — so skills get created and repaired against the anatomy contract instead of by taste. The CLI decides; this skill teaches the loop around it.

Inputs

• Audit: the path to an existing skill directory (one containing SKILL.md), or a

corpus root — a directory of skill directories — audited in one run with --corpus.

• Authoring: the new skill's kebab-case name, its purpose, and the situations that

should trigger it — all three confirmed with the human before scaffolding.

• Optional: a specific finding or rule ID the human wants addressed first.

Preconditions

• Bun is installed and on PATH (bun --version succeeds).
• The shakespii CLI resolves: either installed globally (bun add -g shakespii;

the binary lands in ~/.bun/bin), or the repo cloned and linked (bun install && bun link inside it); shakespii --version then succeeds.

• Audit: the target skill directory is readable.
• Authoring: the parent directory for the new skill is writable.

Procedure

Shared core, both branches:

1. Run shakespii lint --json and parse stdout (schema version: 1). Each

finding carries ruleId, severity, file, line, message.

1. Exit codes: 0 means no errors — proceed. 1 means errors — enter the fix loop.

2 means lint itself could not run — report the stderr message verbatim and stop; never guess around a broken run.

1. For each finding, look up its ruleId in

[references/rule-remediations.md](references/rule-remediations.md) and apply the minimal fix. A finding whose rule has no entry there is fixed from its own message — messages are written to be actionable.

1. Re-lint. Loop until exit 0, then handle warnings: fix each one, or surface it to

the human explicitly with a reason. Never silently ignore a warning.

Audit branch — fix an existing skill:

1. Lint the directory the human named and work the fix loop above. For a corpus

root, run shakespii lint --corpus --json: work each skill's findings with the same loop, and treat corpusFindings (XS rules, whose sites name every involved skill) as refactor suggestions spanning skills.

1. Preserve the skill's voice and intent: reword a description to lead with its

trigger; do not rewrite what the skill is for.

1. Report before/after finding counts, what changed per rule, and any warnings left

standing with reasons.

Testing a skill's evals

After a skill lints clean, verify its eval suite with the harness:

shakespii test  --json

Exit codes: 0 = no error findings (warnings allowed), 1 = error findings to fix, 2 = the run itself failed (bad path, no SKILL.md, claude CLI missing). The deterministic stage checks that evals/evals.json exists, parses, follows the skill-creator schema (skill_name equal to the frontmatter name, unique integer ids, non-empty prompts and expectations, at least three cases), and references only files that exist inside the skill directory. Without --run the scenario and grading stages report skipped — the command is free and safe to loop on.

To actually execute the evals — a headless agent runs each case, then an LLM grader scores every expectation with cited evidence — add --run:

shakespii test  --run --json

--run spends real tokens (one executor and one grader session per eval case), so confirm with the human before the first run on a suite (when a human is present to answer; in a non-interactive run, an explicit approval already given in the task prompt satisfies this). Results are cached per (skill content, eval, model): re-running after no changes replays instantly from cache; editing the skill or its evals re-runs only because the content hash changed. --fresh forces re-execution despite the cache; --model overrides the default executor/grader model (sonnet). Fix loop: deterministic findings name the JSON path of the defect in evals/evals.json; scenario findings mean the executor run itself failed (timeout, crash); grading findings quote the failed expectation and the grader's evidence — fix the skill (or a genuinely wrong expectation, with the human's approval) and re-run until exit 0.

Benchmarking a skill

Once the evals pass, measure the skill's actual capability impact:

shakespii bench  --json

bench runs every eval in evals/evals.json with the skill mounted and again without it, --runs times per configuration (default 3), and writes benchmark.json. Read run_summary.delta for the with-vs-without capability delta on pass rate, time, and tokens — that delta is the signal, not the exit code: exit 0 means the matrix was measured and written, full stop, whether the skill helped, hurt, or made no difference; bench never gates on the delta. Exit 1 means a run failed after its retry and nothing was written; exit 2 means the eval suite has deterministic findings (fix those first, same gate as test) or the claude CLI is unavailable. --model overrides the default executor model (sonnet); --fresh bypasses the cache to force a fresh measurement. Like --run, bench spends real tokens per run — confirm with the human before the first run on a suite (or accept an approval the task prompt already grants), and never point it at an untrusted third-party skill: both --run and bench execute with --dangerously-skip-permissions.

Measuring trigger accuracy

A skill only helps if it fires at the right moments, so measure that directly rather than trusting the description by eye. Author evals/triggers.json: at least 16 labeled queries mixing prompts that should trigger the skill with near-miss negatives that should not — TR02 lints this set statically on every lint run (missing file, schema errors, too few queries, no negatives). Then run it for real:

shakespii test  --run --triggers --json

The trigger stage mounts the skill and issues each query 3 times against a real session, scores a query as triggered when a majority of its reps fire, and reports overall accuracy across the set. That stage fails (an error finding) below 0.8 accuracy. Treat description edits as a deliberate loop, not a guess: adjust the wording, re-run with --fresh to bypass the cache and force fresh measurement, and stop once accuracy holds at 0.8 or above without regressing queries that already passed.

Installing a skill

Once a skill lints clean and its evals pass, land it in an agent's live skills directory through the gate rather than by hand-copying:

shakespii install  --json

install re-lints the source, runs the deterministic eval checks when evals/evals.json exists, and only then copies the skill into the target. Lint errors or deterministic failures block the install (exit 1); warnings never block — surface them to the human instead. The default target is Claude (~/.claude/skills); --provider selects others (claude, codex, cursor, antigravity, gemini, agents, ezio — repeat the flag for several, or --provider all for every provider detected on the machine), and --target installs into an arbitrary directory. An occupied destination — an existing directory or symlink — is refused unless the human approves replacing it; with that approval, add --force. The --json report (version: 1) carries the gate verdict in gate.lint and gate.test plus one entry per target in targets[] (installed, forced, reason); targets[].advisory lists cross-skill duplication findings (XS rules) against the skills already installed at that target — advisory only, never a block, but report them to the human. Exit codes: 0 = gate passed and every target installed; 1 = gate blocked or a target refused; 2 = usage error (unknown provider, unresolvable skill).

Authoring branch — create a new skill:

1. Confirm name, purpose, and trigger situations with the human (or adopt

them from the task prompt when it already supplies and approves them), then run shakespii init in the agreed parent directory.

1. Fill every section of the scaffold, replacing each placeholder token (the scaffold

marks them with TODO(shakespii) plus a trailing colon) with real content.

1. Write at least one concrete worked example in Examples — real input, real output.
2. Fill evals/evals.json with at least three cases, one of them a near-miss

negative that must not trigger the skill.

1. Work the fix loop until lint is clean, then present the skill, its lint output,

and its evals to the human. Install only with explicit approval and never with findings outstanding — and when approved, install through shakespii install (see "Installing a skill"), not by hand-copying.

Output

• Audit: the skill's files fixed in place, plus a report of before/after finding

counts and per-rule changes.

• Authoring: a new skill directory (SKILL.md, README.md, evals/evals.json) that

lints clean, presented to the human for approval — not installed.

Examples

Audit, end to end. The user says: "Lint my skill at ~/.claude/skills/summarize-notes and fix what it finds."

Input: shakespii lint ~/.claude/skills/summarize-notes --json exits 1 with one finding — ruleId FM04, severity error, file SKILL.md, line 3, message "description must begin with a trigger phrase (one of: use when, use for, use if, use this, invoke when, when the user)".

The agent opens the FM04 entry in the remediation reference and rewrites the description from "Compresses long chat threads into short bullet summaries" to "Use when the user asks to summarize a long chat thread into short bullets", changing nothing else.

Output: the re-lint exits 0 with zero findings, and the agent reports "1 error → 0 findings; FM04 fixed by rewording the description trigger-first", listing no remaining warnings.

Anti-patterns

• Editing skill files by taste without running lint first — the CLI is the arbiter.
• Weakening content to appease a rule: deleting a section to silence a finding, or

gutting a description. Fix the defect, keep the substance.

• Pointing --corpus at a single skill directory — corpus mode takes the parent

directory; lint a single skill without the flag.

• Installing a freshly authored skill without human approval, or with findings open.
• Looping on a run that exited 2 — that exit means lint itself could not run;

report it instead of retrying.

• Hand-copying a skill into a live skills directory when shakespii install

exists — the gate is the point of the install step.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: vuphanse
• Source: vuphanse/ai-shakespii
• License: MIT
• Homepage: https://www.npmjs.com/package/shakespii

Install and usage instructions live in the source repository linked above.