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Prompts

Prompts are short, parametric commands shipped under .github/prompts/ that wrap the authoring loop: scaffolding eval suites at creation, cross-model benchmarking, iterative quality improvement, and readiness assessment for promotion.

Use them while authoring, not after. They exist so that every skill and agent you write is grounded in measurable evals from the first commit. Drafting a SKILL.md or .agent.md without running /skill-onboard or /agent-onboard is shipping blind — the prompts are how you turn a rough draft into something a model can reliably execute.

When to use which​

GoalPrompt
"This skill has no eval suite yet — scaffold one."/skill-onboard
"This agent has no eval suite yet — scaffold one."/agent-onboard
"Which model handles this skill best?"/skill-bench
"Which model handles this agent best?"/agent-bench
"This skill scored low — help me fix it."/skill-improve
"This agent scored low — help me fix it."/agent-improve
"Is this skill ready for the pilot tier?"/skill-promote
"Is this agent production-ready?"/agent-promote

Cost notice: every prompt invokes waza run one or more times. Each leg consumes premium model requests. Bench and promote prompts run across multiple models (default four); improve loops can run up to three rounds. Plan your quota before invoking.

File format​

All prompts share the same shape:

---
agent: 'agent'
description: 'One-sentence description'
argument-hint: '[paramA=...] [paramB=...]'
---

# Prompt body

Procedural steps the prompt's wrapping agent will execute,
typically a sequence of `bash` blocks and decision points.

The agent: 'agent' value pins execution to VS Code's generic chat agent (no specific persona). Add or edit prompt files directly under .github/prompts/; no further registration is needed.

skill-onboard​

Description. Stage 0 of the eval lifecycle — bootstrap a brand-new eval suite for a skill that currently has none. Scaffolds eval.yaml + positive / negative / off-topic task files, patches them to repo conventions (hybrid graders, concrete prompts, schema headers), registers the skill at the expanded tier in manifest.yaml, and runs a single-model smoke trial to confirm the suite is wired correctly.

Arguments. [skillName=...] [positiveTasks={2|3|4}] [negativeTasks={1|2}] [smokeModel=claude-sonnet-4.6]

Interactivity. Interactive. Pauses for approval before appending to manifest.yaml and before running the smoke trial.

Output. A new .github/evals/<skill>/ directory containing eval.yaml, positive tasks, a trigger-only negative task, and an off-topic refusal task, plus a { name: <skill>, tier: expanded } entry in manifest.yaml. The smoke trial prints per-task pass / fail and aggregate score.

Out of scope. Does not edit SKILL.md (use /skill-improve for that) and does not promote the skill to the pilot tier (use /skill-promote after the skill has matured in expanded).

Cost. ≈ 5–8 premium requests per invocation: 1 for the waza suggest --apply scaffold + 1 × len(tasks) for the smoke trial (default 4) plus per-task LLM-judge calls.

Use when. You've authored or refactored a SKILL.md that has no companion eval suite and you want a guarded path from zero to a working expanded-tier entry without hand-writing every task YAML.

agent-onboard​

Description. Stage 0 of the agent eval lifecycle — bootstrap a brand-new eval suite for a custom agent that currently has no evaluation. Scaffolds .github/evals/agents/<agent>/ with eval.yaml, a mirror copy of the .agent.md (waza walks the directory under skill_directories: ["."]), positive and negative tasks, and an off-topic task with a clean_refusal grader that asserts the agent identifies itself and redirects to its specialty. Runs a single-model smoke trial. No edits to the canonical .agent.md or to manifest.yaml (agent evals are auto-discovered from the filesystem).

Arguments. [agentName=...] [positiveTasks={2|3|4}] [negativeTasks={1|2}] [smokeModel=claude-sonnet-4.6]

Interactivity. Interactive. Pauses for approval before writing the eval directory and before running the smoke trial.

Output. A new .github/evals/agents/<agent>/ directory with eval.yaml, a mirrored <agent>.agent.md, positive tasks (hybrid trigger + answer_quality graders), a trigger-only negative task, and an off-topic refusal task. The smoke trial prints per-task pass / fail and an aggregate score.

Out of scope. Does not edit the canonical .github/agents/<agent>.agent.md (use /agent-improve for that), does not run readiness checks (use /agent-promote after the agent has matured), and does not touch manifest.yaml.

Cost. ≈ 6–9 premium requests per invocation: 1 × len(tasks) for the smoke trial (default 4) plus per-task LLM-judge calls.

Use when. You've authored or refactored an .agent.md that has no companion eval suite and you want a guarded path from zero to a working agent eval directory.

skill-bench​

Description. Cross-model benchmark for a single skill: runs waza eval once per model, captures results, compares with waza compare, and prints a one-line winner summary.

Arguments. [skillName=...] [models=claude-sonnet-4.6,gpt-5.4,gpt-5-codex,claude-opus-4.6]

Interactivity. Non-interactive once skillName is supplied. Prompts for the name if omitted.

Output. A waza compare table (per-model aggregate score, success rate, latency, premium requests) plus a one-line winner.

Use when. You want to know which model handles a skill best — for example, before promoting a skill or after editing the SKILL.md substantially.

agent-bench​

Description. Same as skill-bench but targets a custom agent (under .github/evals/agents/<name>/).

Arguments. [agentName=...] [models=claude-sonnet-4.6,gpt-5.4,gpt-5-codex,claude-opus-4.6]

Use when. Sweeping the model field for an agent. Pair with /agent-promote once the winner is clear.

skill-improve​

Description. Local feedback loop for a single skill: baseline → audit → propose edits → apply (with approval) → re-rank via waza compare. Optionally loops up to 3 rounds for deeper refinement.

Arguments. [skillName=...] [iterations={1|2|3}] [rescoreQuality={true|false}]

Interactivity. Interactive. After each proposed edit you approve, reject, or amend.

Output. A per-round diff plus updated comparison table. The skill file is modified in place when you approve.

Use when. A skill scored below the pilot promotion bar and you want a guided revision loop instead of hand-editing.

agent-improve​

Description. Same as skill-improve, applied to .github/agents/<name>.agent.md. Also re-syncs the eval-directory mirror (.github/evals/agents/<name>/<name>.agent.md) after every approved edit.

Arguments. [agentName=...] [iterations={1|2|3}] [rescoreQuality={true|false}]

Use when. An agent's persona-lock leaks, off-topic refusals are weak, or trigger precision is below threshold.

skill-promote​

Description. Assess whether a skill in the expanded eval tier is ready to graduate to pilot (full 4-model fan-out). Runs the eval suite, checks against numeric promotion criteria, and prints a graduation report.

Arguments. [skillName=...]

Output. A PROMOTE / BLOCK verdict with the specific criterion that gated the decision (e.g. success_rate < 0.85 on gpt-5.4). When PROMOTE, it suggests the manifest.yaml patch.

Use when. A skill has been stable in expanded for a few PRs and you're considering moving it to pilot.

agent-promote​

Description. Assess whether a custom agent is production-ready: runs the eval suite across pilot-tier models, checks against numeric readiness criteria, and prints a graduation report.

Arguments. [agentName=...] [models=claude-sonnet-4.6,gpt-5.4,gpt-5-codex,claude-opus-4.6]

Output. A readiness verdict per criterion (persona-lock, off-topic refusal rate, trigger precision, budget compliance, answer-quality threshold) and an overall GO / NO-GO.

Use when. Before flipping an agent's user-invocable: true for the first time, or before announcing it to users.