mikeng-io/agent-skills/preflight

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Lightweight scope clarifier. Invoked by deep-* skills when conversation context is too sparse to determine what to analyze. Asks 1–3 targeted questions one at a time, then returns a structured scope_clarification block for the calling skill to use. Adapted from superpowers brainstorming skill (https://github.com/obra/superpowers) — same principle of asking before acting, scoped to review/analysis intent rather than feature design.

Install

npx vskill@latest install mikeng-io/agent-skills/preflight

SKILL.md

Preflight: Scope Clarifier

Adapted from the superpowers `brainstorming` skill by Jesse Wolfe.
Same core principle: ask before acting. Scoped here to clarifying analysis intent rather than designing features.

Preflight resolves ambiguous scope before a deep-* skill runs its agents. It asks the minimum questions needed to proceed confidently — no more.


When to Invoke

A calling skill should invoke preflight when one or more of the following are true:

  • No specific files, paths, or artifacts are mentioned in the conversation
  • The topic could span 3+ unrelated domains
  • The user's intent is unclear (review? verify? research? explore?)
  • The conversation is a fresh session with no prior context
  • The artifact type is ambiguous (code? document? design? financial data?)

If the calling skill can determine scope from context, skip preflight — don't ask unnecessary questions.


Execution Instructions

Step 1: Determine Missing Signals

Preflight is typically called by a deep-* skill after the context skill has already run. When that is the case, context_report.missing_signals is passed in — use it directly and skip re-detection:

yaml
# If called with context_report.missing_signals (preferred):
missing_signals: [from context_report] # e.g. ["artifact", "intent"]
# → Ask only about these. Skip questions for signals not in the list.
# If called without context_report (standalone):
# → Run signal detection below.

Standalone signal detection (only when context_report is not available):

Analyze the conversation for these signals:

yaml
signal_check:
artifact_identified: true | false # specific files/paths/topics mentioned?
intent_clear: true | false # review? verify? research? explore?
domains_detectable: true | false # can domains be inferred from context?
scope_bounded: true | false # is the scope narrow enough to proceed?

If all four are true → skip preflight entirely, return scope_clarification directly from context.

If any are false → ask about the missing signals, one question per message.


Step 2: Ask Clarifying Questions

Rules (from superpowers brainstorming — apply here too):

  • One question at a time — never bundle questions in one message
  • Multiple choice preferred — easier to answer than open-ended
  • Maximum 3 questions total — if still unclear after 3, make reasonable assumptions and proceed
  • Stop asking when scope is clear — don't ask questions whose answers you can infer

Question priority order (ask in this order if needed):

Q1 — What to analyze (if artifact_identified: false):

"What should I analyze? For example: specific files or directories, a topic you've been working on, or something else?"

Q2 — Intent (if intent_clear: false):

"What kind of analysis are you looking for?"
Options: Review (improvement suggestions) / Audit (compliance check) / Verify (risk check) / Research (background investigation) / Explore (map and understand)

Q3 — Domain focus (if domains_detectable: false and artifact is ambiguous):

"Any particular areas to focus on?"
Options: (generate from domain-registry signals based on what's known so far)

Step 3: Synthesize and Return

After questions are answered (or if no questions were needed), produce:

yaml
scope_clarification:
artifact: "" # files, paths, topics, or description of what to analyze
intent: "" # review | audit | verify | research | explore
domains: [] # inferred domains (from domain-registry signals)
constraints: [] # any explicit constraints or focus areas mentioned
confidence: high | medium # high = all signals present, medium = some inferred
questions_asked: 0 # number of clarifying questions asked (0–3)
assumptions: [] # any assumptions made when answers were incomplete

Return this block inline in the conversation. The calling skill reads it and proceeds.


Integration with Calling Skills

Calling skills invoke preflight at their Step 1 (before spawning agents):

If context is sparse → invoke preflight → receive scope_clarification → use as input to domain-registry selection and agent prompts

Preflight does NOT:

  • Spawn review agents
  • Write output files
  • Produce verdicts
  • Replace the calling skill's own context analysis for clear conversations

Key Principles

  • Minimum viable questions — ask only what's needed to unblock the calling skill
  • Never block on perfect clarity — 3 questions max, then proceed with assumptions
  • Conversation-aware — always check the full conversation before asking; never ask what you can infer
  • No design docs, no plans — this is a clarifier, not a brainstorming session

Security

  • Security scanPassed, 95 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
6
7-day trend score
10

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 1 Apr 2026, 6 months ago.

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