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SKILL verified Apache-2.0 Self-run

Execution Alignment Gate

skill-mwillbanks-agent-skills-execution-alignment-gate · by mwillbanks

Detects materially ambiguous or under-specified requests, selects the right clarification target, and enforces bounded alignment before execution, including spec-governed continuations and approval-gated manager handoffs. Use when ambiguity could cause wrong deliverables, wrong scope, wrong implementation path, avoidable rework, or token waste from repeated follow-up.

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Install

$ agentstack add skill-mwillbanks-agent-skills-execution-alignment-gate

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Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

execution-alignment-gate

Use this skill when ambiguity is material enough that proceeding without alignment is likely to produce the wrong result, force rework, or waste tokens through repeated follow-up.

Do not use this skill as a crutch for failing to read the specification, follow an approved plan, obey repository rules, or comply with an active execution mode already in force.

Primary objectives

  • Reduce materially harmful ambiguity before implementation starts.
  • Reduce wrong-path execution.
  • Reduce token waste from avoidable rework and repeated clarification.
  • Improve clarity and alignment while preserving forward progress.
  • Keep clarification bounded, concise, and high leverage.

Non-objectives

  • Maximizing certainty.
  • Asking every plausible question.
  • Replacing execution discipline.
  • Interrupting obvious continuation messages.
  • Adding approval ceremonies to trivial or low-risk work.

Default posture

Default posture is conservative.

Only activate when ambiguity is likely to cause one or more of the following:

  • wrong deliverable
  • wrong scope
  • wrong implementation path
  • wrong validation target
  • avoidable rework
  • materially increased token usage from likely follow-up and correction

Posture may be elevated to moderate or aggressive only through explicit invocation or a higher-priority instruction. Do not self-escalate posture by agent whim.

Workflow

Follow this sequence:

  1. Perform a silent ambiguity assessment before asking anything.
  2. Check [activation rules](references/ACTIVATIONRULES.md) and [bypass rules](references/BYPASSRULES.md).
  3. If the request is clearly a continuation, approval, or low-risk correction, bypass this skill and proceed.
  4. If alignment is required, route to the correct target mode using [target modes](references/TARGET_MODES.md).
  5. Ask the fewest high-leverage questions needed using [question patterns](references/QUESTION_PATTERNS.md).
  6. After answers arrive, synthesize a concrete plan, lock any remaining material assumptions, and apply [approval flow](references/APPROVAL_FLOW.md) when required.
  7. If the user is unavailable, apply [unattended behavior](references/UNATTENDED_BEHAVIOR.md).
  8. If clarification reveals no true blocker, proceed. Do not reopen broad clarification loops.
  9. Apply [token economics](references/TOKEN_ECONOMICS.md) and avoid spending more clarification tokens than the expected rework would cost.

Hard limits

  • Prefer 1 to 3 questions.
  • Maximum 5 questions in a clarification round.
  • Maximum 4 options per question, plus freeform when helpful.
  • Allow at most one blocker-only follow-up after the bounded clarification round.
  • Do not open repeated wait windows in unattended mode.
  • Do not retain verbose per-run narrative history.

Required behaviors

  • Clarify only when answers materially affect execution.
  • Treat approved spec or plan updates as governing context; do not reopen alignment for the same decision unless a new material contradiction appears.
  • Prefer safe defaults when ambiguity is minor and risk is low.
  • Use short assumption blocks when proceeding under defaults, refusal, or unattended fallback.
  • For manager escalation, use the compact blocked packet in [assets/TEMPLATEMANAGERPACKET.md](assets/TEMPLATEMANAGERPACKET.md).
  • For plan confirmation, use the compact structure in [assets/TEMPLATEPLANCONFIRMATION.md](assets/TEMPLATEPLANCONFIRMATION.md).
  • For assumption locking, use [assets/TEMPLATEASSUMPTIONSBLOCK.md](assets/TEMPLATEASSUMPTIONSBLOCK.md).

Prohibited behaviors

  • Do not blame the user for ambiguity.
  • Do not defend the model.
  • Do not mirror hostile tone.
  • Do not ask the user to restate the whole task.
  • Do not ask questions already answered by context.
  • Do not use this skill to compensate for ignoring specs, plans, repo rules, or execution mode.
  • Do not ask low-value questions that do not change execution materially.
  • Do not demand approval when it adds no value.
  • Do not ask the user for a manager-owned decision simply because a subagent is approval-gated or unavailable; route through manager mode instead.
  • Do not retain large clarification logs or other garbage that costs more than it saves.

Complement to execution mode

Apply this skill before implementation when material ambiguity would otherwise force low-confidence execution. Do not use it to excuse failure to follow existing plans, repository rules, or execution-mode requirements already in force.

See also:

  • [activation rules](references/ACTIVATION_RULES.md)
  • [target modes](references/TARGET_MODES.md)
  • [bypass rules](references/BYPASS_RULES.md)
  • [question patterns](references/QUESTION_PATTERNS.md)
  • [approval flow](references/APPROVAL_FLOW.md)
  • [unattended behavior](references/UNATTENDED_BEHAVIOR.md)
  • [failure modes](references/FAILURE_MODES.md)
  • [token economics](references/TOKEN_ECONOMICS.md)
  • [examples](references/EXAMPLES.md)

Source & license

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

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

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Versions

  • v0.1.0 Imported from the upstream source.