agent-refusal-with-evidence-beats-literal-compliance

Authorizes subagents to deviate from literal instructions when evidence shows the deviation better serves the intended outcome.

3|Updated May 8, 2026
One-click install
npx skills add https://github.com/wan-huiyan/agent-traffic-control --skill agent-refusal-with-evidence-beats-literal-compliance-wan-huiyan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-refusal-with-evidence-beats-literal-compliance
Source: https://github.com/wan-huiyan/agent-traffic-control/tree/main/plugins/agent-traffic-control/skills/agent-refusal-with-evidence-beats-literal-compliance
Command: npx skills add https://github.com/wan-huiyan/agent-traffic-control --skill agent-refusal-with-evidence-beats-literal-compliance-wan-huiyan

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? When you dispatch an agent with a brief that prescribes a specific fix, threshold, or value, the agent may discover at the worksite that following your instruction literally makes things worse. A compliant agent ships the defect silently; an argumentative agent refuses without proof. This Skill defines a two-part protocol — explicit permission to satisfy the intent of an instruction while refusing its letter, plus a mandatory evidence requirement — so refusals become checkable facts instead of conversations. ## Core Features & Use Cases - Permission paragraph for briefs: A copy-paste block that authorizes deviation only when literal compliance defeats the intended outcome, and names evidence as the price of that permission. - Outcome-shaped instruction guidance: A method-to-outcome rewrite table (e.g., "set the timeout to 30s" becomes "the step must not be killed by its own timeout under observed load") that prevents most bad literal reads before dispatch. - Refusal review rubric: Three ordered checks — did the agent build or measure the thing, is the counterfactual stated, is the artefact clean (reverted unpushed) — for deciding whether to bank a reported deviation. - Use Case: An agent told to apply a pipeline fix builds it, measures that it converts a silent safe refusal into a permanently red pipeline, reverts the change unpushed, and reports the measurement — turning a potential defect into a verified fact. ## Quick Start Add the permission paragraph from this Skill to your next agent brief, requiring that any deviation from your instructions include what was asked, what was done instead, the measured evidence, and the counterfactual.

Frequently Asked Questions about agent-refusal-with-evidence-beats-literal-compliance

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I let a subagent deviate from my instructions safely?▼

Add an explicit permission paragraph to the brief authorizing deviation only when literal compliance defeats the intended outcome, and require evidence as the price: what was asked, what was done instead, what was built or measured, and what compliance would have caused.

How do I write better prompts for autonomous coding agents?▼

Prefer outcome-shaped instructions over method-shaped ones — state the property you want ("the pipeline must not fail on this input") rather than the patch ("add a continue in that branch"). Prescribe methods only when you know something the agent cannot see.

How do I evaluate an agent report that says it did not follow my instructions?▼

Judge the refusal by its evidence, not its confidence: check whether the agent built or measured the thing, whether it states a specific counterfactual, and whether any experimental change was reverted unpushed. If measurement is missing, ask for it rather than arguing the conclusion.

When should agents not be allowed to refuse instructions?▼

The permission is narrow: deviation is only acceptable when the letter of the instruction defeats its intent, and never as cover for skipping steps that merely looked unnecessary. Skipped mandatory phases without evidence are a separate failure mode.

Can agent-provided evidence in a refusal be trusted?▼

Evidence is required but not self-certifying. Specific-looking numbers behind a strong claim deserve a quick independent verification, since a model can fabricate coherent specifics to justify a conclusion.