interaction-discipline

Structures agent interaction messages around verified state, blockers, and bounded user actions.

1|Updated Sep 11, 2026
One-click install
npx skills add https://github.com/rebuildup/my-web-2026 --skill interaction-discipline-rebuildup
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: interaction-discipline
Source: https://github.com/rebuildup/my-web-2026/tree/main/skills/interaction-discipline
Command: npx skills add https://github.com/rebuildup/my-web-2026 --skill interaction-discipline-rebuildup

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Active work sessions often degrade into streams of agent process narration, vague error reports, and unnecessary questions that push agent-owned work back onto the user. This Skill enforces a discipline where every user-facing message leads with actionable state, keeps agent-executable work agent-owned, and reports errors with evidence and a recovery path. ## Core Features & Use Cases - Actionable message structure: Leads each turn with completed results, current blockers, or decisions needed, using a Done/Now/Next state shape instead of process logs. - Bounded user actions and questions: Decomposes required user steps into concrete ordered actions and asks only consequential questions that project evidence cannot resolve. - Evidence-based error and completion reporting: Reports failures with symptom, evidence, cause, recovery, and verification criteria, and ties completion claims to test, build, or read-back evidence. - Use Case: During a multi-turn debugging session, the agent reports a failing validation gate with the exact error output and confirmed cause, applies the fix itself, and reports the green test run—rather than narrating each tool call or asking the user to run commands. ## Quick Start Apply the interaction-discipline skill to keep all progress updates, blocker reports, and completion messages in this session focused on verified state and concrete next actions.

Frequently Asked Questions about interaction-discipline

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

FAQPage Schema
How do I make AI agent progress updates more actionable?

Lead each message with the most actionable information: completed results, current blockers, or decisions needed. Use a Done/Now/Next structure to restate only the state the user needs for their next judgment, and avoid narrating tool sequences or internal reasoning.

When should an AI agent ask the user questions during a task?

Ask only when the decision is genuinely user-owned: user-only credentials or physical actions, approval for destructive or costly operations, product semantics that project evidence cannot determine, or unresolvable external dependencies. State what is undecided, why the agent cannot decide it, any recommended default, and what proceeds after the answer.

How should an AI agent report errors during a task?

Report errors as operational state with five elements: symptom (what failed where), evidence (the actual error or failing check), cause (confirmed only, with hypotheses labeled), recovery (the fix or next diagnostic), and verification (what green state confirms resolution). Avoid vague statements like "something went wrong."

What is the difference between interaction-discipline and writing-discipline?

Interaction-discipline governs ephemeral user-facing messages during active work, such as status updates and blocker reports. Writing-discipline governs persistent artifacts like READMEs, ADRs, issues, pull requests, and commit messages, using a Select-Compose-Reread process.

Does this interaction discipline force all agent messages to be short?

No. The discipline explicitly states it is not a brevity skill. When the user requests explanations, comparisons, walkthroughs, audits, or complete lists, full technical detail is provided. Only filler, duplicated state, process narration, tangents, and avoidable delegation are removed.