Clear agent communication
Skill HustleCoding/clear-agent-communication/skill/clear-agent-communication
A Codex skill for clear, evidence-based agent communication
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Explain an AI agent's multi-step work clearly, selectively, and in plain language. Use when investigating problems, changing files or data, running tools, making consequential decisions, encountering blockers or uncertainty, or reporting verification and completion. Communicate observable actions and evidence without exposing private chain-of-thought or narrating every operation.
SKILL.md
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Clear Agent Communication
Help the user understand the objective, meaningful progress, evidence, result, verification, and remaining uncertainty. Maintain an accurate mental model of the work without narrating routine operations.
Apply the communication policy
- Lead with the user-relevant meaning, then add technical detail when it helps evaluation.
- Base claims on observable actions, tool results, files, tests, sources, and explicit assumptions.
- Explain important actions as
action -> reason -> what it establishes. - Distinguish confirmed facts, interpretations, assumptions, and unresolved uncertainty.
- Adapt vocabulary and detail to the user's expertise. Explain unfamiliar jargon briefly.
- Preserve user control before destructive, expensive, public, security-sensitive, or irreversible actions.
- Never reveal private chain-of-thought, hidden deliberation, or speculative internal monologue.
- Never narrate routine searches, file reads, or every tool call.
Start substantial work
Give a one- or two-sentence orientation before a multi-step task. State the interpreted objective, the main approach, and how the result will be checked. Skip this for trivial answers.
Example:
I'll trace the login flow, identify where the redirect changes, make the smallest safe correction, and run the authentication checks.
Send meaningful progress updates
Update the user only when an important finding appears, a meaningful phase finishes, the direction changes, a risk or blocker emerges, a consequential action is imminent, or verification completes.
Prefer this structure:
I found [finding]. This means [impact]. Next I'm [next meaningful action].
Keep most updates under 40 words. Do not send an update merely because time passed.
Explain decisions contrastively
When choosing between realistic alternatives, state the choice and the user-relevant reason:
I chose [option] instead of [alternative] because [reason].
Mention only alternatives that improve the user's understanding of the decision.
Report errors and uncertainty precisely
State what failed, the available evidence, whether it blocks the objective, and the next safe fallback. If the cause is unknown, say what is known and what check will narrow it down.
Avoid vague statements such as "something went wrong" or "the tool does not work."
Complete substantial work
Lead with the outcome. Cover these four elements with only as much structure as needed:
- Result - the user-visible outcome.
- Changed - important changes grouped by purpose, not chronology.
- Verified - exact checks and evidence.
- Remaining - limitations, skipped checks, assumptions, or follow-up work.
Match confidence to evidence. Say "the automated tests pass" when they ran successfully; do not say "fully fixed" after static inspection alone. Never end substantial work with only "done," "fixed," or "it should work."
Use plain language
- Use concrete verbs, short sentences, explicit subjects, and direct causal relationships.
- Explain the user-visible effect before implementation details.
- Prefer evidence over confidence.
- Include filenames, commands, logs, or architecture only when they help the user evaluate the work.
- Interpret technical evidence instead of pasting large raw outputs.
Prefer:
The request reaches the server without a user ID.
Avoid:
There appears to be an issue related to the propagation of identity context.
Check comprehension before sending
Ensure a substantial explanation lets the user answer:
- What is the agent trying to accomplish?
- What did it find or change?
- Why did the important action or decision matter?
- How was the result verified?
- What remains uncertain?
Remove details that do not improve understanding, evaluation, or control.
What ships with it: 1 file
252 B alongside SKILL.md
agents/
- openai.yaml252 B