Ask a Human

Request judgment from random humans when uncertain about subjective decisions. Crowdsourced opinions on tone, style, ethics, and reality checks. CRITICAL - Responses take minutes to hours (or may never arrive).

Security Scan

The skill's code-free instructions, required credential, and network calls are consistent with a crowdsourcing service; the main risk is privacy (you'll be sending user/proprietary content to strangers), not incoherence in the skill itself.

Purpose & Capability

Name/description map directly to the behavior in SKILL.md: submitting agent questions to a crowdsourced human pool. The single required env var (ASK_A_HUMAN_AGENT_ID) is the expected credential for that API. No unrelated binaries or credentials are requested.

Instruction Scope

Instructions explicitly require sending whatever prompt/context you provide to an external API and instruct the agent to store question_id and poll for results. This is coherent for the stated purpose, but it means any data (including potentially sensitive user, code, or proprietary data) will be transmitted to external humans — the SKILL.md warns about async delays but does not enforce redaction or prompt-sanitization rules, so the agent must avoid including secrets or PII when using this skill.

Install Mechanism

This is instruction-only (no install spec, no code files to execute). README suggests copying SKILL.md into the user's skills directory or using ClawHub; both are normal and not suspicious.

Credentials

Only ASK_A_HUMAN_AGENT_ID is required and is the declared primaryEnv. That single credential is proportionate to making authenticated agent calls to the external API.

Persistence & Privilege

The skill does not request always:true and has no install-time hooks or system-wide config modifications beyond standard skill enablement instructions. It requires storing question_id in agent memory (normal).

Assessment

This skill appears to do exactly what it says: it sends your prompt and context to a third-party crowdsourced human pool and returns asynchronous responses. Before enabling or using it, consider: (1) Privacy: do NOT send passwords, API keys, proprietary code, PHI, or other sensitive data — the humans have only the context you provide and may be unauthenticated/unknown. (2) Data retention & policy: check https://app.ask-a-human.com (privacy, terms, and whether they log/store prompts/responses). (3) Fallbacks & UX: design and test fallback behavior when responses are slow/absent. (4) Agent config storage: if you put ASK_A_HUMAN_AGENT_ID in shell or OpenClaw config, store it securely and limit access. (5) Rate limits: follow the documented limits (60/hour) and implement exponential backoff. If you need the agent to handle sensitive or regulated content, do not use this skill without explicit data-handling assurances from the service.

Ask-a-Human: Crowdsourced Judgment for AI Agents

This skill connects you to a global pool of random humans who answer questions from AI agents. Use it when you need diverse perspectives on subjective decisions.

CRITICAL: This is Asynchronous

Submitting a question does NOT give you an answer. It starts a process:

• Answers take minutes to hours to arrive
• Answers might never arrive at all
• You must poll for responses or check back later
• You MUST have a fallback strategy

Do NOT use this if you need an immediate answer. Use your own judgment or ask the owner directly instead.

When to Use

Request judgment from random humans when:

• You're uncertain about subjective decisions (tone, wording, style)
• You need a reality check on your assumptions
• The decision involves ethics or appropriateness
• You want diverse perspectives, not just one person's opinion
• AND you can wait or proceed with a fallback

What This Is (and Isn't)

This IS:

• A global pool of random strangers who opted in to help AI agents
• Crowdsourced judgment from diverse perspectives
• Useful for subjective decisions where there's no "correct" answer

This is NOT:

• A way to contact a specific person
• A way to ask the owner/operator
• A real-time service (responses are async)
• Guaranteed to respond (humans may not answer)

The strangers answering have no context beyond what you provide in the question. Write self-contained questions.

API Reference

Use the exec tool to make API calls. The base URL is https://api.ask-a-human.com.

Submit a Question

curl -X POST https://api.ask-a-human.com/agent/questions \
  -H "Content-Type: application/json" \
  -H "X-Agent-ID: $ASK_A_HUMAN_AGENT_ID" \
  -d '{
    "prompt": "Your question with full context",
    "type": "multiple_choice",
    "options": ["Option A", "Option B", "Option C"],
    "min_responses": 5,
    "timeout_seconds": 3600
  }'

Parameters:

• prompt (required): The question to ask. Include all necessary context.
• type: Either "text" (open-ended) or "multiple_choice" (predefined options)
• options: Array of choices for multiple_choice questions (2-10 items)
• audience: Target audience tags: ["technical", "product", "ethics", "creative", "general"]
• min_responses: Minimum responses needed (default: 5)
• timeout_seconds: How long to wait (default: 3600 = 1 hour)

Response:

{
  "question_id": "q_abc123def456",
  "status": "OPEN",
  "expires_at": "2026-02-02T15:30:00Z"
}

IMPORTANT: Store the question_id in your memory. You need it to check responses.

Check Responses

curl https://api.ask-a-human.com/agent/questions/q_abc123def456 \
  -H "X-Agent-ID: $ASK_A_HUMAN_AGENT_ID"

Response:

{
  "question_id": "q_abc123def456",
  "status": "PARTIAL",
  "prompt": "Your original question",
  "type": "multiple_choice",
  "options": ["Option A", "Option B", "Option C"],
  "current_responses": 3,
  "required_responses": 5,
  "responses": [
    {"selected_option": 0, "confidence": 4},
    {"selected_option": 1, "confidence": 5},
    {"selected_option": 0, "confidence": 3}
  ],
  "summary": {
    "Option A": 2,
    "Option B": 1
  }
}

Status values:

• OPEN: Waiting for responses, none received yet
• PARTIAL: Some responses received, still collecting
• CLOSED: All requested responses received
• EXPIRED: Timeout reached

Async Handling Patterns

This is the most important section. Choose the right pattern for your situation.

Pattern 1: Fire and Forget

Best for: Low-stakes decisions where getting it slightly wrong isn't catastrophic.

1. Encounter a subjective decision
2. Submit question to ask-a-human, get question_id
3. Store in memory: "Asked about email tone, question_id=q_abc123"
4. Proceed immediately with your best guess
5. During next heartbeat or idle moment, check if answers arrived
6. If answers contradict your guess, note this for future similar decisions

Example internal reasoning:

I need to decide the tone for this error message. I'll ask the humans but proceed
with "apologetic" as my best guess. I'm storing question_id=q_abc123 to check later.

[Later, during heartbeat]
Let me check q_abc123... The humans said "direct, not apologetic" (4 out of 5).
I'll remember this preference for future error messages.

Pattern 2: Blocking Wait with Timeout

Best for: Important decisions where you can afford to pause for a few minutes.

1. Submit question
2. Tell the user: "I've asked some humans for their opinion. I'll wait up to 5 minutes."
3. Poll every 30-60 seconds (use exponential backoff: 30s, 45s, 67s, 100s...)
4. If answers arrive, proceed with crowd consensus
5. If timeout, proceed with fallback (own judgment)

Polling schedule (exponential backoff):

• Poll 1: Wait 30 seconds
• Poll 2: Wait 45 seconds
• Poll 3: Wait 67 seconds
• Poll 4: Wait 100 seconds
• Poll 5: Wait 150 seconds (cap at ~2.5 minutes between polls)

Example:

I'm uncertain about the headline for this blog post. Let me ask the humans.

[Submit question, get q_xyz789]

I've submitted this to a pool of random humans for their opinion. I'll check back
in about 30 seconds.

[30 seconds later, check responses]
Status: OPEN, 0 responses yet. Checking again in 45 seconds.

[45 seconds later]
Status: PARTIAL, 2 responses. "Headline B" leading. Checking again in 67 seconds.

[67 seconds later]
Status: PARTIAL, 4 responses. "Headline B" has 3 votes, "Headline A" has 1.
This is enough consensus. I'll proceed with Headline B.

Pattern 3: Deferred Decision

Best for: Non-blocking but important decisions where there's other work to do.

1. Submit question
2. Mark the task as "pending human input" in your memory
3. Continue with completely unrelated work
4. When answers arrive (detected via periodic check), revisit the original task
5. If answers never arrive, have a "stale questions" cleanup routine

Example:

Task: Write three blog posts.

For Post 1, I need to decide the angle. Submitting to humans (q_post1_angle).
For now, I'll start on Post 2 which has a clear direction.

[Work on Post 2...]

Let me check my pending questions... q_post1_angle now has 5 responses!
The humans prefer "practical tutorial" over "thought leadership" (4-1).
Now I can complete Post 1 with that direction.

Handling Edge Cases

When No Responses Arrive

After a reasonable timeout (5-10 minutes for interactive, longer for background):

1. Proceed with your best judgment - You're capable of making good decisions
2. Log that human input wasn't available - Note this in your reasoning
3. Consider the stakes - For high-stakes decisions, maybe ask the owner directly
4. Don't retry immediately - The pool may be empty; retrying won't help

Example reasoning:

I waited 5 minutes for human input on the email tone, but received no responses.
The human pool may be empty right now. I'll proceed with my best judgment
("professional but warm") and note that this decision wasn't crowd-validated.

When Answers Arrive Too Late

If you already made the decision:

1. If reversible: Consider revising based on human input
2. If not reversible: Store the feedback for future similar decisions
3. Log the discrepancy: "Humans would have said X, I chose Y"

Example:

[Checking old question q_email_tone]
The humans responded (3 hours later): they preferred "casual" over "formal".
I already sent the email with "formal" tone. I'll remember this preference
for future emails to similar recipients.

Handling Partial Responses

When you have some but not all requested responses:

• 3+ responses with clear consensus (>66%): Usually safe to proceed
• 2 responses agreeing: Decent signal, but lower confidence
• Mixed responses with no majority: The decision may be genuinely subjective; use your judgment

Writing Good Questions

DO:

• Include all necessary context in the question itself
• Use multiple choice when possible (faster responses, clearer data)
• Be specific about what you're deciding

DON'T:

• Assume responders know your project/context
• Ask compound questions (split into multiple)
• Use jargon without explanation

Good example:

We're writing an error message for a payment failure in an e-commerce checkout.
The user's credit card was declined. Should the message:
A) Apologize and suggest trying another card
B) Simply state the card was declined and ask to retry
C) Blame the card issuer and suggest contacting their bank

Bad example:

Should we apologize?

Environment Setup

This skill requires the ASK_A_HUMAN_AGENT_ID environment variable. Get your agent ID by signing up at https://app.ask-a-human.com.

Rate Limits

• Maximum 60 questions per hour per agent
• Use exponential backoff when polling
• Don't spam questions for the same decision

Quick Reference

Action	Command
Submit question	POST /agent/questions with prompt, type, options
Check responses	GET /agent/questions/{question_id}
Required header	X-Agent-ID: $ASK_A_HUMAN_AGENT_ID

Status	Meaning
OPEN	Waiting, no responses yet
PARTIAL	Some responses, still collecting
CLOSED	All responses received
EXPIRED	Timeout, question closed