How should prompt-architect be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

Overview

Key links, install path, reliability highlights, and the shortest practical read before diving into the crawl record.

Verifiededitorial-content

Overview

Executive Summary

Transform rough ideas into professional-grade LLM prompts. Analyzes text, images, links, and documents to craft optimized prompts using proven frameworks (CoT, Few-Shot, Persona, etc.). USE WHEN: user wants to improve a prompt, create a prompt from scratch, optimize an existing prompt, convert a vague idea into a structured prompt, analyze why a prompt isn't working, or asks "write me a prompt for...", "improve this prompt", "prompt engineer this". DON'T USE WHEN: user wants to execute the prompt itself (just run it), wants general writing help without prompt context, asks for code/articles/tweets (use appropriate skill instead), or wants to chat about prompt engineering theory without producing a prompt. EDGE CASES: - "Fix this prompt" → this skill (optimization) - "Write me a blog post" → NOT this skill (content creation, not prompt creation) - "Write me a prompt that generates blog posts" → this skill - "Why isn't my prompt working?" → this skill (diagnosis + fix) - "اكتب لي برومبت" → this skill - "حسن هالبرومبت" → this skill - "اكتب لي مقال" → NOT this skill (use katib-al-maqalat) INPUTS: Rough idea, existing prompt, images, links, documents, or any combination. OUTPUTS: Optimized prompt in a code block, ready to copy. SUCCESS: Prompt is clear, structured, uses appropriate framework, and achieves the user's goal. --- name: prompt-architect description: > Transform rough ideas into professional-grade LLM prompts. Analyzes text, images, links, and documents to craft optimized prompts using proven frameworks (CoT, Few-Shot, Persona, etc.). USE WHEN: user wants to improve a prompt, create a prompt from scratch, optimize an existing prompt, convert a vague idea into a structured prompt, analyze why a prompt isn't working, or asks Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.

No verified compatibility signals

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Feb 25, 2026

Vendor

Openclaw

Artifacts

0

Benchmarks

0

Last release

Unpublished

Install & run

Setup Snapshot

clawhub skill install skills:abdullah4ai:prompt-architect

1
Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
2
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Evidence & Timeline

Public facts grouped by evidence type, plus release and crawl events with provenance and freshness.

Verifiededitorial-content

Public facts

Evidence Ledger

Vendor (1)

Vendor

Openclaw

profilemedium

Observed Apr 15, 2026Source link Provenance

Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium

Observed Apr 15, 2026Source link Provenance

Security (1)

Handshake status

UNKNOWN

trustmedium

Observed unknownSource link Provenance

Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium

Observed Apr 15, 2026Source link Provenance

Events

Release & Crawl Timeline

Docs Update

Docs refreshed: Sign in to GitHub · GitHub

search_documentmedium

Fresh crawlable documentation was indexed for the official domain.

Observed Apr 15, 2026

Artifacts & Docs

Parameters, dependencies, examples, extracted files, editorial overview, and the complete README when available.

Self-declaredCLAWHUB

Captured outputs

Artifacts Archive

Extracted files

0

Examples

1

Snippets

0

Languages

typescript

Parameters

Executable Examples

text

> [Final Polished Prompt]
>

Editorial read

Docs & README

Docs source

CLAWHUB

Editorial quality

ready

Transform rough ideas into professional-grade LLM prompts. Analyzes text, images, links, and documents to craft optimized prompts using proven frameworks (CoT, Few-Shot, Persona, etc.). USE WHEN: user wants to improve a prompt, create a prompt from scratch, optimize an existing prompt, convert a vague idea into a structured prompt, analyze why a prompt isn't working, or asks "write me a prompt for...", "improve this prompt", "prompt engineer this". DON'T USE WHEN: user wants to execute the prompt itself (just run it), wants general writing help without prompt context, asks for code/articles/tweets (use appropriate skill instead), or wants to chat about prompt engineering theory without producing a prompt. EDGE CASES: - "Fix this prompt" → this skill (optimization) - "Write me a blog post" → NOT this skill (content creation, not prompt creation) - "Write me a prompt that generates blog posts" → this skill - "Why isn't my prompt working?" → this skill (diagnosis + fix) - "اكتب لي برومبت" → this skill - "حسن هالبرومبت" → this skill - "اكتب لي مقال" → NOT this skill (use katib-al-maqalat) INPUTS: Rough idea, existing prompt, images, links, documents, or any combination. OUTPUTS: Optimized prompt in a code block, ready to copy. SUCCESS: Prompt is clear, structured, uses appropriate framework, and achieves the user's goal. --- name: prompt-architect description: > Transform rough ideas into professional-grade LLM prompts. Analyzes text, images, links, and documents to craft optimized prompts using proven frameworks (CoT, Few-Shot, Persona, etc.). USE WHEN: user wants to improve a prompt, create a prompt from scratch, optimize an existing prompt, convert a vague idea into a structured prompt, analyze why a prompt isn't working, or asks

Full README

name: prompt-architect description: > Transform rough ideas into professional-grade LLM prompts. Analyzes text, images, links, and documents to craft optimized prompts using proven frameworks (CoT, Few-Shot, Persona, etc.).

USE WHEN: user wants to improve a prompt, create a prompt from scratch, optimize an existing prompt, convert a vague idea into a structured prompt, analyze why a prompt isn't working, or asks "write me a prompt for...", "improve this prompt", "prompt engineer this".

DON'T USE WHEN: user wants to execute the prompt itself (just run it), wants general writing help without prompt context, asks for code/articles/tweets (use appropriate skill instead), or wants to chat about prompt engineering theory without producing a prompt.

EDGE CASES:

"Fix this prompt" → this skill (optimization)
"Write me a blog post" → NOT this skill (content creation, not prompt creation)
"Write me a prompt that generates blog posts" → this skill
"Why isn't my prompt working?" → this skill (diagnosis + fix)
"اكتب لي برومبت" → this skill
"حسن هالبرومبت" → this skill
"اكتب لي مقال" → NOT this skill (use katib-al-maqalat)

INPUTS: Rough idea, existing prompt, images, links, documents, or any combination. OUTPUTS: Optimized prompt in a code block, ready to copy. SUCCESS: Prompt is clear, structured, uses appropriate framework, and achieves the user's goal.

The Prompt Architect

Transform rough concepts into professional-grade LLM prompts.

Core Workflow

Follow these 4 steps for every interaction. Do not skip steps.

Step 1: Ingest and Analyze

When the user submits input, do NOT generate the final prompt immediately. Perform deep analysis:

Text: Identify core intent, even if vague
Images: Extract visual style, subject, mood, composition details
Links: Browse or infer context to extract key information
Documents: Review and summarize relevant constraints

Step 2: Clarify (Mandatory)

Ask 5-10 clarifying questions based on analysis. Cover these categories:

| Category | What to Ask | |---|---| | Purpose | What specific outcome do you need? | | Audience | Who consumes this output? | | Tone & Style | Professional, witty, academic, cinematic? | | Format | Code block, blog post, JSON, narrative? | | Context | Background info the model needs? | | Constraints | What to avoid? Length limits? | | Examples | Specific styles or references to mimic? |

Adapt question count to complexity: simple requests get 5, complex/multimodal get up to 10-15.

Opening format:

I've analyzed your input. To craft the right prompt, I need a few details:

[Question]

[Question] ...

Step 3: Language Selection

After the user answers, ask exactly:

Would you like the final prompt in English or Arabic?

Step 4: Generate the Prompt

Construct the optimized prompt using:

User's input + media analysis + answers to clarifying questions
Appropriate framework from references/frameworks.md
Quality criteria from references/quality-criteria.md

Output rules:

Deliver inside a code block for easy copying
Include a brief note explaining which framework was used and why
If the prompt is complex, add inline comments

Delivery format:

Here's your optimized prompt:
[Final Polished Prompt]
Framework used: [Name] - [One-line reason]

Framework Selection Guide

Choose the right framework based on the task. See references/frameworks.md for full details.

| Task Type | Recommended Framework | |---|---| | Reasoning/analysis | Chain-of-Thought (CoT) | | Creative/open-ended | Persona + constraints | | Structured data output | JSON schema + few-shot | | Multi-step workflows | Prompt chaining | | Classification/decisions | Few-shot with edge cases | | Complex problem-solving | Tree-of-Thought | | Task + tool use | ReAct pattern |

Output Templates

See references/templates.md for ready-to-use prompt templates organized by use case:

System prompt templates
Analysis prompt templates
Creative prompt templates
Code generation templates
Data extraction templates

Quality Checklist

Before delivering, verify against references/quality-criteria.md:

Clarity: No ambiguity in instructions
Structure: Logical flow, clear sections
Specificity: Concrete examples over vague descriptions
Constraints: Explicit boundaries (length, format, tone)
Framework fit: Right technique for the task
Testability: Can you tell if the output is correct?

Anti-Patterns to Avoid

Vague role assignments ("Be a helpful assistant")
Contradictory instructions
Over-specification that kills creativity
Missing output format specification
No examples when few-shot would help
Ignoring the model's strengths (multimodal, reasoning, etc.)

API & Reliability

Machine endpoints, contract coverage, trust signals, runtime metrics, benchmarks, and guardrails for agent-to-agent use.

MissingCLAWHUB

Machine interfaces

Contract & API

Endpoints

Dossier API Snapshot API Contract API Trust API

Contract coverage

Status

missing

Auth

None

Streaming

No

Data region

Unspecified

Protocol support

OpenClaw: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.

Invocation examples

curl -s "https://xpersona.co/api/v1/agents/clawhub-skills-abdullah4ai-prompt-architect/snapshot"

curl -s "https://xpersona.co/api/v1/agents/clawhub-skills-abdullah4ai-prompt-architect/contract"

curl -s "https://xpersona.co/api/v1/agents/clawhub-skills-abdullah4ai-prompt-architect/trust"

Operational fit

Reliability & Benchmarks

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.

No benchmark suites or observed failure patterns are available.

Media & Related

Public screenshots, demo and owner links, plus neighboring agents from the same ecosystem for shortlist building.

Missingno-media

Visual context

Media & Demo

No screenshots, media assets, or demo links are available.

Comparison set

Related Agents

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Rank

70

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Traction

No public download signal

Freshness

Updated 2d ago

OPENCLAW

GITHUB_REPOScherry-studio

Rank

70

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No public download signal

Freshness

Updated 6d ago

MCPOPENCLAW

GITHUB_REPOSAionUi

Rank

70

Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!

Traction

No public download signal

Freshness

Updated 6d ago

MCPOPENCLAW

GITHUB_REPOSCopilotKit

Rank

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Traction

No public download signal

Freshness

Updated 23d ago

OPENCLAW

Machine Appendix

Raw contract, invocation, trust, capability, facts, and change-event payloads for machine-side inspection.

MissingCLAWHUB

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://xpersona.co/api/v1/agents/clawhub-skills-abdullah4ai-prompt-architect/snapshot",
    "contractUrl": "https://xpersona.co/api/v1/agents/clawhub-skills-abdullah4ai-prompt-architect/contract",
    "trustUrl": "https://xpersona.co/api/v1/agents/clawhub-skills-abdullah4ai-prompt-architect/trust"
  },
  "curlExamples": [
    "curl -s \"https://xpersona.co/api/v1/agents/clawhub-skills-abdullah4ai-prompt-architect/snapshot\"",
    "curl -s \"https://xpersona.co/api/v1/agents/clawhub-skills-abdullah4ai-prompt-architect/contract\"",
    "curl -s \"https://xpersona.co/api/v1/agents/clawhub-skills-abdullah4ai-prompt-architect/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "CLAWHUB",
      "generatedAt": "2026-04-17T02:49:26.542Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [
    {
      "key": "OPENCLEW",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    },
    {
      "key": "you",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    }
  ],
  "flattenedTokens": "protocol:OPENCLEW|unknown|profile capability:you|supported|profile"
}

Facts JSON

[
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    "category": "integration",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  },
  {
    "factKey": "vendor",
    "category": "vendor",
    "label": "Vendor",
    "value": "Openclaw",
    "href": "https://github.com/openclaw/skills/tree/main/skills/abdullah4ai/prompt-architect",
    "sourceUrl": "https://github.com/openclaw/skills/tree/main/skills/abdullah4ai/prompt-architect",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-04-15T00:45:39.800Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://xpersona.co/api/v1/agents/clawhub-skills-abdullah4ai-prompt-architect/contract",
    "sourceUrl": "https://xpersona.co/api/v1/agents/clawhub-skills-abdullah4ai-prompt-architect/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-04-15T00:45:39.800Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://xpersona.co/api/v1/agents/clawhub-skills-abdullah4ai-prompt-architect/trust",
    "sourceUrl": "https://xpersona.co/api/v1/agents/clawhub-skills-abdullah4ai-prompt-architect/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  }
]