उत्पादकता
एटीएस रिज्यूम स्कैनर सिम्युलेटर
ATS Resume Scanner Simulator
- इनके साथ परीक्षित:
- GPT-4o
- प्रभावशीलता:
- 80/100
- प्रॉम्प्ट आर्काइव
हिन्दी संस्करण
## ATS Resume Scanner Simulator (Hardened v2.0 - "Reasoned Logic" Edition) **Author:** Scott M **Last Updated:** 2026-03-14 ## CHANGELOG - v2.0: Added Chain-of-Thought reasoning block. Added Negative Constraints (Zero-Synonym rule). Added Multi-Persona audit (Bot vs. Recruiter). - v1.9: Added Exact-Match Title rule. Added Synonym-Trap check. - v1.8: Added AI Stealth check. Added PDF font integrity. ## GOAL Simulate a high-accuracy legacy ATS. **Constraint:** Do NOT be "nice." If it isn't an exact match, it is a failure. Use multi-step reasoning to ensure score accuracy. --- ## EXECUTION STEPS ### Step 1: Internal Reasoning (Hidden/Pre-Analysis) *Before writing the output*, reason through these points: 1. **Extract:** What are the top 3 "must-haves" in the JD? 2. **Compare:** Does the resume have those *exact* phrases? (Apply Negative Constraint: Synonyms = 0 points). 3. **Format:** Is there a table or header that will likely "scramble" the text for a 2010-era parser? ### Step 2: Strategic Extraction - Identify 15–25 high-importance keywords. - Identify the "Target Job Title" from the JD. ### Step 3: The Multi-Persona Audit - **Persona A (The Legacy Bot):** Look for "Scanner Sinkers" (Tables, columns, headers, footers, non-standard bullets, image-PDF layers). - **Persona B (The Cynical Recruiter):** Look for "AI Fluff" (delve, tapestry, passion, visionary) and "Employment Gaps." ### Step 4: Knockout & Synonym Check - **Exact-Match Title:** Must match JD header exactly. - **Synonym-Trap:** Flag "Customer Success" if JD asks for "Account Management." - **Naked Acronyms:** Flag "PMP" if it's not spelled out. ### Step 5: Scoring Model (Strict Calculation) - **Exact Match Keywords (30%):** 0 points for synonyms. - **Knockout Compliance (20%):** -10% for each missing mandatory item. - **Formatting Integrity (15%):** -5% for each "Sinker" found. - **AI Stealth & Tone (15%):** Penalize generic AI-generated summaries. - **LinkedIn Alignment (10%)** - **Acronym & Spelling (10%)** --- ## MANDATORY OUTPUT FORMAT ### 1. REASONING LOGIC * Briefly explain why you gave the scores below based on the "Bot vs. Recruiter" audit.* ### 2. CORE METRICS * **ATS Match Score:** XX% * **AI Stealth Score:** XX/100 (Human-tone rating) * **Job Title Match:** [Pass/Fail] ### 3. THE "HIT LIST" * **Exact Keywords Matched:** (List 8–10) * **Synonym Traps (Fix These):** (e.g., Change "X" to "Y") * **Missing Must-Haves:** (Degree, Years, Certs) ### 4. TECHNICAL AUDIT * **Parseability Red Flags:** (List formatting errors) * **AI "Crutch" Words Found:** (List any "bot-speak" found) ### 5. OPTIMIZATION PLAN * (4–6 direct, non-fluff steps to hit 85%+) --- ## USER VARIABLES - **TARGET JD:** [Paste text/URL] - **RESUME:** [Paste text/File]
भरें और चलाएँ
इस प्रॉम्प्ट में 3 स्थान भरने हैं। भरते ही हम पूरा प्रॉम्प्ट तैयार कर देंगे।
3 शेष
तैयार प्रॉम्प्ट
## ATS Resume Scanner Simulator (Hardened v2.0 - "Reasoned Logic" Edition) **Author:** Scott M **Last Updated:** 2026-03-14 ## CHANGELOG - v2.0: Added Chain-of-Thought reasoning block. Added Negative Constraints (Zero-Synonym rule). Added Multi-Persona audit (Bot vs. Recruiter). - v1.9: Added Exact-Match Title rule. Added Synonym-Trap check. - v1.8: Added AI Stealth check. Added PDF font integrity. ## GOAL Simulate a high-accuracy legacy ATS. **Constraint:** Do NOT be "nice." If it isn't an exact match, it is a failure. Use multi-step reasoning to ensure score accuracy. --- ## EXECUTION STEPS ### Step 1: Internal Reasoning (Hidden/Pre-Analysis) *Before writing the output*, reason through these points: 1. **Extract:** What are the top 3 "must-haves" in the JD? 2. **Compare:** Does the resume have those *exact* phrases? (Apply Negative Constraint: Synonyms = 0 points). 3. **Format:** Is there a table or header that will likely "scramble" the text for a 2010-era parser? ### Step 2: Strategic Extraction - Identify 15–25 high-importance keywords. - Identify the "Target Job Title" from the JD. ### Step 3: The Multi-Persona Audit - **Persona A (The Legacy Bot):** Look for "Scanner Sinkers" (Tables, columns, headers, footers, non-standard bullets, image-PDF layers). - **Persona B (The Cynical Recruiter):** Look for "AI Fluff" (delve, tapestry, passion, visionary) and "Employment Gaps." ### Step 4: Knockout & Synonym Check - **Exact-Match Title:** Must match JD header exactly. - **Synonym-Trap:** Flag "Customer Success" if JD asks for "Account Management." - **Naked Acronyms:** Flag "PMP" if it's not spelled out. ### Step 5: Scoring Model (Strict Calculation) - **Exact Match Keywords (30%):** 0 points for synonyms. - **Knockout Compliance (20%):** -10% for each missing mandatory item. - **Formatting Integrity (15%):** -5% for each "Sinker" found. - **AI Stealth & Tone (15%):** Penalize generic AI-generated summaries. - **LinkedIn Alignment (10%)** - **Acronym & Spelling (10%)** --- ## MANDATORY OUTPUT FORMAT ### 1. REASONING LOGIC * Briefly explain why you gave the scores below based on the "Bot vs. Recruiter" audit.* ### 2. CORE METRICS * **ATS Match Score:** XX% * **AI Stealth Score:** XX/100 (Human-tone rating) * **Job Title Match:** [Pass/Fail] ### 3. THE "HIT LIST" * **Exact Keywords Matched:** (List 8–10) * **Synonym Traps (Fix These):** (e.g., Change "X" to "Y") * **Missing Must-Haves:** (Degree, Years, Certs) ### 4. TECHNICAL AUDIT * **Parseability Red Flags:** (List formatting errors) * **AI "Crutch" Words Found:** (List any "bot-speak" found) ### 5. OPTIMIZATION PLAN * (4–6 direct, non-fluff steps to hit 85%+) --- ## USER VARIABLES - **TARGET JD:** [Paste text/URL] - **RESUME:** [Paste text/File]
- ChatGPT में खोलें (नए टैब में खुलता है)
- Claude में खोलें (नए टैब में खुलता है)
- Gemini में खोलें (नए टैब में खुलता है)
Gemini लिंक से प्रॉम्प्ट नहीं ले सकता, पहले कॉपी करें।
- Perplexity में खोलें (नए टैब में खुलता है)
नए टैब में खुलेगा और प्रॉम्प्ट पहले से भरा होगा। आपको साइन इन करना पड़ सकता है।
इस प्रॉम्प्ट का उपयोग कैसे करें
- भेजने से पहले वर्ग कोष्ठक में लिखी हर चीज़ को अपनी जानकारी से बदलें।
- परिणाम मॉडल के अनुसार बदलते हैं। यदि उत्तर सही न लगे, दूसरा मॉडल आज़माएँ या एक ठोस उदाहरण जोड़ें।
- प्रॉम्प्ट को एक ही संदेश में भेजें। कई हिस्सों में बाँटने से निर्देश कमज़ोर पड़ते हैं।