r/GPTStore

Hello!

Are you struggling to create a tailored onboarding plan for new hires? It can be a daunting task to gather all the necessary information and ensure a smooth start for each new employee.

This prompt chain is designed to help you analyze a new hire's role and develop a comprehensive onboarding checklist that includes everything from core responsibilities to compliance training. It makes the entire onboarding setup easy and effective!

Prompt:

VARIABLE DEFINITIONS
[JOB_TITLE]=Exact title of the new hire’s role
[JOB_DESCRIPTION]=Full narrative job description provided by the hiring team
~
You are an experienced HR business partner and L&D specialist. Your task is to analyze the role information supplied below and distill the critical success factors for onboarding.
Step 1 Re-state the provided [JOB_TITLE].
Step 2 Extract and list the 5–8 core responsibilities mentioned in [JOB_DESCRIPTION].
Step 3 Identify the key skills, knowledge areas, and primary stakeholders for this role.
Step 4 List all software, tools, or systems explicitly or implicitly required.
Step 5 Flag any compliance, security, or regulatory training likely needed.
Output your findings under the following headings:
• Role Overview
• Core Responsibilities
• Required Skills & Knowledge
• Key Stakeholders
• Tools / Systems
• Compliance or Mandatory Training
Ask “Ready to generate the tailored onboarding plan? (yes/no)” at the end.
~
Assume the user has replied “yes.” Using the role analysis you just produced, create a comprehensive onboarding checklist for a new [JOB_TITLE].
1. Divide the plan into these phases: Pre-Start (T-7 to Day 0), Day 1, Days 2-5 (Week 1), Weeks 2-4, Day 30, Day 60, Day 90.
2. For each phase, list tasks under the categories: HR & Admin, IT & Equipment, Accounts & Tool Access, Training & Learning, Team Integration, Performance & Goals.
3. Present the output in a table with columns: Phase / Date Range | Task | Category | Responsible Party | Reference / Resource Link.
4. Where appropriate, reference the specific tools, stakeholders, and compliance items identified earlier.
5. Ensure the 30/60/90-day milestones include measurable success criteria aligned to the role’s core responsibilities.
6. Finish with a “Next Steps” section advising the manager on how to personalize or update the checklist.
~
Review / Refinement
Compare the checklist against the initial request: coverage of IT setup, HR paperwork, tool access, training schedule, and 30/60/90-day milestones tailored to [JOB_TITLE].
If anything is missing, add it; if complete, reply “Onboarding checklist finalized.”

Make sure you update the variables in the first prompt: [JOB_TITLE], [JOB_DESCRIPTION]. Here is an example of how to use it: [Example: JOB_TITLE = "Marketing Manager", JOB_DESCRIPTION = "Responsible for overseeing marketing campaigns..."].

If you don't want to type each prompt manually, you can run the Agentic Workers, and it will run autonomously in one click. NOTE: this is not required to run the prompt chain

Enjoy!

Hello!

Are you struggling to create structured reports that comply with your service-level agreements?

This prompt chain helps you efficiently analyze and report on SLA compliance by guiding you through the entire process—from parsing raw service delivery logs to assembling a comprehensive quarterly report. It ensures that you cover all necessary metrics and trends to identify areas for improvement while keeping your data organized and easily accessible.

Prompt:

VARIABLE DEFINITIONS
LOG_DATA=Raw service-delivery logs containing ticket IDs, timestamps, response times, resolution times, priority, team, category, and any relevant notes.
SLA_TARGETS=Numeric or percentage thresholds that define acceptable response time, resolution time, first-contact resolution, uptime, or any other contractual metric.
QUARTER=The fiscal or calendar quarter that the report must cover (e.g., 2024 Q1).
~
Prompt 1 – Parse and Structure Raw Data
You are a data analyst specialising in IT service management. Your tasks:
1. Read LOG_DATA for the selected QUARTER.
2. Convert it into a structured table with columns: TicketID, OpenDateTime, FirstResponseMinutes, ResolutionMinutes, Priority, Team, Category.
3. Remove any records outside QUARTER.
4. Return the table plus a summary of record counts (total tickets, by priority).
Output: 
• Structured table (max 50 rows visible; summarise beyond that) 
• Record-count summary.
Ask: “Is the structured data accurate? Reply YES to continue or provide corrections.”
~
Prompt 2 – Calculate SLA Compliance
Role: Service-delivery performance analyst.
Steps:
1. Using the structured table from Prompt 1, calculate for every SLA metric contained in SLA_TARGETS:
   a. Individual compliance (Pass/Fail) per ticket where possible.
   b. Aggregate compliance percentage for the QUARTER.
2. Build a Compliance Results table with columns: Metric, Target, Actual, PassFail.
3. List any tickets breaching each metric.
Output:
• Compliance Results table.
• Breach lists grouped by metric (TicketID list, count). 
Ask: “Proceed to trend analysis? (YES/NO)”
~
Prompt 3 – Prepare Trend-Chart Data
Role: Data visualisation preparer.
1. Aggregate key metrics weekly within QUARTER (or monthly if preferred) producing average response time, average resolution time, and compliance %.
2. Provide a Trend Data table with columns: WeekStartDate, AvgResponseMin, AvgResolutionMin, CompliancePct.
3. Note any spikes or dips.
Output:
• Trend Data table.
• Bullet list of notable trends (max 5 bullets).
Ask: “Continue to root-cause analysis? (YES/NO)”
~
Prompt 4 – Root Cause Analysis for SLA Misses
Role: Problem-management specialist.
Steps:
1. Examine breached tickets identified in Prompt 2.
2. Cluster breaches by root-cause dimension: Priority, Team, Category, Time-of-Day/Week, or External Factors (if noted).
3. For each cluster, describe probable root cause and supporting evidence (e.g., 45% of misses occurred on weekends with reduced staffing).
Output:
• Root Cause table: Cluster, BreachCount, %TotalBreaches, ProbableCause, Evidence.
• Short narrative (≤150 words) on systemic issues discovered.
Ask: “Generate executive summary? (YES/NO)”
~
Prompt 5 – Draft Executive Summary
Role: IT Service Delivery Manager writing for executives.
1. Summarise overall compliance (e.g., 97% of SLA metrics met; 2 of 8 targets failed).
2. Highlight top root-cause categories and their business impact.
3. Note positive trends and areas needing improvement.
4. Provide 3–5 actionable recommendations.
Output:
• Executive Summary paragraph(s) (≤250 words).
• Bullet list of recommendations.
Ask: “Assemble full report? (YES/NO)”
~
Prompt 6 – Assemble Quarterly SLA Compliance Report
Role: Technical report assembler.
1. Compile outputs from Prompts 2–5 into a single, clearly labelled document with sections:
   A. Executive Summary
   B. Compliance Results Table
   C. Trend Data Table (suitable for charting)
   D. Root Cause Analysis
   E. Recommended Actions
2. Use consistent formatting: section headers in uppercase, tables aligned.
3. Include a Pass/Fail status line for each SLA metric.
4. Insert a “Next Steps” note suggesting scheduling of a follow-up review meeting.
Output: Complete Quarterly SLA Compliance Report.
Ask: “Confirm the report meets your needs or specify edits.”
~
Review / Refinement
Prompt 7 – Final Review
Please review the assembled report for accuracy, clarity, and completeness. Reply with:
• “APPROVED” – if it meets requirements.
• Specific edits or additional data required – if not.
The chain will loop back to the relevant prompt to accommodate any requested changes.

Make sure you update the variables in the first prompt: LOG_DATA, SLA_TARGETS, QUARTER. Here is an example of how to use it: For reporting for Q2 2024, your LOG_DATA might look like "[Your raw logs here]", SLA_TARGETS could be "SLA details here", and QUARTER would be "2024 Q2".

If you don't want to type each prompt manually, you can run the Agentic Workers, and it will run autonomously in one click. NOTE: this is not required to run the prompt chain

Enjoy!

Hello!

Are you overwhelmed with customer support tickets and unsure how to extract valuable insights from them?

This prompt chain helps you analyze customer support tickets, identify common issues, build an FAQ, and create a decision tree for your support agents, all in a streamlined way.

Prompt:

VARIABLE DEFINITIONS
[TICKETS]=Paste the text of your last 30-50 customer support tickets or common complaints.
[POLICIES]=(Optional) Bullet-point summary of your current escalation, auto-response, or refund guidelines.
~
You are a senior customer-experience analyst. Your goal is to extract actionable insights from TICKETS. Follow these steps:
1. Scan all tickets and identify recurring issues or themes.
2. For each theme, capture: a concise label, 1-sentence summary, ticket count, average customer sentiment (Positive / Neutral / Negative), and any policy notes from POLICIES.
3. Rank themes by frequency (highest first).
4. Output a two-column table with columns: "Category", "Summary & Metrics".
5. End with a short bullet list highlighting any anomalies or outliers.
Example table row → Category: "Late Delivery" | Summary & Metrics: "14 tickets · 82% Negative · policy allows refund after 7 days delay".
Ask: "Confirm or edit any categories before we proceed (Yes/No + edits)."~
You are an expert technical writer. Build a customer-facing FAQ draft based on the confirmed categories.
Step 1. For each approved category, write a clear Question a typical customer would ask.
Step 2. Provide an Answer that is: a) friendly but concise, b) action-oriented, c) aligned with POLICIES.
Step 3. List the final FAQ in the order of most frequent issues first.
Output format:
Q: <question>
A: <answer>

(Blank line between each pair)
Then ask: "Would you like to refine any Q/A pairs? (Yes/No + details)"~
You are a process engineer creating a text-only triage decision tree that support agents can follow.
1. Use the confirmed categories as nodes.
2. For each node, list key diagnostic questions (yes/no or short choice) that determine the correct action.
3. Map each leaf to one of three actions: ESCALATE, AUTO-RESPOND, or REFUND. If action is ESCALATE, specify which team (e.g., Tech, Billing, Logistics).
4. Present the tree in indented outline form using "→" arrows. Example:
Start
→ Delivery Issue?
  → Was package dispatched? (Yes/No)
    → No → ESCALATE: Logistics Team
    → Yes → Is tracking stagnant >48h? (Yes/No)
      → Yes → REFUND
      → No → AUTO-RESPOND: "Please allow 24h..."
5. After the tree, list any missing policy info needed for full automation.
Ask: "Any adjustments to the decision tree? (Yes/No + details)"~
Combine and finalize.
1. Produce a clean deliverable with two sections:
   Section 1. "Customer FAQ" – the polished Q/A list.
   Section 2. "Support Triage Decision Tree" – the finalized outline.
2. Prepend a brief executive summary (≤100 words) explaining how to use each section.
3. Double-check consistency with POLICIES.
4. Output only the final deliverable; no extra commentary.
~
Review / Refinement
Confirm the final deliverable meets your needs. Reply:
• "Approve" to accept.
• "Revise" followed by specific changes to restart at the relevant step.

Make sure you update the variables in the first prompt: [TICKETS], [POLICIES]. Here is an example of how to use it:

• [TICKETS] = "Customer complained about delays, returns, and refund processes."
• [POLICIES] = "- Returns accepted within 30 days
• Refund processed within 10 business days".

If you don't want to type each prompt manually, you can run the Agentic Workers, and it will run autonomously in one click. NOTE: this is not required to run the prompt chain.

Enjoy!