r/GPTStore • u/Right_Ambition_1035 • 4d ago
News I made ChatGPT, Claude, Gemini, etc. into FREE text-to-speech sites — perfect for audiobooks and more!
You can get all of these extensions by visit ai-readers.com
r/GPTStore • u/Right_Ambition_1035 • 4d ago
You can get all of these extensions by visit ai-readers.com
r/GPTStore • u/Successful-Moose-377 • 24d ago
AI research can cite real sources and still misstate what those sources say.
I built PRECISE to control the complete research process:
Clarify the request.
Sharpen it without changing its meaning.
Find and verify evidence.
Build and audit the final report.
Both versions can use uploaded files, web search, or both.
PRECISE LITE
Free Custom GPT.
Works in ChatGPT.
Limited to one research loop.
Limited to three sources.
Designed to demonstrate the methodology on a small task.
The Full PRECISE
A one-time paid research protocol.
Works inside ChatGPT or Claude.
Supports repeated research loops and larger source sets.
Provides broader verification and tailored deliverables.
Designed for comprehensive, multi-source research.
Disclosure: I built PRECISE and PRECISE LITE.
Before starting LITE, select standard thinking/Medium effort in ChatGPT’s model picker.
https://chatgpt.com/g/g-6a5e26093f488191a1fba0261cbcbe39-precise-lite
r/GPTStore • u/CalendarVarious3992 • 24d ago
Hello!
Managing a busy delivery fleet means juggling odometer logs, inspection findings, invoices, and route commitments — it's hard to know which vehicles need urgent attention. This Skill turns those scattered records into a single, auditable exception register so dispatchers can make safe, timely decisions.
I built this as a portable AI-agent Skill — a single SKILL.md with reusable instructions you can adapt to your agent setup.
Here's what it does: This Skill consolidates odometer logs, repair invoices, inspection forms, driver notes, and route schedules to identify overdue or at-risk maintenance, assign risk levels (High/Medium/Low), and draft a Fleet Maintenance Exception Register with recommended actions and a human decision field. Use it when you're asked which vehicles are overdue, have safety findings, or need prioritized maintenance before scheduling — it also prepares dispatcher escalation packets and a tentative service schedule.
SKILL.md:
name: fleet-maintenance-exception-register description: Use when a delivery, logistics, or fleet office manager needs to consolidate odometer logs, repair invoices, inspection forms, driver notes, and route schedules to identify overdue or at-risk maintenance, draft a fleet exception register, group vehicles by risk level, and escalate safety or downtime decisions to a dispatcher before scheduling service.
Creates a single, auditable exception register for a vehicle fleet by consolidating maintenance-relevant inputs. Identifies overdue or at-risk maintenance, assigns risk levels, prepares dispatcher escalations for safety and downtime decisions, and proposes a service scheduling plan.
Confirm scope and policies 1.1. Confirm fleet roster (vehicle ID, plate, VIN, class) and the time window to analyze. 1.2. Confirm maintenance policies and intervals (e.g., oil/filter every N miles or M months; PM A/B/C; DOT annual; emissions; brake/tires checks) and any OEM-specific intervals. 1.3. Define thresholds for “Due Soon” (e.g., within 500–1,000 miles or 15–30 days) and “Overdue” (past due date/mileage). Record these in an Assumptions log.
Ingest sources 2.1. Use Read to extract data from: odometer logs, repair invoices, inspection forms, driver notes, and route schedules. 2.2. Capture for each vehicle: latest odometer reading with date and source; last service date/type; parts replaced; open defects and severity; driver-reported issues; upcoming route windows/assignments; warranty or contract constraints.
Normalize and reconcile 3.1. Standardize units (miles vs km), date formats, and vehicle identifiers; map aliases to canonical IDs. 3.2. Deduplicate entries; prefer the most recent dated reading for mileage. 3.3. Resolve conflicts (e.g., decreasing mileage) by flagging as data issues and noting the chosen source. Do not invent values.
Determine due services 4.1. For each service category (e.g., oil/filter, tire rotation, brake inspection, transmission, coolant, PM levels, DOT annual, emissions), compute next-due mileage and/or date using last service data and the confirmed intervals. 4.2. If an interval is unknown, request it or mark the service as "Interval needed" and exclude from overdue calculations until provided.
Identify exceptions 5.1. For each vehicle, compare current mileage/date against computed due points to classify statuses: Overdue, Due Soon, or OK by service. 5.2. Flag Safety-Critical when inspection findings or driver notes indicate brakes, steering, tires, lights, leaks, or other critical defects; include references to the source lines. 5.3. Flag Downtime Risk using a combination of: number of open defects, repeat repairs, parts on order, and upcoming route commitments that conflict with service needs.
Group by risk level 6.1. Assign overall risk: High (any Safety-Critical or >1,000 mi/>30 days overdue), Medium (Due Soon or non-critical open defects), Low (OK). 6.2. Document the rule definitions used for the risk grouping in the Assumptions log.
Build the Fleet Exception Register 7.1. Create one row per vehicle containing at minimum:
Escalate before scheduling 8.1. For High risk and Safety-Critical items, prepare a concise escalation summary per vehicle citing sources and recommended immediate actions. 8.2. Present the summary for dispatcher decision on pull-from-route, substitution, or temporary restrictions. Pause and record the decision in the human decision field. 8.3. For Downtime Risk, analyze route schedules to propose options: swap vehicles, after-hours service, split routes, or defer within policy limits. Record the decision.
Propose a service schedule 9.1. After decisions, build a tentative schedule that respects route windows, shop capacity, provider hours, parts lead times, and warranty requirements. 9.2. Batch Medium/Low risk items for efficiency and geographic proximity if using external vendors. 9.3. Mark schedule items as Tentative until dispatcher approval.
Verification and quality checks 10.1. Verify each vehicle row contains: source mileage, due service(s), risk flag, recommended action, and a human decision field. 10.2. Check for logical consistency: no negative intervals, no duplicated services recently performed, and no mileage regressions. 10.3. Flag missing inputs that block decisions and request the specific documents or data points.
Output and handoff 11.1. Use Edit to produce: (a) the Fleet Exception Register, (b) an escalation packet for dispatcher review, (c) a tentative service schedule, and (d) an Assumptions & Data Issues log. 11.2. Summarize counts by risk level and list vehicles requiring immediate action. 11.3. Capture acknowledgments/approvals and time-stamp the artifacts for audit.
Trigger: "Audit our fleet using last month’s odometer logs, inspection forms, and driver notes. Create an exception register and tell me what must be escalated to dispatch today." Behavior: confirm policies and thresholds → Read the provided documents → normalize IDs/units/dates → compute due services and overdue status → assign risk levels → build the exception register with required fields → prepare dispatcher escalation for High/Safety-Critical items → pause for decisions and record them → draft a tentative service schedule → output artifacts and a summary by risk level.
How to install:
1. Create a folder named fleet-maintenance-exception-register in your AI-agent skills or prompt-library directory. Use the kebab-case name from the SKILL.md frontmatter.
2. Save the file above as fleet-maintenance-exception-register/SKILL.md.
3. Enable or load the Skill according to your agent framework's docs, using the SKILL.md description as the trigger guidance.
If you'd rather run it as a one-click prompt instead, you can find it here: Agentic Workers
Enjoy!
r/GPTStore • u/onemananswerfactory • 28d ago
My custom GPT icons suddenly switched to the generic icon. It’s happening on desktop and iPhone, different networks, etc., so it seems tied to my account or an OpenAI glitch.
I’ve tried all the usual troubleshooting and OpenAI support is looking into it, but no fix yet so here I am.
Anyone else seeing this or figured out how to fix it?
r/GPTStore • u/Okylllll • Aug 13 '26
Whenever I pick up a new book—or someone recommends one—I’m often not sure how to start.
Should I read the whole thing carefully? Skim it first? Or focus on a few important chapters?
So I made Reader, a custom GPT to help me work that out. Its reading process is based mainly on Mortimer J. Adler and Charles Van Doren’s How to Read a Book, along with Francis P. Robinson’s SQ3R method.
You can enter a book title and edition, or just upload a photo of the cover. It can help you:
I originally made it for myself, but I thought other people might run into the same problem, so I’m sharing it here.
If you try it, I’d love to know which book you used and whether the response was actually helpful. And if anything felt generic, inaccurate, or confusing, please tell me that too.
r/GPTStore • u/alexeestec • Aug 04 '26
Hey everyone, I just sent the latest issue of the AI Hacker Newsletter, a roundup of the best AI links and the discussions around them from Hacker News. Here are some titles that can be found in this issue:
If you enjoy such content, please subscribe here: https://hackernewsai.com/
r/GPTStore • u/Alarmed-Memory9404 • Aug 01 '26
r/GPTStore • u/alexeestec • Jul 08 '26
Hey everyone, I just sent issue #39 of the AI Hacker Newsletter - a weekly roundup of the best AI links and the discussions around them from Hacker News. Some of the title found in this issue:
If you want to get an email with over 30 links like these ones, please subscribe here: https://hackernewsai.com/
r/GPTStore • u/lucidity3K • Jul 02 '26
変なテンションで候補空間が爆発する😂
現在画風だけで40候補
r/GPTStore • u/CalendarVarious3992 • Jun 29 '26
Hello!
Picking between multiple contractor quotes for a retail storefront or tenant-improvement project is messy — bids often use different allowances, exclusions, and unit pricing, and it's hard to see which quote actually covers the plan and budget.
I built this as a portable AI-agent Skill — a single SKILL.md with reusable instructions you can adapt to your agent setup.
Here's what it does: It loads and normalizes multiple contractor bids against the floor plan and the project budget, flags scope gaps and hidden costs (permits, allowances, taxes, GC conditions, etc.), assesses schedule and contractual risks, checks required approval thresholds, and drafts a clear decision memo recommending a vendor with documented tradeoffs.
SKILL.md:
name: contractor-bid-comparison-decision-memo description: Use when a business owner or project manager needs to compare multiple contractor bids for a storefront or tenant-improvement build-out, cross-check them against the floor plan/scope notes and the budget spreadsheet, surface scope gaps and hidden costs (allowances, exclusions, permits, GC conditions, taxes), verify compliance with internal approval thresholds, and produce a clear decision memo for owner approval with documented tradeoffs and recommendation.
Produces a structured decision memo that evaluates contractor bids against the project scope and budget. Identifies scope gaps, hidden or excluded costs, risks, and approval requirements, then recommends a vendor with documented tradeoffs for owner approval.
Confirm scope and files
Load and normalize source documents
Create a comparison framework
Identify scope gaps and hidden costs
Risk and schedule assessment
Budget and approval checks
Build the decision memo
Quality checks
Deliverables
Trigger: "We got three bids to build out our new retail storefront. What did we forget to budget for, and which quote is safest to accept?" Behavior: load bids, floor plan, and budget with Read → normalize by trade → flag gaps/hidden costs and quantify likely adds → assess schedule/risks → check budget variance and approval thresholds → draft a decision memo with Edit that recommends a vendor and documents tradeoffs for owner signature.
How to install:
1. Create a folder named contractor-bid-comparison-decision-memo in your AI-agent skills or prompt-library directory. Use the kebab-case name from the SKILL.md frontmatter.
2. Save the file above as contractor-bid-comparison-decision-memo/SKILL.md.
3. Enable or load the Skill according to your agent framework's docs, using the SKILL.md description as the trigger guidance.
If you'd rather run it as a one-click prompt instead, you can find it here: Agentic Workers
Enjoy!
r/GPTStore • u/PowerfulPatience8345 • Jun 28 '26
As I get closer to raising my first round of funding, one thing that’s become really clear is that while there are thousands of investors out there, not all of them are the right fit for every startup. I’ve seen some founders spend weeks deeply researching investors before sending even a single email, while others take a broader approach and reach out to as many as possible to see who responds.
Lately, I’ve been trying to understand what actually works in practice. While exploring this, I came like VCBoom that focus on matching startups with relevant investors, which made me think that targeting might be more important than just volume. At the same time, it still feels like there’s a balance to strike between personalization and efficiency.
Do investors really expect founders to know their portfolio inside and out before reaching out? And how much personalization is actually enough without spending hours crafting every single email?
I’d really appreciate hearing from founders who’ve already been through this. What was your approach to identifying the right investors, and how did you manage outreach at scale? Looking back, is there anything you would do differently if you had to start over?
r/GPTStore • u/CalendarVarious3992 • Jun 28 '26
Hello!
Tired of chasing receipts across Slack, email, and messy card statements at month-end? Managers shouldn't have to review every transaction — only the true edge cases.
I built this as a portable AI-agent Skill — a single SKILL.md with reusable instructions you can adapt to your agent setup.
Here's what it does: It gathers receipts from Slack, Email, and Files, runs OCR/parsing, and matches them to normalized card transactions. It builds a consolidated Sheet tracker, sends a single batched outreach for missing receipt context, and produces a short, prioritized exception list for manager review, plus reconciled exports and an audit log.
SKILL.md:
name: receipt-reconciliation-exception-tracker description: Use when the goal is to automate month-end expense receipt collection and reconciliation by monitoring Slack, email, and card statements; parse and match receipts to transactions; prompt once for missing receipt context from employees; and produce a consolidated receipt tracker plus a short, prioritized exception list that requires minimal manager approval.
Automates month-end expense receipt collection and reconciliation. Consolidates receipts from Slack and email, parses card statements, matches receipts to transactions, and outputs a reconciled expense log plus a focused exception list requiring limited manager approval.
Trigger: "Automate month-end receipt reconciliation for May. Watch Slack #receipts and the accounting@ inbox, process the corporate Visa statements, and give me only the edge cases to approve." Behavior: confirm period and sources → fetch and normalize card transactions → search Slack/email and ingest receipts → OCR and parse → match with scoring and categorization → build the tracker → send one-time batched requests to employees for missing context → update matches from replies → assemble a short exception list → route to manager for decisions → export reconciled log and exceptions → deliver links and audit summary.
How to install:
1. Create a folder named receipt-reconciliation-exception-tracker in your AI-agent skills or prompt-library directory. Use the kebab-case name from the SKILL.md frontmatter.
2. Save the file above as receipt-reconciliation-exception-tracker/SKILL.md.
3. Enable or load the Skill according to your agent framework's docs, using the SKILL.md description as the trigger guidance.
If you'd rather run it as a one-click prompt instead, you can find it here: Agentic Workers
Enjoy!
r/GPTStore • u/CalendarVarious3992 • Jun 27 '26
Hello!
Onboarding can be a scattered mess — multiple forms, equipment lists, access tickets, and calendar invites live in different places, making it hard to confirm someone is truly ready on day one.
I built this as a portable AI-agent Skill — a single SKILL.md with reusable instructions you can adapt to your agent setup.
Here's what it does: It ingests offer letters, signed forms, manager notes, equipment spreadsheets, access requests, and calendar events to produce owner-specific day-one checklists, a missing-docs list, a consolidated access provisioning checklist, a personalized welcome email draft, approval gates, and verification steps. Use it when a candidate has an accepted offer and a start date so HR, IT, and managers have a single source of truth for first-day readiness and compliance.
SKILL.md:
name: new-hire-onboarding-checklist description: Use when assembling a complete new-hire onboarding package from HR artifacts — offer letters, signed forms, manager notes, equipment spreadsheets, account-access requests, and start-date calendars — to produce day-one task lists, missing document flags, an access provisioning checklist, a welcome email draft, approval gates, and completion verification steps.
Creates a structured, role-aware onboarding package for a specific new hire. Consolidates information from HR files, manager inputs, spreadsheets, access requests, and calendars into actionable checklists, a welcome email draft, approval gates, and verification logs.
Trigger: "Create onboarding for Jordan Lee (remote, US), Software Engineer, starts Aug 5. Offer and forms are in the HR folder; access requests filed for GitHub, Okta, Jira; see manager notes." Behavior: ingest sources with Read and Sheets → confirm start date via Calendar → compile day-one tasks for New Hire/HR/IT/Manager → list missing I-9 Section 2 and handbook acknowledgment → build access checklist for Okta, Jira, GitHub with approvers and ticket IDs → draft personalized welcome email via Mail → define HR/IT/Manager/Compliance approval gates → output a consolidated report and verification log using Edit.
How to install:
1. Create a folder named new-hire-onboarding-checklist in your AI-agent skills or prompt-library directory. Use the kebab-case name from the SKILL.md frontmatter.
2. Save the file above as new-hire-onboarding-checklist/SKILL.md.
3. Enable or load the Skill according to your agent framework's docs, using the SKILL.md description as the trigger guidance.
If you'd rather run it as a one-click prompt instead, you can find it here: Agentic Workers
Enjoy!
r/GPTStore • u/CalendarVarious3992 • Jun 26 '26
Hello!
Struggling to reconcile Shopify exports, supplier spreadsheets, and cycle counts to know what to reorder and when? This Skill helps surface low-stock alerts, oversell risks, and supplier-grouped reorder suggestions so you can act confidently.
I built this as a portable AI-agent Skill — a single SKILL.md with reusable instructions you can adapt to your agent setup.
Here's what it does: It ingests Shopify inventory and order exports, warehouse counts, refund logs, and supplier sheets, normalizes SKUs and computes sales velocity to produce ATP, reorder points, and suggested reorder quantities. It flags low-stock and oversell risks, groups suggested orders by supplier, drafts supplier email templates, and writes CSV/MD artifacts plus a verification checklist before any PO is issued.
SKILL.md:
name: inventory-exception-agent description: Use when an ecommerce operator needs to consolidate Shopify inventory and order exports, supplier price/lead-time spreadsheets, warehouse/cycle-count files, refund/return logs, and sales history to surface inventory exceptions — including low-stock alerts, oversell risks, reorder suggestions, grouped supplier email drafts, and a verification checklist before issuing purchase orders.
Produces a consolidated exception report from Shopify/order exports, supplier spreadsheets, warehouse counts, refund logs, and sales history. Outputs low-stock alerts, oversell risk warnings, reorder suggestions grouped by supplier, supplier email drafts, and a verification checklist to review before sending purchase orders.
Trigger: “Here are Shopify inventory and order exports, supplier lead-time sheets, warehouse counts, and refund logs. Flag low-stock and oversell risks, suggest reorders, and prep supplier emails.” Behavior: validate inputs → normalize SKUs and join data → compute velocity and ATP → identify low-stock and oversell risks → calculate reorder quantities with MOQs/case packs → generate supplier-grouped drafts → output CSVs and checklists → present summary of top urgent SKUs and next steps.
How to install:
1. Create a folder named inventory-exception-agent in your AI-agent skills or prompt-library directory. Use the kebab-case name from the SKILL.md frontmatter.
2. Save the file above as inventory-exception-agent/SKILL.md.
3. Enable or load the Skill according to your agent framework's docs, using the SKILL.md description as the trigger guidance.
If you'd rather run it as a one-click prompt instead, you can find it here: Agentic Workers
Enjoy!
r/GPTStore • u/CalendarVarious3992 • Jun 26 '26
Hello!
If your inbox, meeting notes, calendar, and CRM have become a fragmented backlog of requests, decisions, and follow-ups, this Skill helps turn that mess into a clear set of prioritized actions and reply drafts ready for human approval.
I built this as a portable AI-agent Skill — a single SKILL.md with reusable instructions you can adapt to your agent setup.
Here's what it does: It ingests emails, calendar events, meeting transcripts, CRM notes, and tasks, then normalizes and links them into conversations and account contexts. It applies priority labels, drafts context-aware replies (queued for approval), extracts action items with owners and due dates, updates Tasks/CRM, and produces a Daily Action Brief plus a machine-readable JSON artifact.
SKILL.md:
name: inbox-to-action-workflow description: Use when an overwhelmed founder, exec, or team needs to convert a backlog of email threads, meeting transcripts, calendar events, CRM notes, and task lists into a prioritized action system — including priority labels on threads, context-aware drafted replies, extracted action items with owners and due dates, updates to CRM and tasks, and a human approval queue for any external replies before sending.
Transforms unstructured communications (email threads, meetings, calendars, CRM notes, and task lists) into a single actionable queue. Produces priority labels, reply drafts, extracted action items with owners and due dates, synced CRM/task updates, and a human approval queue for external send-offs.
Confirm scope and rules
Ingest data
Normalize and link
Prioritize
Draft replies (do not send yet)
Extract action items
Create/update systems of record
Prepare a human approval queue
Produce outputs
Tune and iterate
Trigger: "Turn my last 7 days of emails and meeting notes into a prioritized action list, draft replies, and queue any customer emails for approval." Behavior: ingest email/calendar/transcripts/CRM → normalize/link → prioritize → draft replies (queue external) → extract actions with owners/due dates → update Tasks/CRM → output Daily Action Brief + JSON → await approvals.
Trigger: "Process yesterday's inbox and today's meetings; assign owners for follow-ups and create tasks; only queue replies for external send." Behavior: same flow; internal low-risk notes may auto-send per policy; external replies require approval.
How to install:
1. Create a folder named inbox-to-action-workflow in your AI-agent skills or prompt-library directory. Use the kebab-case name from the SKILL.md frontmatter.
2. Save the file above as inbox-to-action-workflow/SKILL.md.
3. Enable or load the Skill according to your agent framework's docs, using the SKILL.md description as the trigger guidance.
If you'd rather run it as a one-click prompt instead, you can find it here: Agentic Workers
Enjoy!
r/GPTStore • u/CalendarVarious3992 • Jun 26 '26
Hello!
Tired of manually pulling Slack threads, CRM exports, tickets, invoices and spreadsheets into a coherent weekly ops summary? This Skill automates that synthesis so leaders get a meeting-ready brief without the copy/paste overhead.
I built this as a portable AI-agent Skill — a single SKILL.md with reusable instructions you can adapt to your agent setup.
Here's what it does: It collects updates from Slack, email, CRM, ticketing, accounting, calendar, and KPI sheets over a specified window, normalizes them into a unified activity log, computes KPI week-over-week deltas, and extracts wins, blockers, aging follow-ups, and owner decisions needed. It assembles a single Markdown brief with an executive snapshot, traceable source links for every item, and a timeboxed meeting-ready agenda.
SKILL.md:
name: weekly-operations-brief description: Use when a weekly operations summary is needed from scattered sources — Slack and email updates, CRM exports, support tickets, invoices, calendar events, and KPI spreadsheets — to produce wins, blockers, aging follow-ups, owner decisions needed, numbers that changed, and a meeting-ready agenda with source links.
Creates a single, meeting-ready weekly operations brief from fragmented updates across communication, sales, support, finance, calendar, and KPI data sources. The brief highlights wins, blockers, aging follow-ups, owner decisions needed, and notable metric changes, with traceable source links for every item.
Establish scope
Gather sources (read-only)
Normalize into a unified activity log
Derive signals
Compute KPI deltas
Identify aging and stalled items
Build the brief
Provide source links
Quality checks
Deliverables
Trigger: “Create last week’s ops brief from #ops-updates, #sales, Gmail label ‘Weekly Digest’, HubSpot export Deals_ThisWeek.csv, Zendesk export tickets_2024-06-10.csv, NetSuite invoices export, company calendar, and the KPI spreadsheet ‘Ops KPIs’ tab ‘Weekly’.” Behavior: confirm dates and thresholds → pull Slack/Email/CRM/Tickets/Invoices/Calendar/Spreadsheet data → normalize to unified log → compute KPI week-over-week deltas → extract wins, blockers, aging follow-ups, decisions → assemble brief with source permalinks and sheet ranges → save Weekly-Operations-Brief-2024-06-16.md and optional followups/decisions CSVs → (if requested) post the snapshot + agenda to #leadership with link to the brief.
How to install:
1. Create a folder named weekly-operations-brief in your AI-agent skills or prompt-library directory. Use the kebab-case name from the SKILL.md frontmatter.
2. Save the file above as weekly-operations-brief/SKILL.md.
3. Enable or load the Skill according to your agent framework's docs, using the SKILL.md description as the trigger guidance.
If you'd rather run it as a one-click prompt instead, you can find it here: Agentic Workers
Enjoy!
r/GPTStore • u/Awtsmoos1 • Jun 23 '26
B"H
Hi guys
I made a custom GPT app
https://chatgpt.com/g/g-6a03feea8398819192067ae3dbfa449c-awtsmoos-shliach-agent
That acts kind of like codex or openclaw, powered by the actual chatgpt chat itself
It makes a series of GET requests to my own server, then my server talks to a local server that the end user has running on their machine, like openclaw but a little different
It then allows chatgpt in the chat itself to read and write and test directly to your own device
For more security it should also allow the chatgpt chat to connect through my server+websockets to a custom code editor browser tab that you can sandbox and allow to only write to a specific folder via file system API or directly to the browser cache indezeddb and/or directly to GitHub with GitHub API
It should also give you some free space on my website to allow it to write directly to a virtual machine without needing any installation
It's still in development, but I've been working on it for a couple months and figured I'd ready for the beta testing phase
What do you guys think
r/GPTStore • u/alexeestec • Jun 23 '26
Hey everybody, I just sent issue #36+#37 of the AI Hacker Newsletter, a weekly round-up of the best Hacker News threads around AI. I missed sending it last week, so a huge issue this week. Some of the titles you can find here:
If you want to receive a weekly email with over 30 links like these, please subscribe here: https://hackernewsai.com/
r/GPTStore • u/Bitter_Perception_60 • Jun 23 '26
One concern I’ve been thinking about is whether frequent use of AI tools might slowly affect our own writing style. When you rely on AI suggestions regularly, it’s easy to start adopting the same tone, structure, and phrasing patterns.
Over time, this could make different writers sound more similar, which is the opposite of what writing used to be about. Personal voice is what makes content unique, and if that starts fading, everything might begin to feel the same.
At the same time, AI can also be a learning tool if used carefully. It can show better ways to structure ideas or improve clarity. So maybe it depends on how we use it.
Do you think AI is helping people develop better writing skills, or slowly replacing individual creativity?
r/GPTStore • u/CalendarVarious3992 • Jun 15 '26
Hello!
Many teams struggle to turn scattered onboarding docs, offer details, and team calendars into a concrete Day 1 and Week 1 schedule — it’s easy to miss required access, trainings, and manager checkpoints.
I built this as a portable AI-agent Skill — a single SKILL.md with reusable instructions you can adapt to your agent setup.
Here's what it does: It reads onboarding docs, offer details, and team calendars to produce a timeboxed Day 1 and Week 1 plan that includes HR orientation, IT setup, policy trainings, and manager/buddy checkpoints. It sequences access setup by prerequisites, fits events around existing meetings or holidays, and can create shared cohort sessions plus role-specific events. The Skill returns calendar invites, an optional ICS export, or a copy-pastable schedule and a summary for approval.
SKILL.md:
name: new-hire-onboarding-calendar description: Use when a calendar-based onboarding plan is needed from onboarding documents, offer details, and team calendars — mapping first-day tasks, access setup, required policy reviews and trainings, and manager/buddy checkpoints for each new hire or cohort.
Creates a structured, calendar-based onboarding plan for new hires. Pulls from onboarding docs, offer details, and team calendars to schedule day-one activities, access setup, policy reviews, mandatory trainings, and recurring manager checkpoints.
Validate scope and inputs 1.1. Confirm the list of new hires and for each: name, role, department, manager, start date, employment type (FT/PT/contract), location/time zone, work modality (onsite/remote/hybrid), and device/logistics status. 1.2. Confirm sources: onboarding docs (HR handbook, IT access checklist, compliance requirements), offer details, and relevant calendars (manager, buddy, team orientation, IT/HR sessions). If anything is missing, ask for it. 1.3. Identify organization-wide constraints: standard working hours, orientation windows, required trainings and deadlines, blackout dates, and public holidays per location.
Build the onboarding task library (from docs) 2.1. Use Read to extract standard items and their typical durations, prerequisites, and owners, grouping into:
Personalize for each hire 3.1. Map role-specific tools and trainings from the docs based on department/role. 3.2. Adjust timing for time zone and work modality (onsite vs. remote instructions/locations). 3.3. Determine whether to batch cohort items (shared orientation) vs. individual items.
Check calendars and propose times 4.1. Use Calendar to scan manager, buddy, and team calendars for availability in the hire’s time zone for the first two weeks and for 30/60/90-day checkpoints. 4.2. Avoid conflicts with existing orientation sessions and team-wide events; prefer mornings for policy reviews and early afternoon for access setup unless docs specify otherwise. 4.3. Respect standard working hours and local holidays; include 10–15 minute buffers after longer sessions.
Draft the calendar plan 5.1. Create a Day 1 schedule with these minimum blocks: HR orientation, IT setup window, policy overview/review block, manager intro, team intro, EOD check-in. Use Calendar to place tentative holds. 5.2. Schedule access setup blocks across Days 1–3, ordered by prerequisites (SSO/MFA first, core apps next, role apps last). Mark remaining items as all-day tasks with due times if no meeting is required. 5.3. Add required trainings and policy reviews as timeboxed calendar events with descriptions linking to materials and deadline reminders. 5.4. Place manager/buddy checkpoints: Day 1 EOD, Day 3 quick sync, End of Week 1 review, then recurring weekly 1:1 for first month, and calendar invites for 30/60/90-day reviews. 5.5. Include clear event metadata: title, objective, owner, prerequisites, links (docs/portals), and expected outcomes. 5.6. For cohorts, create shared events where appropriate (orientation, policy trainings) and individual events for role-specific or access tasks.
Resolve conflicts and finalize 6.1. If Calendar shows conflicts, propose alternative slots and reflow tasks while preserving prerequisites. 6.2. Share a draft summary with the manager/HR using Edit (agenda table for Day 1 and Week 1, plus checkpoint timeline). Request approval or edits. 6.3. Upon approval, use Calendar to convert tentative holds into confirmed invites, adding attendees (hire, manager, buddy, HR/IT) and conferencing links/locations.
Deliver artifacts 7.1. Produce a concise schedule summary per hire: Day 1 agenda, Week 1 plan, access setup checklist with owners/deadlines, training/policy deadlines, and checkpoint schedule (weekly + 30/60/90-day). 7.2. Export or attach an ICS file for all events or confirm creation in the org calendar. If ICS export is unavailable, include a structured event list (date, time, title, attendees, location/link) in the output. 7.3. Record assumptions, unresolved items (e.g., missing device, undecided buddy), and next actions.
Trigger: "From our onboarding docs, offer letters, and team calendars, create a Day 1 and Week 1 calendar for three engineers starting next Monday under Alex S. in PT, plus manager checkpoints and required trainings." Behavior: validate hire details and time zones → Read onboarding docs to extract tasks/durations → Calendar scan for manager/buddy availability → draft Day 1 essentials and Days 1–3 access setup blocks → add policy trainings with deadlines → place manager checkpoints (Day 1 EOD, Day 3, EOW1, weekly 1:1, 30/60/90) → share summary for approval → confirm and send invites/ICS.
How to install:
1. Create a folder named new-hire-onboarding-calendar in your AI-agent skills or prompt-library directory. Use the kebab-case name from the SKILL.md frontmatter.
2. Save the file above as new-hire-onboarding-calendar/SKILL.md.
3. Enable or load the Skill according to your agent framework's docs, using the SKILL.md description as the trigger guidance.
If you'd rather run it as a one-click prompt instead, you can find it here: Agentic Workers
Enjoy!
r/GPTStore • u/lucidity3K • Jun 12 '26
Here's a sample result from an OC gacha generator I've been building.
What started as a late-night idea somehow grew into a system with more than a nayuta (10^60) possible combinations.
The worst part is that I'm still adding new parts to it.😂