Works with the AI Agents you already use
Claude Code
Cursor
Codex CLI
Muse
Dots+22 moreClaude CodeCursorCodex CLIMuseDots
OpenClaw
Manus
Grok
Grok Bot+18 agentsAI Workflow Opportunity Hunter — Find Automation
Find the processes worth improving with automation, compare AI and rule-based fit, and rank opportunities using evidence, feasibility, value, and risk.
$9.99
AI Workflow Opportunity Hunter — Find Automation
Example session with this skill installed
Review this customer onboarding process from the notes and volume data I provide. Map the current steps and exceptions, identify candidates for deterministic automation versus AI assistance, estimate potential value only from the supplied inputs, flag permissions and integration unknowns, then rank the best 3 opportunities with validation plans.
- Read your context and instructions
- Compiled the ai workflow opportunity
Executive summary
Top opportunity to validate: automate low-risk, rules-based intake completeness checks and route incomplete applications for human follow-up.
Confidence: medium from the supplied workflow notes; validate the exact exception rate and system capabilities before committing. AI-assisted document extraction is a second-stage candidate only if input variability makes fixed rules insufficient and a representative accuracy test passes.
Current-state map (from supplied notes)
- Customer submits an onboarding form and attachments.
- Coordinator checks required fields and documents.
- Missing information is requested by email.
- Complete cases are entered into the CRM and assigned.
- Exceptions wait for manual review; the notes do not specify their frequency or handling time.
Gaps: monthly volume, average handling time, rework rate, exception categories, CRM API/access, document formats, privacy constraints, and baseline completion time are not supplied. No ROI or cash savings can be calculated yet.
Opportunity ranking
| Rank | Candidate | Automation fit | Potential value | Main dependency / risk | First validation |
|---|---|---|---|---|---|
| 1 | Required-field and checklist validation | Deterministic rules likely fit | Unknown until volume and effort are measured | Agreed checklist; form/CRM integration; false rejection risk | Replay 30–50 de-identified historical cases; compare results with coordinator decisions |
| 2 | Completeness-based routing and reminders | Rules-based if statuses and due rules are explicit | Unknown | Permissioned messaging; duplicate-send prevention; exception ownership | Test all status transitions, duplicate events, and opt-out/suppression rules |
| 3 | Document field extraction for variable attachments | AI-assisted candidate; not yet proven | Unknown | Document diversity, sensitive data, review burden, accuracy threshold | Blind test on representative formats; measure field-level accuracy and critical errors before any automatic write |
Value model (inputs needed)
Potential time capacity released = monthly cases × minutes saved per case ÷ 60. Convert capacity to cash savings only if the team can reduce paid hours, avoid hiring, or redeploy capacity to measurable value. Add implementation, integration, QA, exception handling, model/API usage, human review, and ongoing support costs before estimating net benefit.
Guardrails and next steps
- Keep final approval and unusual/high-impact cases with an accountable human.
- Confirm data classification, access scope, retention, messaging consent, CRM permissions, and audit needs before connecting systems.
- Validate the process map with its owner and collect missing volumes, effort, error/rework, and exception data.
- Test the rules-based candidate first; compare deterministic results against human labels. Add AI only if it measurably improves a defined ambiguous step.
- Stage rollout with a manual fallback, duplicate protection, owner, success threshold, and rollback plan.
Decision: proceed to a small validation study, not production deployment. Re-rank after measured baseline and integration/security evidence are available.
Connects securely to your tools. The creator never sees your data.
About this skill
Find processes worth automating
AI Workflow Opportunity Hunter helps teams discover where automation can create practical business value. It maps real work before proposing technology, separates deterministic automation from AI-assisted tasks, surfaces exceptions and dependencies, and ranks opportunities using transparent evidence rather than invented ROI.
What it does
- Starts from a business goal, named workflow, system, customer journey, cost, speed, error, or agency-service lens.
- Maps triggers, actors, steps, decisions, systems, handoffs, waits, rework, errors, inputs, outputs, and exceptions.
- Finds repetitive effort, delays, duplicate entry, search burden, coordination overhead, and unstructured-information bottlenecks.
- Assesses AI fit, rule-based automation fit, integration feasibility, data quality, access, risk, reversibility, measurability, effort, readiness, and time to value.
- Distinguishes time saved from cash saved and theoretical value from verified value.
- Prioritizes quick wins and strategic changes, with validation steps and implementation-ready briefs.
HUNT-360™ assessment
Each candidate is examined across outcome, owner, trigger, frequency, volume, inputs, steps, decisions, systems, handoffs, wait, rework, error, search, data entry, exceptions, judgment, AI fit, rule fit, integration, data, permissions, risk, reversibility, measurability, value, effort, time to value, repeatability, and readiness.
Working principles
Understand how work actually happens before automating. Simplify broken processes first. Use deterministic rules when they suffice; apply AI only where ambiguity, language, extraction, classification, ranking, generation, or reasoning adds value. Treat exceptions, human accountability, privacy, security, permissions, and integration dependencies as core requirements.
Deliverables
Receive a process inventory or current-state map, automation-fit assessment, opportunity scorecard, value scenarios with explicit assumptions, risk/dependency register, prioritized portfolio, validation plan, or implementation-ready opportunity brief.
Evidence boundaries
The skill never claims to have observed a workflow, queried a system, measured a metric, verified an API, or calculated ROI without evidence. Missing volume, labor cost, error rate, or savings inputs stay unknown or become labeled scenarios. Recommendations are provisional until process owners validate the map and assumptions.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
Reviews
No reviews yet
Be one of the first to try it. Every listed skill passes our trust checks below.
Security scanned
Passed our 8-point scan before listing
Fresh listing
Recently published to Agensi
Money-back guarantee
Doesn't do what its page promises? You get your money back.
Trust & safety
Security scanned
Verified clean 10 days ago
- Passed all security checks, Safe to install