REPOGEO REPORT · LITE

hatchet-dev/icepick

Default branch main · commit caa28329 · scanned 6/7/2026, 9:53:28 AM

GitHub: 574 stars · 27 forks

AI VISIBILITY SCORE

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface hatchet-dev/icepick, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highabout#1

Clarify the repository's 'About' description to be more specific about AI agents

Why:

CURRENT

Build agents that scale with a zero-cost abstraction.

COPY-PASTE FIX

A TypeScript library for building fault-tolerant and scalable AI agents, handling durable execution and orchestration without being a heavy framework.

mediumreadme#2

Add a clear comparison to general workflow engines in the README

Why:

CURRENT

It handles the complexities of durable execution, queueing and scheduling, allowing you to focus on writing core business logic. [It is not a framework](#philosophy).

COPY-PASTE FIX

Icepick is a simple Typescript library for building AI agents that are fault-tolerant and scalable. It handles the complexities of durable execution, queueing and scheduling, allowing you to focus on writing core business logic. While Icepick provides durable execution and orchestration, it is purpose-built for AI agents, distinguishing it from general-purpose workflow engines like Temporal or Cadence. [It is not a framework](#philosophy).

lowtopics#3

Expand topics to include 'durable-execution' and 'workflow-orchestration'

Why:

CURRENT

agentic-ai, ai, bun, llm, no-frameworks, nodejs, orchestration, scalability, typescript

COPY-PASTE FIX

agentic-ai, ai, bun, durable-execution, llm, no-frameworks, nodejs, orchestration, scalability, typescript, workflow-orchestration

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface hatchet-dev/icepick

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

NestJS

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

NestJS · recommended 1×
BullMQ · recommended 1×
Prisma · recommended 1×
Fastify · recommended 1×
TypeORM · recommended 1×

CATEGORY QUERY
How to build fault-tolerant and scalable AI agents using TypeScript and Node.js?
you: not recommended
AI recommended (in order):
1. NestJS
2. BullMQ
3. Prisma
4. Fastify
5. TypeORM
6. Knex.js
7. Express
8. Agenda.js
9. Mongoose
10. Sequelize
11. AWS Lambda
12. Google Cloud Functions
13. AWS SQS
14. Google Cloud Pub/Sub
15. AWS DynamoDB
16. Google Cloud Firestore
17. AdonisJS
18. Winston
19. Pino
20. Prometheus
21. Grafana
22. opossum
23. Docker
24. Kubernetes
25. AWS ECS
26. AWS EKS
27. Google Kubernetes Engine
AI recommended 27 alternatives but never named hatchet-dev/icepick. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Library for orchestrating AI agent workflows with durable execution, avoiding heavy frameworks?
you: not recommended
AI recommended (in order):
1. Temporal (temporalio/temporal)
2. Cadence (uber/cadence)
3. Prefect (PrefectHQ/prefect)
4. Durable Functions (Azure)
5. AWS Step Functions
6. Apache Airflow (apache/airflow)
AI recommended 6 alternatives but never named hatchet-dev/icepick. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of hatchet-dev/icepick?
pass
AI named hatchet-dev/icepick explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts hatchet-dev/icepick in production, what risks or prerequisites should they evaluate first?
pass
AI named hatchet-dev/icepick explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo hatchet-dev/icepick solve, and who is the primary audience?
pass
AI named hatchet-dev/icepick explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

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HTML

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite