Software
written by
machines.
Served with
a label.
Everything published here was written end to end by an AI — the code, the
tests, the documentation, this sentence. No human has read it line by line.
That is not a disclaimer buried in a footer. It is the first thing on the
page, and every project ships with the numbers to check it by.
On the counter
terraform-provider-freshdesk
Slop Facts
1 provider per helpdesk
Serving size28 resources
Amount per release
Written by machines100%
% verified
- Read by humans
- 0
- 0%
- Acceptance tests against a live helpdesk
- 21
- 100%
- Unit tests
- 420
- 100%
- API endpoints covered
- 364
- 100%
- Lint findings every linter enabled
- 0
- 0%
- Data sources
- 45
- Client methods
- 246
- Go, hand-written
- 0 lines
A Terraform provider covering the whole of the Freshdesk API v2 — agents,
groups, tickets, fields, SLA policies, automations, the knowledge base and
the forums.
Writing it turned up twelve places where Freshdesk's API disagrees with
Freshdesk's documentation: field writes that 404 on the documented path,
IDs that arrive quoted on some endpoints and bare on others, a
choices attribute with five different shapes, endpoints that
answer 405 to the delete their own docs describe. Each one is
handled, and named in the README.
Install
terraform {
required_providers {
freshdesk = {
source = "slop-place/freshdesk"
version = "~> 0.1"
}
}
}
Read the source
Registry
How it gets made
The spec is read, not recalled
The API reference is scraped and parsed before a line is written, and a
coverage script checks the client against that inventory on every build.
A missing endpoint fails CI.
It runs against the real thing
Every resource is exercised through create, refresh, update, import and
destroy on a live account. That is how the twelve documentation bugs were
found; a mock would have agreed with the docs.
Nothing is hidden
Where the API cannot do something — no delete on an SLA policy, a body the
server rewrites — the provider says so in its own docs and warns at plan
time rather than pretending.
Read it before you run it. Thorough testing is not the same
as review, and a machine can be confidently wrong in ways a test suite agrees
with. Everything here is MPL-2.0 and the diff is right there.