Install
$ agentstack add skill-happyhackingspace-skills-dit ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
dit - HTML Page, Form & Field Classifier
dît (means "found" in Kurdish) classifies HTML pages, forms, and fields using machine learning (LogReg + CRF). Zero external ML dependencies.
It classifies pages (login, error, landing, blog, etc.), detects form types (login, search, registration, password recovery, contact, etc.), and classifies each field (username, password, email, search query, etc.).
Installation
go get github.com/happyhackingspace/dit
Or install the CLI:
go install github.com/happyhackingspace/dit/cmd/dit@latest
CLI Usage
# Classify page type and forms on a URL
dit run https://github.com/login
# Classify forms in a local file
dit run login.html
# With probabilities
dit run https://github.com/login --proba
# Download training data and model from Hugging Face
dit data download
# Train a model
dit train model.json --data-folder data
# Evaluate model accuracy
dit evaluate --data-folder data
# Upload training data and model to Hugging Face
dit data upload
Library Usage (Go)
import "github.com/happyhackingspace/dit"
// Load classifier
c, _ := dit.New()
// Classify page type
page, _ := c.ExtractPageType(htmlString)
fmt.Println(page.Type) // "login"
fmt.Println(page.Forms) // form classifications included
// Classify forms in HTML
results, _ := c.ExtractForms(htmlString)
for _, r := range results {
fmt.Println(r.Type) // "login"
fmt.Println(r.Fields) // {"username": "username or email", "password": "password"}
}
// With probabilities
pageProba, _ := c.ExtractPageTypeProba(htmlString, 0.05)
formProba, _ := c.ExtractFormsProba(htmlString, 0.05)
Page Types
login, registration, search, checkout, contact, passwordreset, landing, product, blog, settings, soft404, error, captcha, parked, comingsoon, admin, directorylisting, defaultpage, wafblock, other.
Form Types
login, search, registration, password/login recovery, contact/comment, join mailing list, order/add to cart, other.
Field Types
Authentication: username, password, password confirmation, email, email confirmation, username or email Names: first name, last name, middle name, full name, organization name, gender Address: country, city, state, address, postal code Contact: phone, fax, url Search: search query, search category Content: comment text, comment title, about me text Buttons: submit button, cancel button, reset button Verification: captcha, honeypot, TOS confirmation, remember me checkbox, receive emails confirmation Security: security question, security answer
Full list of 79 field type codes available in data/config.json.
References
- Repository: https://github.com/HappyHackingSpace/dit
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: HappyHackingSpace
- Source: HappyHackingSpace/skills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.