Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/threatcl/claude-pluginWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/threatcl/claude-plugin/threat-hcl-new)<a href="https://agentmods.dev/commands/threatcl/claude-plugin/threat-hcl-new"><img src="https://agentmods.dev/badge/commands/threatcl/claude-plugin/threat-hcl-new/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/threatcl/claude-plugin/threat-hcl-new"><img src="https://agentmods.dev/badge/commands/threatcl/claude-plugin/threat-hcl-new.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00019 | $0.00966 |
| Opus 5 | $0.00010 | $0.00483 |
| Sonnet 5 | $0.00004 | $0.00193 |
| Haiku 4.5 | $0.00002 | $0.00097 |
Grade A, and why
threat-hcl-new scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are creating a new Threatcl HCL threat model file. The argument string $ARGUMENTS is the model name (and optionally a short description after the first word group). If it's empty, ask the user for a name and stop.
1. Resolve the org slug
Run threatcl cloud whoami. Capture the org slug from the output — it goes into the backend block. If multiple orgs are listed, ask which one to scaffold for.
If whoami fails with an auth error, tell the user to run threatcl cloud login and stop. Don't try to write the file with a placeholder slug — threatcl cloud push will fail anyway.
2. Pick a filename
Slugify the model name to kebab-case and suffix with .tm.hcl (e.g. Payment Service → payment-service.tm.hcl). Write to the current working directory unless the user already has a threatmodels/ or models/ directory — in which case write there.
The .tm.hcl suffix is threatcl's convention and it matters for tooling: threatcl/drift-action discovers *.tm.hcl at the repo root (a bare root-level *.hcl is deliberately skipped so its own .threatcl-ci.hcl config is never mistaken for a model), and editors scope the threatcl lsp language server on the same suffix to avoid colliding with Terraform. The threatcl CLI itself accepts either.
If the chosen path already exists, stop and ask the user before overwriting.
3. Write the file
Use this template, filling in the org slug and model name:
spec_version = "0.8.0"
backend "threatcl-cloud" {
organization = "<org-slug>"
# threatmodel slug is added automatically on first `threatcl cloud push`
}
threatmodel "<Model Name>" {
description = "<one-sentence description, from $ARGUMENTS or a sensible default>"
author = "<git config user.name, falling back to '@team'>"
attributes {
new_initiative = "true"
internet_facing = "false"
initiative_size = "Medium"
}
# Add information_asset blocks for the data this system handles, e.g.
# information_asset "user records" {
# description = "Names, emails, hashed passwords"
# information_classification = "Confidential"
# }
# Add usecase blocks describing what the system is supposed to do.
# usecase {
# description = "Customers sign in to view their account"
# }
# Sample threat — replace or expand. Reference library items with `ref`
# when one applies (search with `threatcl cloud search -type threats`).
threat "Example threat — replace me" {
description = "Describe the threat in one or two sentences"
impacts = ["Confidentiality"]
stride = ["Info Disclosure"]
control "Example control — replace me" {
description = "How this threat is mitigated"
implemented = false
risk_reduction = 50
}
}
}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 89 lines · 19 tokens per session scan A 4b79d318dbc4
threat-hcl-new is a command published in the GitHub repository threatcl/claude-plugin (4 stars, last pushed 24d ago), licensed MIT. It adds 19 tokens to every session and 966 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.