threat-model-discipline
Use when starting an engagement, before exploitation, or whenever the attack surface changes — build/validate the threat model and detect drift (new unreviewed surface) before advancing
Install / Use
npx skills add hypnguyen1209/offensive-claude --skill threat-model-disciplineInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
SecuritySupported Platforms
Our assessment of threat-model-discipline
threat-model-discipline scores 83/100 on our quality scale, 924th of 1,096 Security skills we index.
Its SKILL.md is 2.6 KB long, well organised into 9 sections with 1 code example: a solid amount of guidance for an agent.
It has 377 GitHub stars, a meaningful sign that others use it.
Maintenance, license and trust
- The repository was last updated 14 days ago, so threat-model-discipline is actively maintained.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.
Automated pattern scan on 2026-10-05. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
threat-model-discipline compared with similar skills
All 4 of these similar skills score higher than threat-model-discipline; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| threat-model-discipline (this skill)by hypnguyen1209 | 83 | 377 | 14d ago | SKILL.md |
| algorithmic-artby anthropics | 100 | 177.9k | 12d ago | SKILL.md |
| pptxby anthropics | 100 | 177.9k | 12d ago | SKILL.md |
| designby nextlevelbuilder | 100 | 130.2k | 13d ago | SKILL.md |
| ui-ux-pro-maxby nextlevelbuilder | 100 | 130.2k | 13d ago | SKILL.md |
Frequently asked questions
- How do I install threat-model-discipline?
- Run
npx skills add hypnguyen1209/offensive-claude --skill threat-model-discipline. The install tabs above show the steps for each supported agent. - Which AI agents does threat-model-discipline work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is threat-model-discipline safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is threat-model-discipline still maintained?
- The repository was last updated 14 days ago, so threat-model-discipline is actively maintained.
Skill content
View source on GitHubname: threat-model-discipline description: Use when starting an engagement, before exploitation, or whenever the attack surface changes — build/validate the threat model and detect drift (new unreviewed surface) before advancing scripts:
- scripts/threatmodel_lint.py
Threat-Model Discipline
Overview
You cannot test what you have not modeled. A threat model names the assets, entry points, trust boundaries, relevant ATT&CK techniques, and existing mitigations — so coverage is deliberate, not accidental. On a long engagement the surface drifts (a new endpoint, a new dependency); un-reviewed drift is where bugs hide. This skill keeps the model complete and re-checks it for drift.
When to Activate
- At engagement start (after recon-osint), before weaponize/exploit.
- Whenever recon is re-run or the target changes — to catch new attack surface.
- At
/engage.gate— the gate refuses to advance on un-acknowledged drift.
The model (JSON, materialized from recon)
threat-model.json (see templates/threat-model/): five required lists —
assets, entry_points, trust_boundaries, attck (technique ids), mitigations.
# 1. Lint - every required field present, no placeholders, valid ATT&CK ids
python skills/threat-model-discipline/scripts/threatmodel_lint.py lint .engage/recon/threat-model.json
# 2. Drift - diff a re-run against the reviewed baseline; NEW entry points/assets/boundaries are
# unreviewed surface and BLOCK the gate until re-reviewed or acknowledged
python skills/threat-model-discipline/scripts/threatmodel_lint.py drift \
.engage/recon/threat-model.baseline.json .engage/recon/threat-model.json
Or use /engage.threatmodel (materialize | lint | drift).
Red Flags — STOP
- "We'll model it as we go" — unmodeled surface = untested surface. Model first.
- "Recon changed but the threat model didn't" — re-run drift; new surface must be re-reviewed.
- A threat model full of
TBD/[fill in]— that is not a model; the lint fails it. - A new
entry_pointappeared and you proceeded anyway — that is the exact gap attackers use.
Rationalizations
| Excuse | Reality | |--------|---------| | "The model is obvious, skip it" | Obvious to you ≠ documented. Coverage you can't diff is coverage you can't trust. | | "Drift is just noise" | A new entry point is new attack surface. Acknowledge it explicitly or re-review. | | "ATT&CK mapping is busywork" | It turns 'we tested stuff' into 'we covered these techniques' — the report's backbone. |
Pairs with scope-discipline (what you may touch) and finding-discipline (what counts as proven).
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
