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/atuljha23/holocronWrote 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/atuljha23/holocron/threat-model)<a href="https://agentmods.dev/commands/atuljha23/holocron/threat-model"><img src="https://agentmods.dev/badge/commands/atuljha23/holocron/threat-model.svg" alt="Measured on agentmods" 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 | $0.00059 | $0.00445 |
| Opus 5 | $0.00030 | $0.00222 |
| Sonnet 5 | $0.00012 | $0.00089 |
| Haiku 4.5 | $0.00006 | $0.00044 |
Grade A, and why
threat-model 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 7d 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.
What it actually says
/holocron:threat-model
Scope: $ARGUMENTS — or the working-tree diff if empty.
Gather
- If scope is a file/dir: read it + the files it calls/imports from (one hop).
- If scope is "diff" or empty:
git diff --statthengit diffto see what changed. - Identify trust boundaries crossed — user ↔ app, app ↔ datastore, tenant ↔ tenant, internal ↔ external.
- Identify assets at stake — credentials, PII, customer data, billing, IP.
Delegate
Spawn @threat-modeler with the gathered context. Ask for a STRIDE pass (Spoofing / Tampering / Repudiation / Information disclosure / Denial of service / Elevation of privilege).
For systems with heavy PII, overlay a LINDDUN pass (Linkability, Identifiability, Non-repudiation, Detectability, Disclosure, Unawareness, Non-compliance).
Output
Severity-scored threat list. Each threat:
- Category
- Entry point (where the attacker touches the system)
- Scenario (2-3 sentences)
- Impact × Likelihood (H/M/L each)
- Mitigation (specific, file:line where possible)
- Residual risk after mitigation
Do not
- Do not confuse with
/holocron:sec-scan— that's SAST for line-level bugs; this is design-level. - Do not produce academic threat lists — rank by exploitability, cut the noise.
- Do not recommend "user education" as a mitigation.
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.
- 7d ago First seen · 40 lines · 59 tokens per session scan A 83fd199df4e9
threat-model is a command published in the GitHub repository atuljha23/holocron (2 stars, last pushed 4mo ago), licensed MIT. It adds 59 tokens to every session and 445 once invoked, about $0.0003 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.
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