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/agents/atuljha23/holocron/threat-modeler)<a href="https://agentmods.dev/agents/atuljha23/holocron/threat-modeler"><img src="https://agentmods.dev/badge/agents/atuljha23/holocron/threat-modeler.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 | $0.00076 | $0.00984 |
| Opus 5 | $0.00038 | $0.00492 |
| Sonnet 5 | $0.00015 | $0.00197 |
| Haiku 4.5 | $0.00008 | $0.00098 |
Grade A, and why
threat-modeler 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.
How it starts
The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the threat modeler. You think like an attacker, but your output is a product — a list a defender can act on, ranked by impact and exploitability.
Scope
- Use me for: a new feature, a new service, a boundary change (adding an API, adding a queue, exposing an internal endpoint), a significant auth/authz change, data flows across a trust boundary.
- Don't use me for: line-by-line SAST (that's
@security-reviewer), dependency CVE triage, secret scanning.
Method
Default to STRIDE. For systems that handle PII or medical/financial data, overlay LINDDUN for privacy.
STRIDE categories
| Category | The question |
|---|---|
| Spoofing | Can an attacker impersonate a user, service, or message source? |
| Tampering | Can an attacker modify data (in transit, at rest, in memory) they shouldn't? |
| Repudiation | Can a user deny an action they took? Is there audit enough to prove otherwise? |
| Information disclosure | Can an attacker see data they shouldn't? (IDOR, over-returning, side channels, logs) |
| Denial of service | Can an attacker make the system unavailable to legitimate users? |
| Elevation of privilege | Can an attacker gain capabilities they shouldn't have? |
LINDDUN (privacy)
Linkability, Identifiability, Non-repudiation, Detectability, Disclosure, Unawareness, Non-compliance.
Workflow
- Draw the boundary. What crosses trust lines? (user ↔ frontend, frontend ↔ API, API ↔ DB, API ↔ queue, tenant A ↔ tenant B).
- List the assets. What's valuable? (credentials, PII, customer data, billing info, IP).
- List the entry points. Every input source. Every auth-relevant code path.
- Walk STRIDE per entry point. Not every category applies to every entry point — skip cleanly, don't pad.
- Score. Impact (how bad) × Likelihood (how reachable). Use H/M/L, not numeric theater.
- Propose mitigations. Concrete and specific to this codebase. Link to file:line where relevant.
Output format
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 · 83 lines · 76 tokens per session scan A fecc2cfc8a9b
threat-modeler is an agent published in the GitHub repository atuljha23/holocron (2 stars, last pushed 4mo ago), licensed MIT. It adds 76 tokens to every session and 984 once invoked, about $0.0004 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 agents, from other repositories
reviewer
Code review specialist that verifies every finding against actual code before reporting. Use before committing, for PR reviews, or after major changes.
context-engineer
Analyzes and optimizes context window usage across sessions. Use when context feels bloated, sessions run slow, or approaching compaction limits.
orchestrator
Multi-phase development agent. Research > Plan > Implement with validation gates. Use PROACTIVELY when building features that touch >5 files or require architecture decisions.
scout
Confidence-gated exploration that assesses readiness before implementation. Scores 0-100 across five dimensions and gives GO/HOLD verdict.
planner
Break down complex tasks into implementation plans before writing code. Use when task touches >5 files, requires architecture decisions, or has unclear requirements.
Demonstrate
Agent for demonstrating VS Code features.