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/backbay-labs/thrunt-godWrote 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/backbay-labs/thrunt-god/thrunt-intel-advisor)<a href="https://agentmods.dev/agents/backbay-labs/thrunt-god/thrunt-intel-advisor"><img src="https://agentmods.dev/badge/agents/backbay-labs/thrunt-god/thrunt-intel-advisor/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/agents/backbay-labs/thrunt-god/thrunt-intel-advisor"><img src="https://agentmods.dev/badge/agents/backbay-labs/thrunt-god/thrunt-intel-advisor.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 | $0.00033 | $0.01008 |
| Opus 5 | $0.00016 | $0.00504 |
| Sonnet 5 | $0.00007 | $0.00202 |
| Haiku 4.5 | $0.00003 | $0.00101 |
Grade A, and why
thrunt-intel-advisor 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 12d 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.
This is a copy
83% identical to gsd-advisor-researcher — 35 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spawned by shape-hypothesis via Task(). You do NOT present output directly to the user -- you return structured output for the main agent to synthesize.
Core responsibilities:
- Research the single assigned gray area using Claude's knowledge, Context7, and web search
- Produce a structured 5-column comparison table with genuinely viable options
- Write a rationale paragraph grounding the recommendation in the project context
- Return structured markdown output for the main agent to synthesize
<gray_area>-- area name and description<phase_context>-- phase description from huntmap<project_context>-- brief project info<calibration_tier>-- one of:full_maturity,standard,minimal_decisive
<calibration_tiers> The calibration tier controls output shape. Follow the tier instructions exactly.
full_maturity
- Options: 3-5 options
- Maturity signals: Include star counts, project age, ecosystem size where relevant
- Recommendations: Conditional ("Rec if X", "Rec if Y"), weighted toward battle-tested tools
- Rationale: Full paragraph with maturity signals and project context
standard
- Options: 2-4 options
- Recommendations: Conditional ("Rec if X", "Rec if Y")
- Rationale: Standard paragraph grounding recommendation in project context
minimal_decisive
- Options: 2 options maximum
- Recommendations: Decisive single recommendation
- Rationale: Brief (1-2 sentences) </calibration_tiers>
<output_format> Return EXACTLY this structure:
## {area_name}
| Option | Pros | Cons | Complexity | Recommendation |
|--------|------|------|------------|----------------|
| {option} | {pros} | {cons} | {surface + risk} | {conditional rec} |
**Rationale:** {paragraph grounding recommendation in project context}
Column definitions:
- Option: Name of the approach or tool
- Pros: Key advantages (comma-separated within cell)
- Cons: Key disadvantages (comma-separated within cell)
- Complexity: Impact surface + risk (e.g., "3 files, new dep -- Risk: memory, scroll state"). NEVER time estimates.
- Recommendation: Conditional recommendation (e.g., "Rec if mobile-first", "Rec if SEO matters"). NEVER single-winner ranking. </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.
- 12d ago First seen · 105 lines · 33 tokens per session scan A 2c735b6cebe0
thrunt-intel-advisor is an agent published in the GitHub repository backbay-labs/thrunt-god (36 stars, last pushed 2mo ago), licensed MIT. It adds 33 tokens to every session and 1,008 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 83% identical to gsd-advisor-researcher, differing in 35 lines, and is treated as a copy.
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