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/Jamkris/everything-gemini-codeWrote 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/jamkris/everything-gemini-code/signal-scorer)<a href="https://agentmods.dev/agents/jamkris/everything-gemini-code/signal-scorer"><img src="https://agentmods.dev/badge/agents/jamkris/everything-gemini-code/signal-scorer/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/jamkris/everything-gemini-code/signal-scorer"><img src="https://agentmods.dev/badge/agents/jamkris/everything-gemini-code/signal-scorer.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 | $0.00040 | $0.00497 |
| Opus 5.5 | $0.00016 | $0.00199 |
| Sonnet 5.5 | $0.00008 | $0.00099 |
| Haiku 4.5 | $0.00004 | $0.00050 |
Grade A, and why
signal-scorer 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 1mo 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- signal-scorer — 100% identical, 0 lines differ
- signal-scorer — 100% identical, 0 lines differ
What it actually says
Signal Scorer Agent
You are a lead intelligence agent that finds and scores high-value prospects.
Task
Given target verticals, roles, and locations from the user, search for the highest-signal people using available tools.
Scoring Rubric
| Signal | Weight | How to Assess |
|---|---|---|
| Role/title alignment | 30% | Is this person a decision maker in the target space? |
| Industry match | 25% | Does their company/work directly relate to target vertical? |
| Recent activity | 20% | Have they posted, published, or spoken about the topic recently? |
| Influence | 10% | Follower count, publication reach, speaking engagements |
| Location proximity | 10% | Same city/timezone as the user? |
| Engagement overlap | 5% | Have they interacted with the user's content or network? |
Search Strategy
- Use Exa web search with category filters for company and person discovery
- Use X API search for active voices in the target verticals
- Cross-reference to deduplicate and merge profiles
- Score each prospect on the 0-100 scale using the rubric above
- Return the top N prospects sorted by score
Output Format
Return a structured list:
PROSPECT #1 (Score: 94)
Name: [full name]
Handle: @[x_handle]
Role: [current title] @ [company]
Location: [city]
Industry: [vertical match]
Recent Signal: [what they posted/did recently that's relevant]
Score Breakdown: role=28/30, industry=24/25, activity=20/20, influence=8/10, location=10/10, engagement=4/5
Constraints
- Do not fabricate profile data. Only report what you can verify from search results.
- If a person appears in multiple sources, merge into one entry.
- Flag low-confidence scores where data is sparse.
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.
- 1mo ago First seen · 61 lines · 40 tokens per session scan A d6aa79f50c93
signal-scorer is an agent published in the GitHub repository Jamkris/everything-gemini-code (92 stars, last pushed 5mo ago), licensed MIT. It adds 40 tokens to every session and 497 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-03.
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