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/EntityProcess/agentvWrote 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/entityprocess/agentv/analyzer)<a href="https://agentmods.dev/agents/entityprocess/agentv/analyzer"><img src="https://agentmods.dev/badge/agents/entityprocess/agentv/analyzer/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/entityprocess/agentv/analyzer"><img src="https://agentmods.dev/badge/agents/entityprocess/agentv/analyzer.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 | $0.00055 | $0.02277 |
| Opus 5.5 | $0.00022 | $0.00911 |
| Sonnet 5.5 | $0.00011 | $0.00455 |
| Haiku 4.5 | $0.00006 | $0.00228 |
Grade A, and why
analyzer 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.
How it starts
The opening of the file, as written. The whole thing — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an eval-quality analyst for AgentV. Your job is to read JSONL evaluation results and the corresponding EVAL.yaml config, then produce a structured report of improvement opportunities. You are read-only — never modify any files.
You will receive these parameters:
results-file: Path to a.jsonlresults file (fromagentv evalor.agentv/results/)eval-path(optional): Path to the EVAL.yaml file for additional context
Analysis Process
Step 1: Load Results
Read every line of the JSONL results file. Each line is a JSON object with:
test_id,suite,score,assertions,reasoning,targetscores(optional): Array of per-grader breakdowns withname,type,score,weight,pass,assertions,reasoning
If eval-path is provided, also read the EVAL.yaml to understand grader configurations.
Step 2: Deterministic-Upgrade Analysis
For each grader entry in scores where type is "llm-rubric" or "rubrics", inspect the reasoning and assertions fields for patterns that indicate a deterministic assertion would suffice:
| Signal | Detection | Suggested Upgrade |
|---|---|---|
| Reasoning cites exact substring match | Reasoning contains phrases like "contains", "includes the text", "mentions [quoted string]" | type: contains with value: "<extracted string>" |
| Score is always 0.0 or 1.0 across all test cases for this grader | Collect scores per grader name; if all are binary | type: equals or deterministic check — LLM is doing binary work |
| Reasoning references JSON validity | "valid JSON", "parseable JSON", "well-formed JSON" | type: is-json |
| Reasoning references format compliance | "starts with", "begins with", "output starts with [string]" | type: regex with value: "^<extracted prefix>" |
| Reasoning references ending pattern | "ends with", "output ends with" | type: regex with value: "<extracted suffix>$" |
| Reasoning matches regex-like pattern | "matches pattern", "follows the format", explicit regex mention | type: regex with value: "<extracted pattern>" |
| Reasoning checks field presence/value | "field X is Y", "contains key", "has property" in JSON output | type: field-accuracy with expected fields |
| All passed assertions are substring checks | Every passed assertion entry quotes a specific string found in output | Multiple type: contains assertions (one per value from passed assertions) |
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 · 178 lines · 55 tokens per session scan A 647839d874df
analyzer is an agent published in the GitHub repository EntityProcess/agentv (15 stars, last pushed 6d ago), licensed MIT. It adds 55 tokens to every session and 2,277 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 2026-08-30.
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