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/jayvee/aigonWrote 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/jayvee/aigon/aigon-research-eval)<a href="https://agentmods.dev/commands/jayvee/aigon/aigon-research-eval"><img src="https://agentmods.dev/badge/commands/jayvee/aigon/aigon-research-eval/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/commands/jayvee/aigon/aigon-research-eval"><img src="https://agentmods.dev/badge/commands/jayvee/aigon/aigon-research-eval.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 | $0.00000 | $0.02266 |
| Opus 5.5 | $0.00000 | $0.00906 |
| Sonnet 5.5 | $0.00000 | $0.00453 |
| Haiku 4.5 | $0.00000 | $0.00227 |
Grade A, and why
aigon-research-eval 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.
This is a copy
95% identical to research-eval — 14 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 — 259 lines — stays where its author put it; the contents beside it link to each section on GitHub.
aigon-research-eval
Evaluate and synthesize research findings from ALL agents, help the user select features, and update the main research document. This transitions research from in-progress to in-evaluation (matching the feature pipeline).
Argument Resolution
If no ID is provided, or the ID doesn't match an existing topic in progress or in-evaluation:
- List all files in
./docs/specs/research-topics/03-in-progress/and./docs/specs/research-topics/04-in-evaluation/matchingresearch-*.md - If a partial ID or name was given, filter to matches
- Present the matching topics and ask the user to choose one
Recommended: Use a Different Model
For unbiased evaluation, use a different model than those that conducted the research.
claude --model sonnet
/aigon-research-eval 05
Step 1: Run the CLI command
IMPORTANT: You MUST run this command first. It transitions the engine state to evaluating and refreshes the generated lifecycle view.
aigon research-eval <name>
Step 2: Read All Findings
Find and read ALL findings files:
docs/specs/research-topics/logs/research-{ID}-*-findings.md
Also read the main research topic:
docs/specs/research-topics/04-in-evaluation/research-{ID}-*.md
(If not found in 04-in-evaluation/, check 03-in-progress/.)
Step 3: Synthesize Findings
Present to the user:
Consensus
What all agents agree on.
Divergent Views
Where agents disagree and why - be specific about which agent said what.
Step 4: Consolidate Features
Extract the ## Suggested Features table from each agent's findings file.
Deduplication rules:
- Exact match: Same feature name from multiple agents = one entry, note all agents
- Similar concept: Different names but same idea = merge into one, pick the best name
- Related but distinct: Keep separate but note the relationship
- Unique: One agent only = keep, but flag for user attention
Present a consolidated table:
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 · 259 lines · 0 tokens per session scan A 237387091a13
aigon-research-eval is a command published in the GitHub repository jayvee/aigon (26 stars, last pushed 12d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 2,266 tokens. A static security scan graded it A with 0 findings. It is 95% identical to research-eval, differing in 14 lines, and is treated as a copy.
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