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/research-autopilot)<a href="https://agentmods.dev/commands/jayvee/aigon/research-autopilot"><img src="https://agentmods.dev/badge/commands/jayvee/aigon/research-autopilot.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 | $0.00018 | $0.00572 |
| Opus 5 | $0.00009 | $0.00286 |
| Sonnet 5 | $0.00004 | $0.00114 |
| Haiku 4.5 | $0.00002 | $0.00057 |
Grade A, and why
research-autopilot 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 3d 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
1 near-identical copy found in the catalogue:
- arap — 98% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
aigon-research-autopilot
Run a fully autonomous Fleet research pipeline. Sets up findings files, spawns agents in parallel, monitors progress, and optionally runs synthesis when all agents submit.
aigon research-autopilot {{args}} [agents...]
Argument Resolution
If no ID is provided, or the ID doesn't match an existing topic in backlog or in-progress:
- List all files in
./docs/specs/research-topics/02-backlog/and03-in-progress/matchingresearch-*.md - If a partial ID or name was given, filter to matches
- Present the matching topics and ask the user to choose one
Usage
Basic usage (use configured default agents)
aigon research-autopilot {{args}}
Specify agents explicitly
aigon research-autopilot {{args}} cc ag cx
With auto-eval
aigon research-autopilot {{args}} cc ag cx --auto-eval
What it does
- Setup phase: If no findings files exist, runs
research-startto create them - Spawn phase: Creates a tmux session for each agent, running
research-do - Monitor phase: Polls agent findings files every 30s for
completestatus, prints status table - Evaluate phase: If
--auto-eval, runsresearch-evalautomatically; otherwise prints the command
Subcommands
aigon research-autopilot status {{args}} # Check agent statuses
aigon research-autopilot stop {{args}} # Stop all agents
Options
--auto-eval— Automatically runresearch-evalwhen all agents submit--poll-interval=N— Seconds between status checks (default: 30)
Requirements
- tmux must be installed (
brew install tmux) - At least 2 agents must be specified (or configured as defaults)
- Research topic must be in backlog or in-progress
Agent Workflow
Each spawned agent will:
- Run
research-doto conduct their research - Write findings to their findings file
- Run
agent-status research-completeto signal completion
Prompt Suggestion
After all agents submit, suggest the eval command:
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.
- 3d ago First seen · 76 lines · 18 tokens per session scan A d64b395f4276
research-autopilot is a command published in the GitHub repository jayvee/aigon (25 stars, last pushed 5d ago), licensed Apache-2.0. It adds 18 tokens to every session and 572 once invoked, about $0.0001 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 2026-09-03.
Other commands, from other repositories
pick-agent
팀 통째가 아니라, 검증된 직원 중 필요한 사람만 골라 내 직원으로 가져오기.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.