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/gettalon/talon-pluginsWrote 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/gettalon/talon-plugins/autoresearch)<a href="https://agentmods.dev/commands/gettalon/talon-plugins/autoresearch"><img src="https://agentmods.dev/badge/commands/gettalon/talon-plugins/autoresearch/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/gettalon/talon-plugins/autoresearch"><img src="https://agentmods.dev/badge/commands/gettalon/talon-plugins/autoresearch.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 | $0.01007 |
| Opus 5.5 | $0.00016 | $0.00403 |
| Sonnet 5.5 | $0.00008 | $0.00201 |
| Haiku 4.5 | $0.00004 | $0.00101 |
Grade A, and why
autoresearch 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autoresearch — Autonomous Experiment Loop
Inspired by Karpathy's autoresearch. Iteratively modify a target file, run an experiment, measure a metric, and keep improvements or discard failures. Runs forever until stopped.
Arguments
Parse $ARGUMENTS for:
<file>— The file to modify (e.g.,train.py,src/lib.rs,index.tsx)<metric>— What to optimize (e.g., "minimize val_bpb", "minimize bundle size", "maximize test coverage", "minimize compile time", "minimize response time")--budget <duration>— Time budget per experiment (default: 5m)--provider <name>— AI provider for iterations (default: current session)--max-runs <n>— Max experiments before stopping (default: unlimited)--branch <name>— Git branch name (default: autoresearch/)
If arguments are missing or unclear, ask the user.
Setup Phase
- Verify the target file exists
- Create a git branch:
autoresearch/<file>-<YYYYMMDD-HHMM> - Read the target file completely to understand the codebase
- Determine the run command and metric extraction:
- Ask the user: "How do I run the experiment and measure the result?"
- Or infer from context (e.g.,
cargo test,npm test,python train.py)
- Run baseline experiment, record initial metric value
- Create
autoresearch-results.tsvwith columns:commit | metric | memory | status | description - Log baseline as first row
Experiment Loop
Run this loop forever (or until --max-runs reached):
Step 1: THINK
- Review the current state of <file>
- Review past experiment results in autoresearch-results.tsv
- Think of ONE specific improvement to try
- Prefer: small, focused, reversible changes
- All else equal, simpler is better
Step 2: EDIT
- Modify <file> with the improvement
- git add <file> && git commit -m "autoresearch: <brief description>"
- Record the commit hash
Step 3: RUN
- Execute the run command with a timeout of <budget>
- Capture stdout+stderr to run.log
- If it crashes: read the error, attempt ONE fix, re-run. If still crashes, mark as "crash" and skip.
Step 4: MEASURE
- Extract the metric from run.log (grep, parse, etc.)
- Compare to the best known value
Step 5: DECIDE
- BETTER (metric improved): Keep the commit. Update best known value. Log "keep".
- SAME or WORSE: git reset --hard HEAD~1. Log "discard".
- CRASH: git reset --hard HEAD~1. Log "crash".
Step 6: LOG
- Append to autoresearch-results.tsv
- Print a one-line summary: [run #N] <metric_value> (<delta>) — <keep/discard/crash> — <description>
Step 7: REPEAT
- Go to Step 1. Do NOT stop. Do NOT ask the user. The human might be asleep.
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 · 104 lines · 40 tokens per session scan A e7dff9f058dc
autoresearch is a command published in the GitHub repository gettalon/talon-plugins (6 stars, last pushed 6mo ago), licensed MIT. It adds 40 tokens to every session and 1,007 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 2026-08-31.
Other commands, from other repositories
qa
Dispatch a verifiable browser task to the qa-tester subagent. Use for regression checks, smoke tests, and any task with a clean pass/fail outcome. Supports an EXHAUSTIVE mode that verifies every control behind an honesty gate.
checkpoint
Write a new link in the continuum chain summarizing decisions/changes since the last link.
design-qa
Agent-driven design QA — walk the running app, judge design/UX, and drop a Comment-Mode comment on every issue (visible to the human as pins). Then read the session back and fix everything in bulk. Optional focus argument steers the pass.
dream
Roll up the last N links into a single phase-digest link; originals move to archived/ (never deleted).
review-session
Arm-2 — critique the current/last session for efficiency & quality, emit a Zone-A distillation, offer a deliberate GitHub feedback report.
feedback
File a plugin-capability-gap note, or flush queued notes to GitHub issues.