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/llv22/AutoResearchWithEyesWrote 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/llv22/autoresearchwitheyes/autor.research-pipeline)<a href="https://agentmods.dev/commands/llv22/autoresearchwitheyes/autor.research-pipeline"><img src="https://agentmods.dev/badge/commands/llv22/autoresearchwitheyes/autor.research-pipeline/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/llv22/autoresearchwitheyes/autor.research-pipeline"><img src="https://agentmods.dev/badge/commands/llv22/autoresearchwitheyes/autor.research-pipeline.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 | $0.00083 | $0.01580 |
| Opus 5 | $0.00042 | $0.00790 |
| Sonnet 5 | $0.00017 | $0.00316 |
| Haiku 4.5 | $0.00008 | $0.00158 |
Grade A, and why
autor.research-pipeline 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 9d 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Full Research Pipeline: Idea → Experiments → Reviewed Research
End-to-end autonomous research workflow for: $ARGUMENTS
Constants
All constants (PILOT_MAX_HOURS, MAX_ROUNDS, POSITIVE_THRESHOLD, REVIEWER_MODEL) are defined in the project's CLAUDE.md. Read them from there before proceeding.
Overview
This skill chains two major workflows plus the implementation bridge between them:
/autor.idea-discovery → implement → /run-experiment → /autor.auto-review-loop → reviewed research
├── Workflow 1 ──┤ ├────────── Workflow 2 ──────────────┤
Note: This pipeline ends at reviewed research. To generate a submission-ready PDF, run
/autor.paper-writingseparately after this completes.
Pipeline
Stage 1: Idea Discovery (Workflow 1)
Invoke the idea discovery pipeline:
/autor.idea-discovery "$ARGUMENTS"
This internally runs: /research-lit → /idea-creator → /novelty-check → research-reviewer agent
Output: IDEA_REPORT.md with ranked, validated, pilot-tested ideas.
Gate 1 — Human Checkpoint:
After IDEA_REPORT.md is generated, pause and present the top ideas to the user:
Idea Discovery complete. Top ideas:
1. [Idea 1 title] — Pilot: POSITIVE (+X%), Novelty: CONFIRMED
2. [Idea 2 title] — Pilot: WEAK POSITIVE (+Y%), Novelty: CONFIRMED
3. [Idea 3 title] — Pilot: NEGATIVE, eliminated
Recommended: Idea 1. Shall I proceed with implementation?
Wait for user confirmation before continuing. The user may:
- Approve an idea → proceed to Stage 2.
- Pick a different idea → proceed with their choice.
- Request changes (e.g., "combine Idea 1 and 3", "focus more on X") → update the idea prompt with user feedback, re-run
/autor.idea-discoverywith refined constraints, and present again. - Reject all ideas → collect feedback on what's missing, re-run Stage 1 with adjusted research direction. Repeat until the user commits to an idea.
- Stop here → save current state to
IDEA_REPORT.mdfor future reference.
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.
- 9d ago First seen · 167 lines · 83 tokens per session scan A 9f226bdc7d6e
autor.research-pipeline is a command published in the GitHub repository llv22/AutoResearchWithEyes (5 stars, last pushed 1mo ago), licensed MIT. It adds 83 tokens to every session and 1,580 once invoked, about $0.0004 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 2026-08-31.
Other commands, from other repositories
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.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.