Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/raja21068/AutoResearchnpx agentmods add skills/raja21068/autoresearch/idea-discovery-robotWrote 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/skills/raja21068/autoresearch/idea-discovery-robot)<a href="https://agentmods.dev/skills/raja21068/autoresearch/idea-discovery-robot"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/idea-discovery-robot/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/skills/raja21068/autoresearch/idea-discovery-robot"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/idea-discovery-robot.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 | $0.00104 | $0.03392 |
| Opus 5 | $0.00052 | $0.01696 |
| Sonnet 5 | $0.00021 | $0.00678 |
| Haiku 4.5 | $0.00010 | $0.00339 |
Grade A, and why
idea-discovery-robot 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 8d 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 idea-discovery-robot — 6 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 — 364 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Robotics Idea Discovery Pipeline
Orchestrate a robotics-specific idea discovery workflow for: $ARGUMENTS
Overview
This skill chains four sub-skills into a single automated pipeline:
/research-lit → /idea-creator (robotics framing) → /novelty-check → /research-review
(survey) (filter + pilot plan) (verify novel) (critical feedback)
But every phase must be grounded in robotics-specific constraints:
- Embodiment: arm, mobile manipulator, drone, humanoid, quadruped, autonomous car, etc.
- Task family: grasping, insertion, locomotion, navigation, manipulation, rearrangement, multi-step planning
- Observation + action interface: RGB/RGB-D/tactile/language; torque/velocity/waypoints/end-effector actions
- Simulator / benchmark availability: simulation-first by default
- Real robot constraints: hardware availability, reset cost, safety, operator time
- Evaluation quality: success rate plus failure cases, safety violations, intervention count, latency, sample efficiency
- Sim2real story: whether the idea can stay in sim, needs offline logs, or truly requires hardware
The goal is not to produce flashy demos. The goal is to produce ideas that are:
- benchmarkable
- falsifiable
- feasible with available robotics infrastructure
- interesting even if the answer is negative
Constants
- MAX_PILOT_IDEAS = 3 — Validate at most 3 top ideas deeply
- PILOT_MODE =
sim-first— Prefer simulation or offline-log pilots before any hardware execution - REAL_ROBOT_PILOTS =
explicit approval only— Never assume physical robot access or approval - AUTO_PROCEED = true — If user does not respond at checkpoints, proceed with the best sim-first option
- REVIEWER_MODEL =
gpt-5.4— External reviewer model via Codex MCP - TARGET_VENUES = CoRL, RSS, ICRA, IROS, RA-L — Default novelty and reviewer framing
Override inline, e.g.
/idea-discovery-robot "bimanual manipulation" — only sim ideas, no real robotor/idea-discovery-robot "drone navigation" — focus on CoRL/RSS, 2 pilot ideas max
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.
- 8d ago First seen · 364 lines · 104 tokens per session scan A ec32052b4da5
idea-discovery-robot is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 104 tokens to every session and 3,392 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to idea-discovery-robot, differing in 6 lines, and is treated as a copy.
Other skills, from other repositories
content-refinement-agent
Step 5 of the PaperOrchestra pipeline (arXiv:2604.05018). Iteratively refine drafts/paper.tex by simulating peer review and applying targeted revisions, with strict accept/revert halt rules, deterministic 0-100 decision bands (Accept/Minor/Major/Reject) that drive a target-met early stop, and a Devil's Advocate…
section-writing-agent
Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimentallog.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges…
outline-agent
Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimentallog.md, template.tex, conferenceguidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level writing plan with citation hints. TRIGGER when the orchestrator…
paper-autoraters
Run the four paper-quality autoraters from PaperOrchestra (arXiv:2604.05018, App. F.3) — Citation F1 (P0/P1 partition + Precision/Recall/F1), Literature Review Quality (6-axis 0-100 with anti-inflation rules), SxS Overall Paper Quality (side-by-side), and SxS Literature Review Quality (side-by-side). TRIGGER when the…
plotting-agent
Step 2 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the visualization plan from outline.json — render plots and conceptual diagrams from experimentallog.md and idea.md, optionally refine via VLM critique loop, and produce context-aware captions. Runs in parallel with the literature-review-agent. TRIGGER…
paper-writing-bench
Reverse-engineer raw materials (Sparse idea, Dense idea, experimental log) from an existing AI research paper to build a benchmark case for evaluating paper-writing pipelines. Replicates the PaperWritingBench dataset construction procedure from arXiv:2604.05018 §3 / App. C. TRIGGER when the user asks to "build a…