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.
npx skills add raja21068/AutoResearch --skill dse-loopgit clone --depth 1 https://github.com/raja21068/AutoResearchWrote 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/dse-loop)<a href="https://agentmods.dev/skills/raja21068/autoresearch/dse-loop"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/dse-loop/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/dse-loop"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/dse-loop.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 | $0.00070 | $0.03264 |
| Opus 5.5 | $0.00028 | $0.01306 |
| Sonnet 5.5 | $0.00014 | $0.00653 |
| Haiku 4.5 | $0.00007 | $0.00326 |
Grade C, and why
dse-loop scanned grade C with 1 finding 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
- `rm -rf`, `rm -r`, or any recursive deletion How it starts
The opening of the file, as written. The whole thing — 279 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DSE Loop: Autonomous Design Space Exploration
Autonomously explore a design space: run → analyze → pick next parameters → repeat, until the objective is met or timeout is reached. Designed for computer architecture and EDA problems.
Context: $ARGUMENTS
Safety Rules — READ FIRST
NEVER do any of the following:
sudoanythingrm -rf,rm -r, or any recursive deletionrmany file you did not create in this session- Overwrite existing source files without reading them first
git push,git reset --hard, or any destructive git operation- Kill processes you did not start
If a step requires any of the above, STOP and report to the user.
Constants (override via $ARGUMENTS)
| Constant | Default | Description |
|---|---|---|
TIMEOUT |
2h | Total wall-clock budget. Stop exploring after this. |
MAX_ITERATIONS |
50 | Hard cap on number of design points evaluated. |
PATIENCE |
10 | Stop early if no improvement for this many consecutive iterations. |
OBJECTIVE |
minimize | minimize or maximize the target metric. |
Override inline: /dse-loop "task desc — timeout: 4h, max_iterations: 100, patience: 15"
Typical Use Cases
| Problem | Program | Parameters | Objective |
|---|---|---|---|
| Microarch DSE | gem5 simulation | cache size, assoc, pipeline width, ROB size, branch predictor | maximize IPC or minimize area×delay |
| Synthesis tuning | yosys/DC script | optimization passes, target freq, effort level | minimize area at timing closure |
| RTL parameterization | verilator sim | data width, FIFO depth, pipeline stages, buffer sizes | meet throughput target at min area |
| Compiler flags | gcc/llvm build + benchmark | -O levels, unroll factor, vectorization, scheduling | minimize runtime or code size |
| Placement/routing | openroad/innovus | utilization, aspect ratio, layer config | minimize wirelength / timing |
| Formal verification | abc/sby | bound depth, engine, timeout per property | maximize coverage in time budget |
| Memory subsystem | cacti / ramulator | bank count, row buffer policy, scheduling | optimize bandwidth/energy |
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 · 279 lines · 70 tokens per session scan C c8ed70b66ae3
dse-loop is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 5mo ago), licensed MIT. It adds 70 tokens to every session and 3,264 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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…
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…
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…
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…