ablation-planner
Use when main results pass result-to-claim (claim_supported=yes or partial) and ablation studies are needed for paper submission.
Autonomous design space exploration loop for computer architecture and EDA. Runs a program, analyzes results, tunes parameters, and iterates until objective is met or timeout. Use when user says \"DSE\", \"design space exploration\", \"sweep parameters\", \"optimize\", \"find
Claude Code
$ npx -y skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill dse-loop --agent claude-code
Codex
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill dse-loop -a codex -y
Cursor
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill dse-loop -a cursor -y
Gemini CLI
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill dse-loop -a gemini-cli -y
GitHub Copilot
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill dse-loop -a github-copilot -y
OpenCode
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill dse-loop -a opencode -y
Windsurf
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill dse-loop -a windsurf -y
name: dse-loop description: "Autonomous design space exploration loop for computer architecture and EDA. Runs a program, analyzes results, tunes parameters, and iterates until objective is met or timeout. Use when user says \"DSE\", \"design space exploration\", \"sweep parameters\", \"optimize\", \"find best config\", or wants iterative parameter tuning." argument-hint: "[task-description — include program, parameters, objective, and timeout]" allowed-tools: Bash(*), Read, Grep, Glob, Write, Edit
> 🔁 **Do not wrap this skill in `/loop` / `CronCreate`.** It already loops > internally until its objective is met or it times out. Unlike the > verdict-bearing review/audit skills, its stop gate is an **objective > machine-checkable metric** (Type-A), so its self-termination is safe > same-model — the reason not to wrap it is **scheduler duplication**, not the > verdict fence. See > [`shared-references/external-cadence.md`](../shared-references/external-cadence.md).
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.
**NEVER do any of the following:**
**If a step requires any of the above, STOP and report to the user.**
| 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"`
| 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 |
1. **Parse $ARGUMENTS** to extract:
2. **Infer missing parameter ranges** — If the user provides parameter names but NOT ranges/options, you MUST infer them before exploring:
a. **Read the source code** — search for the parameter names in the codebase:
b. **Apply domain knowledge** to set reasonable ranges: | Parameter type | Inference strategy | |---------------|-------------------| | Cache/memory sizes | Powers of 2, typically 1KB–16MB | | Associativity | Powers of 2: 1, 2, 4, 8, 16 | | Pipeline width / issue width | Small integers: 1, 2, 4, 8 | | Buffer/queue/FIFO depth | Powers of 2: 4, 8, 16, 32, 64 | | Clock period / frequency | Based on technology node; try ±50% from default | | Bound depth (BMC/formal) | Geometric: 5, 10, 20, 50, 100 | | Timeout values | Geometric: 10s, 30s, 60s, 120s, 300s | | Boolean/enum flags | Enumerate all options found in source | | Continuous (learning rate, threshold) | Log-scale sweep: 5 points spanning 2 orders of magnitude around default | | Integer counts (threads, cores) | Linear: from 1 to hardware max |
c. **Start conservative** — begin with 3-5 values per parameter. Expand range later if the best result is at a boundary.
d. **Log inferred ranges** — write the inferred parameter space to `dse_results/inferred_params.md` so the user can review:
# Inferred Parameter Space
| Parameter | Source | Default | Inferred Range | Reasoning |
|-----------|--------|---------|---------------|-----------|
| CACHE_SIZE | config.py:42 | 32768 | [8192, 16384, 32768, 65536, 131072] | powers of 2, ±2x from default |
| ASSOC | config.py:43 | 4 | [1, 2, 4, 8] | standard associativities |
| BMC_DEPTH | run_bmc.py:15 | 10 | [5, 10, 20, 50] | geometric, common BMC depths |e. **Boundary expansion** — during the search, if the best result is at the min or max of a range, automatically extend that range by one step in that direction (but log the extension).
3. **Read the project** to understand:
· · · · · · · · 💬 Join Community · 🌱 ARIS is a methodology, not a platform. What matters is the research workflow — take it wherever you go.
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