
Build and validate theory-grounded optimization models for backside front-vs-back net cost (signal and clock), with strict metric contracts and stability gates against HPWL baselines. Use when users request principled model fitting, crossover reasoning, or promotion decisions from shadow mode to active optimization.
Evaluate whether backside benefits come from `CTS-backside-only` or from moving a selected subset of signal nets to backside, under a fair and physically valid comparison contract.
Plan and execute internet-assisted backside net cost modeling for signal and clock nets, then evaluate stability and correlation against HPWL baselines across multiple designs. Use when users ask to improve/validate backside cost models, choose benchmark designs, compare model vs HPWL, or prepare publishable model-evaluation evidence.
Theory-level veto skill for EDA plans. Use before expensive experiment submissions or major flow/model changes to identify logically unsound assumptions, contradiction with known physics/policy, and high-risk invalid comparisons. Produces GO/CONDITIONAL/NO-GO with evidence.
Drive BSPDN objective optimization toward target outcomes (about 8% dynamic-power reduction without area/timing regression, or 5%+ frequency uplift with non-worse power/area) using gated experiments and evidence tracking.
Manage and standardize project conda environment usage as an auxiliary utility skill, including env discovery, package availability checks, explicit env selection, and reproducible `conda run -n ...` integration for scripts and workflows.
Explore and structure local EDA knowledge, identify evidence gaps, and prepare targeted literature retrieval tasks for local download and follow-up parsing.
Perform post-experiment retrospective for EDA runs, classify failure/success mechanisms, propose high-confidence next actions, and decide whether to recursively trigger a new workflow-scoped-execution iteration. Use after each experiment batch with monitor/summary/manifest artifacts.
Bridge autoIdea literature recommendation, historical formulation, and idea-generation outputs into the current repo's paper/knowledge/skill workflow.
Execute delay-model validation gates against HPWL baselines. Use when evaluating model consistency, running Gate-0 contract checks, building Gate-1 bucketed scorecards, performing wirelength-aware correlation checks, or deciding readiness for active optimization.
Clean and normalize knowledge-base, tool-registry, and log artifacts by merging duplicates, removing stale items, and correcting inaccurate naming while preserving traceability.
Fetch primary-source paper metadata when evidence is missing or weak for EDA/model/flow claims. Use for requests like building reproducible paper candidate lists, generating user-download queues, and recording citation metadata for local validation.
Convert research ideas into falsifiable hypotheses and experiment plans with metrics, controls, pass/fail criteria, and confounder mitigation.
Maintain and evolve the EDA infrastructure stack (agent policy, knowledge base, tool registry, and skill system) with auditable guardrails and minimal-risk updates.
Enforce experiment hygiene for this EDA repo. Utility skill for knowledge gate, tool reuse checks, and maintenance-log updates during scoped execution.
Implement EDA research methods from approved hypothesis plans, with integration discipline, measurable contracts, and minimal-risk iteration.
Summarize local paper PDFs into structured, citation-grounded evidence notes. Use when the user provides local PDF paths and asks for methods/assumptions/results/limitations extraction or wants evidence mapped to current EDA hypotheses.
Capture and maintain reusable script-writing experience across wrappers, helpers, parsers, validators, and runtime shims so future script work can reuse proven patterns and avoid repeated anti-patterns.
Create immutable stage checkpoints for Innovus flows. Use when users ask for fixed golden files or restart-from-stage workflows (place/cts/route) with matched DEF+V+SDC and reproducible manifests.
Retrieve scoped knowledge-base context and tool-registry reuse evidence for an EDA task, then emit compact context artifacts that downstream skills can consume directly.
Audit and refine academic manuscripts for structure, terminology consistency, LaTeX hygiene, and high-signal grammar fixes. Use this skill for theses, papers, proposals, and technical reports when the task is manuscript writing rather than experiment execution.
Diagnose and implement backside-routing realization paths, including targeted reroute, local DEF/OpenDB patching, and OpenROAD-backed net-level rerouter bring-up, when theory predicts benefit but the current flow does not realize BM2/BM1 usage.
Evaluate whether the current BSPDN PDN contract is strong enough under `BPR reserved for PDN`, separating PDN sufficiency from signal-mixing questions.
Extract and maintain reusable experiment knowledge across `log -> result -> conclusion -> experience` layers, then hand that evidence to execution, retrospective, and veto skills without forcing them to re-read raw logs every time.
Generate and refine EDA research ideas through structured brainstorming and pro-vs-con debate, then output a testable idea decision memo.
Refines and restructures existing presentations into concise, high-impact versions suitable for short academic conference talks (e.g., 20 minutes). Focuses on logical flow, information density, and visual consistency.
Creates and refines academic presentations from research papers. Use this skill for tasks involving the conversion of scientific or technical documents into slide decks for conferences, seminars, lectures, thesis defenses, or academic reviews. Supports beamer LaTeX (.tex), PowerPoint (.pptx), and Markdown output formats.
Before each experiment, analyze latest route/STA evidence, reflect on method gaps, and output concrete improvement hypotheses and next-step A/B plan. Use when user asks to start new experiments or when model/flow conclusions may be unstable.
Audit whether the local BSPDN physical contract is coherent across paper assumptions, GT3 tech collateral, layer/via topology, and current flow policy before promotion or expensive attribution experiments.