القائمة
assists in designing, evaluating and implementing factor-based investment strategies with Python
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Opinionated, scale-aware git workflow — Conventional Commits (English even under Chinese prompts), linear history via `pull --rebase` + fast-forward, when to commit to `main` vs open a PR, GitHub-Flow, worktrees for parallel agents, SemVer release tags, and pre-merge secret hygiene. Use when committing, branching, merging, opening a PR, tagging a release, setting up worktrees, or cleaning up stale branches — incl. "幫我 commit / 整理 git", "should I branch or PR", "git 工作流".
Set up and operate a research-experiment memory for ML/DL/Quant projects — LEDGER.md of numbered findings (what we currently believe, evidence links, overturn protocol), ROADMAP.md experiment queue triaged by expected payoff × cost × category × assumption dependencies, INBOX.md human scratchpad swept into the roadmap via targeted questions, an auto-rendered Mermaid map of how experiments/findings/queued work reference each other, and per-experiment REPORT.md with pre-registration, single-axis ablation contract, results tables and provenance blocks (git SHA, config hash, data window, seeds, optional MLflow refs). Use when the user wants to track which directions were explored vs not, record which succeeded or failed, avoid re-running dead-end experiments or wasting compute, make results discoverable and comparable, keep experiments reproducible, re-prioritize planned work when a conclusion changes, get a big-picture map of all experiments, jot a raw research idea somewhere, or asks to sweep the experiments inb
Commit SpecStory chat transcripts (`.specstory/history/*.md`), Claude Code plan files (`.claude/plans/*.md`, `plansDirectory`), and other coding-agent artifacts (`.cursor/plans/`, `.cursor/rules/`, `.opencode/plans/`, `.specify/`, `.codex/`) alongside the feature diff they produced — without leaking `.env` contents, API keys, or private-key PEM blocks into git history. Use when the user says "commit my chat", "save this specstory session", "stage the plan file", "scrub the transcript", "my .env leaked in chat", "bootstrap pre-commit for this project", or when you notice untracked `.specstory/history/*.md` or `.claude/plans/*.md` files while running `git status`. Also use after an accidental push of a secret to enforce rotate-first, rewrite-last remediation instead of reflexive `git push --force`.
Capture acceptance evidence — screenshots, terminal/screen recordings, HTTP logs — for a just-built feature into a gitignored .evidence/ bundle keyed to git branch/commit + coding-agent session, for async human validation (Cursor "Demos over diffs" style). Use when the user says "record a demo", "leave evidence", "capture a screenshot/video", "prove it works", wants async sign-off / 驗收, or asks to show a feature works without reading the diff or booting the app by hand. Playwright/asciinema/curl/ffmpeg.
Generic Slurm/HPC know-how for any cluster. Use when the user writes or debugs sbatch/srun batch scripts, requests resources (--mem/--cpus-per-task/--gres/--time/partitions), reads job state with squeue/sacct/sinfo/scontrol, cancels jobs, or reasons about resource isolation — cgroup CPU/RAM limits and GPU GRES (gpu vs shard vs mps vs MIG) and which of them actually fence VRAM so a misbehaving job fails alone.
Discover and drive a running Clash/mihomo proxy through its external-controller API — show the current node/mode/latency, switch proxy groups, set mode (rule/global/direct), toggle TUN, reload config, close connections, and toggle the OS system proxy. Use when the user asks to check or change their Clash/mihomo/Clash Verge (Rev)/ClashX proxy, switch nodes, test node latency, enable TUN, set up Service Mode or Mixin, toggle the system proxy on macOS/Linux, or fix an unreachable Clash API.
| name | quantatitive-factor-researcher |
| description | assists in designing, evaluating and implementing factor-based investment strategies with Python |
You are a Quantitative Factor Researcher who assists in designing, evaluating and implementing factor-based investment strategies with Python.
Core Responsibilities
Language & Style Rules
Technical Conventions
Ethos
遵守以上規範，成為使用者可靠的量化因子研究伙伴。
