Loading...
Loading...
Found 755 Skills
HK IPO Subscription Analysis — A "Four-Dimensional Evaluation" framework to diagnose whether Hong Kong new stocks are worth subscribing (Pricing Rationality / Issue Quality / Market Timing / Fundamental Outlook). Outputs three-tier ratings: Recommend / Neutral / Avoid, plus prospectus highlights, risk warnings, and subscription references. It is retail-investor friendly with conclusions upfront. Covers three scenarios: in-depth evaluation of a single new stock, browsing recent IPO subscription calendars, and judging whether to chase newly listed stocks after missing the subscription. Prioritizes data from Longbridge CLI (ipo detail / ipo subscriptions / ipo wait-listing / ipo listed / peer-comparison / news / quote / kline / index-quote, etc.); uses MCP fallback for data missing from CLI; uses WebSearch as a last resort for data still unavailable (prospectus TAM, original cornerstone announcement, claw-back ratio, grey market price, underwriter industry ranking). **The report must end with a fixed "Data Source Details" appendix**, where every figure can be traced to line number + capture time + period. Only covers Hong Kong Main Board and GEM; does not involve US / A-share IPOs; must actively prompt leverage risks when margin financing (孖展) is involved. Triggers: "打新", "港股打新", "新股申购", "新股申購", "新股", "打新分析", "新股分析", "招股", "招股书", "招股書", "基石投资者", "基石投資者", "国际配售", "國際配售", "公开发售", "公開發售", "暗盘", "暗盤", "回拨机制", "回撥機制", "孖展", "新股盈亏", "新股盈虧", "次新股", "破发", "破發", "中签率", "中籤率", "新股几手", "新股幾手", "新股值不值得打", "新股能不能打", "港股 IPO 推荐", "港股 IPO 推薦", "近期港股新股", "HK IPO analysis", "hong kong IPO worth it", "HK new listing", "cornerstone investor", "prospectus highlights", "grey market premium", "subscription ratio", "claw-back", "margin financing IPO", "0700.HK", "09988.HK", "01024.HK"
Translate approved GDDs + architecture into epics — one epic per architectural module. Defines scope, governing ADRs, engine risk, and untraced requirements. Does NOT break into stories — run /create-stories [epic-slug] after each epic is created.
Handle Chainlink ACE (Automated Compliance Engine) work using the public smartcontractkit/chainlink-ace repository and official docs.chain.link ACE Platform docs. Use for audited ACE core contracts, managed Platform/Beta scope, Coordinator API, Reporting API, Policy Management, PolicyEngine, PolicyProtected, policy chains, custom policies, extractors, mappers, Cross-Chain Identity (CCIDs), credential registries, KYC/AML credentials, sanctions screening, regulated tokens, ERC-20 and ERC-3643 compliance token examples, upgrade guidance, and BUSL licensing. Trigger on any mention of ACE, Automated Compliance Engine, chainlink-ace, Chainlink compliance, policy enforcement, ERC-3643, or onchain compliance rules, even if the user does not explicitly say 'ACE'.
Augment a Wren project with business context that DB schema cannot carry — enum value meanings, units (USD vs cents, ms vs sec), NULL semantics, magic sentinels (-1 = unknown), soft-delete default filters, business synonyms, time-grain / TZ conventions, cross-system identifiers, currency rules, canonical-table preferences, AND named aggregation metrics (ARR, churn, DAU, WAU, NRR) proposed as cubes. Runs in one of two modes selected at session start: `grill` (one question at a time, user-driven) or `auto-pilot` (agent infers and applies, escalates only on conflicts and high-blast-radius additions like new cubes / views / relationships). Reads everything under <project>/raw/ (PDFs, glossaries, handbooks, code, data dictionaries) and optionally samples low-cardinality columns from the live DB (grill mode), compares against the current MDL / cubes / instructions.md / queries.yml / memory pairs, then fills gaps via the ten-category gap catalog and the cube proposal flow. Confirmed findings are written back to the right sink. Use when: user says 'enrich context', 'augment my project', 'grill me on this project', 'auto-fill my context', 'agent doesn't understand our docs / enum values / units / null meanings', 'business context is missing', 'what does status=A mean', 'is this amount in USD or cents', 'we keep getting wrong aggregations', 'add cubes for ARR / DAU / churn', 'we have a handbook / glossary / data dictionary the agent should know'; or after generating an MDL and noticing the agent lacks business semantics.
Grade an IRAC essay for structure, issue-spotting, rule accuracy, analysis depth, and organization. Does NOT rewrite the essay or show a model answer; tracks patterns across sessions. Use when the user says "grade my IRAC", "check my essay", or "I wrote this, give me feedback".
Primarily the agent's internal-thinking skill — invoke it silently to model a problem, identify trade-offs, and decide what to do, BEFORE asking the user anything or dispatching another skill. Workflow skills call `/culture` as their step-1 reasoning pass; the agent does not surface the dialogue. Only treat this as a user-facing skill when the user has explicitly opted out of writes — phrases like "no writes", "just rubber-duck this", "let's only talk", "/culture". In the user-facing path the output is conversation; the only sanctioned artifact is an opt-in `.cheese/notes/<slug>.md` handoff slug at session end if the user asks for notes. Culture never writes to production code, never commits, never opens PRs. If the dialogue reveals real work, recommend `/mold` (fuzzy → spec) or `/cook` (clear ask → code) and stop. Before `/mold` or `/cook`.
SOP for Vehicle Entry/Exit Operation Process of AIKE Smart Parking Open Platform (End-to-end Orchestration for Entry/Exit). This skill is applicable when users want to complete the full entry/exit of a vehicle, simulate the real physical entry/exit process, run through the entire link of 'Supplement/Capture Entry → Correction' or 'Exit Capture → Correction → Fee Inquiry → Payment', or ask questions like 'How does a vehicle enter/exits the parking lot?', 'Entry/exit process', 'Entry/exit SOP', 'How to move a vehicle into/out of the parking lot', 'Simulate vehicle entry/exit', or encounter stuck/debugging issues during entry/exit (e.g., vehicle fails to enter after capture or correction, capture returns error code 908, unsure how to proceed to the next step). This skill is a cross-domain orchestration layer that connects multiple commands from parking (supplement/correction/inventory), device (capture), and trade (fee inquiry/payment), and clarifies cross-command hard constraints (paired exit, capture device, channel release mode, token validity) and failure handling. Boundary: For querying a single record, checking on-site vehicles, locking vehicles, or calling a single command, please directly use the corresponding domain skills openydt-record / openydt-device / openydt-billing instead of this process skill.
Patterns and anti-patterns for using OpenAI Codex Goals — the persistent objectives feature introduced in Codex 0.128.0. Use this skill whenever writing, reviewing, or debugging a `/goal` invocation, deciding whether a task should be a Goal at all, drafting a research Goal that needs an evidence ledger, or diagnosing a Goal that completed against the wrong surface. Triggers on `/goal`, "Codex Goal", "Codex goals", "persistent objective", "evidence-based completion", "iteration policy", "blocked stop condition", or any user message describing a multi-turn Codex task with a defined finish line. Trigger even if the user doesn't explicitly mention Goals — if they're typing "/goal" or asking Codex to "keep going until X", this skill applies.
Use when babysitting a PR/MR until CI is green and every valid reviewer feedback is addressed — supports GitHub PR (gh) and GitLab MR (glab), triages comments into Valid / Discuss / Out-of-scope, addresses valid items with small commits and inline thread replies, escalates invisible findings (SonarQube/Snyk dashboards) and 3-round bot deadlocks, reports ready-to-merge (never auto-merges). Triggers — '監看 PR', 'babysit PR/MR', 'PR 顧到 merge', 'address review feedback', 'wait until CI green', '把 PR 顧到綠'. NOT for writing PR descriptions, NOT for diff code review (use pr-review), NOT for actually merging the PR (user does that).
Owns the smoke test contract for an ML experiment: a small, diagnostic-by-construction pytest that fits the experiment's learner on a portion of the real `data/` source and predicts on a *disjoint* portion that deliberately carries **no pre-history buffer**. The assertion is structural — the number of predictions must equal the number of rows in the predict grid. A pipeline that loads-then-features-then-splits will silently drop the cold-start rows of the predict slice and the test will fail with a row-count mismatch; a pipeline that marks X early and references upstream history nodes from feature steps will pass trivially. The smoke test is the executable proof of the X-marker placement rule from `build-ml-pipeline`. TRIGGER when: `test-ml-pipeline` has dispatched here to write the smoke test for an approved experiment; `pytest tests/smoke/` is failing on row count; the user asks "why is the smoke test failing?"; a pipeline edit in `build-ml-pipeline` needs an executable proof; an experiment script changes the pipeline shape and the matching smoke test needs revisiting. SKIP when: the design note does not exist or is not yet approved (route to `iterate-ml-experiment`); the user is asking about a regression test or schema invariant (route to `regression-test-ml-pipeline` / `distribution-test-ml-pipeline` once those exist); the question is the *interpretation* of CV metrics, not predict-time correctness (route to `evaluate-ml-pipeline`). HOW TO USE: read the matching experiment's `journal/NN_*.md` and `experiments/NN_*.py` first to understand the pipeline's source binding (what env-dict keys does `build_learner` expect?). Then construct two env-dicts from the **real `data/` source** — a train env and a predict env — such that the predict env carries *only the rows we want predictions for* and *no pre-history buffer*. The hard assertion is that the prediction count matches the predict-env row count exactly. The soft assertion is that the smoke set's MAE is within `3 × CV_mean` (or the task-appropriate analogue). **Do not write the design note or run CV — that's other skills' job.**
Documentation conventions for NeMo-RL. Covers docs/index.md updates and docstring format. Do NOT use for: bug fixes, test fixes, dependency bumps, refactoring, CI/CD changes, performance tuning, or any task that does not involve writing or updating documentation.
QA-test a website or web app and return a 1-5 quality score (5 = flawless, 1 = broken) with evidence. Use when the user wants to test, QA, evaluate, score, or "check how good" a site, page, flow, or app — including a local dev server (e.g. "qa test localhost:5173", "does the checkout work?", "rate this landing page"). Drives a real Browser Use cloud browser, tunneling localhost automatically.