All skills
21 skills, 68 reference docs and 22 helper scripts. Search matches names, descriptions, section headings and reference titles.
excel-advanced-formulas
Modern Excel 365/2021+ formulas — LAMBDA and LAMBDA helpers (MAP, REDUCE, SCAN, BYROW, BYCOL), LET, dynamic arrays (FILTER, SORT, UNIQUE, HSTACK, VSTACK, TAKE, DROP), TEXTSPLIT, REGEX functions, GROUPBY and PIVOTBY.
excel-formulas
Write error-free Excel formulas — syntax rules, cell references, math, logical, lookup (VLOOKUP, INDEX/MATCH, XLOOKUP), text, date/time and conditional aggregation (SUMIFS, COUNTIFS) with error handling.
excel-fundamentals
Excel workbook fundamentals — cells, ranges, absolute/relative references, named ranges, number formats, conditional formatting, sheets and views, sorting, filtering, tables and keyboard shortcuts.
python-beginner-concept-tutor
Teach Python concepts to beginners heading into AI engineering (data scientists, analysts, students, career switchers): plain-language definitions, analogies, minimal examples, line-by-line walkthroughs, common mistakes, check-your-understanding questions and mini exercises; Socratic hints before solutions; explaining tracebacks; mutable vs immutable, references and aliasing, is vs ==, integer division, floating point, slicing and off-by-one, indentation errors; a staged learning path from variables to calling an LLM API, embeddings and a tiny RAG. Ships a script that lists the concepts a snippet uses in learning order and traces execution step by step like Python Tutor.
python-comprehension-refactorer
Refactor Python loops into list, dict and set comprehensions and generator expressions, and unroll comprehensions that are too clever: filtering, mapping, nested-loop flattening, conditional expressions, walrus in comprehensions, dict inversion, grouping with defaultdict/Counter, lazy generators for sum/any/all/max, streaming large files, itertools (chain, islice, groupby, batched, pairwise, accumulate, product), scoping rules, side-effect anti-patterns and performance (when map/filter or numpy/pandas vectorization win). AI-engineering focus: building prompt lists, batching texts for embeddings, filtering retrieved chunks by score, id->embedding dicts, flattening tool-call results, lazy dataset streaming and token counting.
python-context-manager-writer
Write correct Python context managers and find resource-handling bugs — __enter__/__exit__ semantics and exception suppression, contextlib (contextmanager, asynccontextmanager, ExitStack, AsyncExitStack, closing, aclosing, suppress, redirect_stdout, nullcontext, chdir), async with, re-entrancy, leaked files/connections/clients/pools/locks, and AI-engineering recipes: torch.no_grad/inference_mode/autocast, eval()/train() switches, GPU cache cleanup, token and latency meters, tracing spans, temp env vars, rate-limit semaphores, httpx/aiohttp client lifecycle, FastAPI lifespan, DB/vector-store transactions, atomic checkpoint writes, seeds and agent sandboxes.
python-dataclass-converter
Convert boilerplate Python classes, dicts, tuples and JSON payloads into dataclasses, or into pydantic v2 BaseModel, TypedDict, NamedTuple, attrs or msgspec when that fits better. Covers every @dataclass option (frozen, slots, kw_only, order, unsafe_hash, match_args, weakref_slot), field() (default_factory, init=False, repr, compare, hash, metadata), __post_init__, InitVar, KW_ONLY, inheritance and default ordering, replace(), asdict deep-copy pitfalls, hashing/equality, serialization and validation. AI focus: LLM request/response and chat Message types, RAG chunks with embeddings (numpy eq gotcha), eval records, configs, typed OpenAI/Anthropic JSON.
python-exception-handling-designer
Design and review Python exception handling: custom exception hierarchies with retryable flags, what to catch where, EAFP vs LBYL, chaining with raise ... from, else/finally, logger.exception, add_note, ExceptionGroup and except* with asyncio.TaskGroup, warnings, asserts vs validation, FastAPI error translation, retries with backoff and jitter, timeouts, cancellation, pytest.raises, plus LLM-app failures: 429 rate limits with retry-after, 5xx overloaded, context-length exceeded, content filters, broken streams, malformed JSON repair loops, tool-call errors fed back to the model, partial embedding batch failures, CUDA OOM batch halving, vector DB outages, circuit breakers and model fallbacks.
python-idiomatic-code-reviewer
Review Python code for non-idiomatic, unpythonic or risky patterns and rewrite it into clean modern Python: PEP 8 / PEP 20, EAFP vs LBYL, iteration idioms (enumerate, zip, comprehensions, any/all), unpacking, f-strings, pathlib, truthiness, `is None`, mutable defaults, walrus, match statements, collections / itertools / functools, logging instead of print, with-blocks and resource handling. Includes AI-engineering checks: hardcoded API keys, time.sleep or requests inside async def, missing HTTP timeouts, unbounded retry loops, model clients built at import time, prompt string concatenation, sequential awaits, eval() on LLM output. Ships a 46-rule AST reviewer with severities and suggested rewrites.
python-type-hints-annotator
Add accurate, modern type hints to Python and fix mypy / pyright (pyrefly, ty) errors: builtin generics, X | None, TypeAlias and the 3.12 type statement, TypeVar and PEP 695 generics, Protocol, Callable / ParamSpec / Concatenate, TypedDict (Required, NotRequired, ReadOnly), Literal, Final, ClassVar, overload, Annotated, TypeGuard / TypeIs, Self, Never, TypeVarTuple, async types, Iterable vs Sequence vs Mapping, TYPE_CHECKING, stubs, py.typed, gradual strictness. AI-engineering focus: typed LLM message dicts and tool schemas, pydantic vs TypedDict, numpy NDArray, torch / jaxtyping shapes, streaming generators, async clients, callbacks, generic VectorStore protocols, typed config. Ships an AST audit: coverage gate, legacy-typing rewrites, inferred types.
sql-analytics
Analytical and aggregation SQL — GROUP BY, ROLLUP, CUBE, GROUPING SETS, pivots, cohorts, funnels, retention, percentiles and statistical functions.
sql-data-quality
Profile and validate data with SQL — NULL analysis, duplicate detection, referential integrity, range and format checks, outliers, constraints and cleansing queries.
sql-debugger
Debug failing SQL or queries returning wrong results — interpret error messages, find causes of duplicates, missing rows, NULL surprises, fan-out joins and type mismatches, and produce a fixed query.
sql-dialect-translator
Translate SQL between dialects — PostgreSQL, MySQL, SQL Server (T-SQL), Oracle, SQLite — covering data types, functions, pagination, date handling, upserts and identity columns.
sql-optimizer
Rewrite slow SQL queries for performance — read execution plans (EXPLAIN / EXPLAIN ANALYZE), remove anti-patterns, make predicates sargable, choose JOIN/EXISTS strategies and suggest indexes.
sql-performance-tuning
Database-level SQL performance tuning — index design and maintenance, query profiling, statistics, partitioning, schema design, locking and configuration bottlenecks.
sql-query-explainer
Explain what an existing SQL query does in plain language — logical execution order, step-by-step breakdown of joins, filters, grouping and window logic, and the shape of the result.
sql-query-writer
Write correct, readable SQL from natural-language requirements — SELECT, INSERT, UPDATE, DELETE, JOINs, subqueries, CTEs and DDL (CREATE/ALTER/DROP).
sql-reporting
Build business reporting SQL — KPIs, dashboard datasets, period comparisons (MoM/YoY), executive, sales and financial reports and scheduled-report queries.
sql-syntax-checker
Validate SQL syntax and structure before execution and point out errors with corrected versions, dialect-aware (PostgreSQL, MySQL, SQL Server, Oracle, SQLite).
sql-window-functions
Write window/analytic SQL — ROW_NUMBER, RANK, DENSE_RANK, NTILE, LAG/LEAD, FIRST_VALUE/LAST_VALUE, running totals, moving averages and frame clauses (ROWS/RANGE).