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SkillsMP has collected 22 skills from DavydenkoGr/AFTER. Open a skill to review its source and details.
Showing 22 of 22 collected skills.
Use for tasks that define, repair, validate, or preserve external interface contracts: REST or FastAPI endpoints, Pydantic models, JSON Schema, OpenAI tool/function schemas, ChatML or SFT JSONL formats, request batching, streaming callbacks, and…
Use for configuration repair and validation tasks involving YAML, JSON, TOML, environment variables, Nginx, Docker Compose, Kubernetes manifests, feature flags, service settings, logging, rate limits, and runtime behavior controlled by config files.
Author Dockerfiles, docker-compose stacks, and Kubernetes manifests as text artifacts: multi-stage builds, layer caching, image-size discipline, service composition with health checks, Deployment/Service/ConfigMap manifests, and pure-Python verification of…
Use for bug-fixing and failure-investigation tasks: reproduce failing tests or builds, trace dependency or configuration errors, patch minimal code, audit vulnerabilities, fix package/build breakage, and verify the repaired behavior with regression tests.
Create, inspect, and fill Word `.docx` files with python-docx, including robust template replacement across paragraphs, tables, nested tables, headers, and footers.
Use for tasks that compute, compare, optimize, or report metrics: classification and retrieval metrics, campaign uplift, model or prompt comparisons, LLM-as-judge reports, statistical significance, budgeted optimization, and behavior-preserving performance…
Use for evidence and safety verification tasks: detect prompt injection, mask PII, classify factual or policy-sensitive claims, redact sensitive text, compare claims to trusted evidence, and produce auditable reports with precision, recall, F1,…
Use for migration tasks that convert systems or artifacts while preserving behavior: SQL schema/data migrations, Terraform or IaC migrations, serialization format migrations, rollback planning, state movement, compatibility checks, and before/after validation.
Use for tasks that train, fine-tune, adapt, or evaluate predictive models: AutoML tabular models, time-series forecasting, custom PyTorch modules, object detection, QA fine-tuning, LoRA/support models, checkpoints, predictions, and metrics files.
Use for tasks where a PDF file is the primary input or output: extract text or tables, fill AcroForm fields, redact sensitive or authorship-leaking content, generate PDFs from structured records, inspect page-level statistics, or prove that a produced PDF is…
Use for data, ML, CI, and monitoring pipelines where the task is to assemble inputs, transform records, preserve dependencies, process streams, optimize workflow execution, or produce durable outputs and run statistics.
Use for PowerPoint tasks where a .pptx deck is read, created, edited, reformatted, compared, or validated, including slide layouts, placeholders, theme styles, speaker notes, images, charts, and reference-format matching.
Cheatsheet of prompt-engineering techniques: zero/few-shot, chain-of-thought, self-consistency, ReAct, tree-of-thought, role and system prompts, JSON-schema outputs, tool/function definitions, decomposition, critique-and-revise, structured extraction,…
Use for retrieval and grounding tasks: build or debug BM25, dense, hybrid, FAISS, metadata-filtered, or RRF retrieval pipelines; rank documents for queries; evaluate NDCG/MAP/Recall; and produce grounded outputs with document IDs, scores, ranks, and source…
Use for code-quality refactoring tasks: extract function from a long method, rename a confusingly-named symbol, restructure a tangled module, replace a conditional dispatch with polymorphism, split a god-class along its responsibilities, and introduce…
Reference for writing and tuning SQL on tabular sources: SELECT/INSERT/UPDATE/DELETE, INNER/LEFT/RIGHT/FULL/SEMI/ANTI joins, window functions (ROW_NUMBER, RANK, LAG/LEAD, running aggregates), recursive and non-recursive CTEs, EXPLAIN/EXPLAIN ANALYZE, index…
Reference for applied statistics on tabular data: descriptives, confidence intervals, one/two-sample and paired tests (t-test, Mann-Whitney, Wilcoxon, chi-square, Fisher exact, ANOVA, Kruskal-Wallis), assumption checks (Shapiro, Levene, Bartlett), correlation…
Use for authoring Terraform / HCL infrastructure-as-code: provider declarations (AWS, GCP, Azure), resources (VPC, subnet, security group, EC2, S3 / GCS, IAM, RDS), variables, locals, outputs, modules (calling and authoring), data sources, count vs for_each,…
Author Python test suites graders execute: pytest unit tests with fixtures and tmp_path, parametrized edge cases, mocking external boundaries with unittest.mock, property-based invariants via hypothesis, integration markers with conditional skips, coverage…
Use for tasks over transactional / event data: aggregating raw event logs into entity-level features (RFM, time-window stats), per-entity sequence representations (CoLES embeddings or hand-crafted lag features), double-entry ledger reconciliation and balance…
Use for tasks that check data, artifacts, citations, schemas, or cross-format records against explicit rules and produce clean outputs, violation lists, anomaly reports, reconciliation diffs, or validation summaries.
Use for spreadsheet tasks where the workbook itself matters: read, repair, populate, reconcile, calculate, format, or export XLSX/XLSM/CSV/TSV artifacts while preserving sheets, formulas, number formats, styles, tables, named ranges, and deterministic…