linguistic-skills

Plugin linguistic-skills from marketplace magic-agent-skills in Votee-AI/magic-agent-skills. Commands read from the repo's manifest — 1 month ago.

Skills (30)

• magic-data-cleaning

  Clean data by detecting issues, handling missing values, normalizing strings, and executing cleaning plans. Use when: (1) data has missing values or nulls to impute, (2) text columns need normalization or deduplication, (3) type errors or inconsistent formats need fixing, (4) planning a cleaning str

• magic-data-exploration

  Explore data interactively and detect patterns systematically. Use when investigating a dataset — freely exploring quality issues, comparing segments, discovering correlations, or running automated pattern detection. Covers both interactive investigation (asking questions, following threads) and scr

• magic-data-lifecycle

  Routing and orchestration knowledge for data processing tasks. Provides pipeline ordering (load → profile → clean → transform → validate → deliver), skill routing table (which magic-data-* skill handles which operation), quality gating guidance, and checkpoint strategy. Read this skill to understand

• magic-data-loading

  Load and ingest data from any source — files (CSV, TSV, Parquet, JSON, JSONL, Excel), databases (SQLite, PostgreSQL, MySQL via connection string), or remote repositories (HuggingFace Hub datasets). Auto-detects format, encoding, and delimiter for files. Use when a user mentions data, a dataset, a fi

• magic-data-profiling

  Profile datasets — run quality scoring, distribution analysis, outlier detection, and issue detection. Use when assessing data quality, running quality_score.py, getting a quality overview, or profiling before cleaning.

• magic-data-synthesis

  Synthesize, generate, and transform data using LLM-based operations via DataDesigner engine. Use when: (1) filling missing values/sentinels with contextual content, (2) translating columns, (3) converting formats (HTML→markdown), (4) annotating/labeling records, (5) extracting structured data from t

• magic-data-transformation

  Transform data by reshaping, aggregating, merging, deriving columns, and delivering to external destinations (database, HuggingFace Hub). Use when: (1) pivoting, melting, or unpivoting tables, (2) grouping and aggregating data, (3) joining or merging multiple datasets, (4) creating calculated or der

• magic-data-validation

  Validate datasets against inferred or custom schemas, check cross-column constraints, detect sentinel/placeholder values, and catch statistical pitfalls (Simpson's paradox, join explosion). Use when verifying data quality after cleaning, enforcing schemas before delivery, checking for content placeh

• magic-data-visualization

  Select appropriate chart types and generate publication-quality visualizations (PNG, SVG, interactive HTML). Use when creating charts, plotting distributions, comparing groups visually, visualizing correlations, or supporting findings with visuals. Covers bar, line, scatter, histogram, box, heatmap,

• magic-linguistic-annotate

  Design, run, and audit annotation projects: guideline authoring methodology, IAA (inter-annotator agreement) metric selection (Cohen κ, Fleiss κ, Krippendorff α, γ), adjudication workflow, active learning for sample selection. Use whenever the user mentions annotation, labeling, gold standard, IAA,

• magic-linguistic-bitext

  Mine, align, filter, and synthesize parallel corpora for low-resource MT. Use whenever the user mentions parallel data, bitext, sentence alignment, LASER3, SONAR, Vecalign, hunalign, Bleualign, NLLB mining, CCMatrix, CCAligned, FLORES, OPUS, back-translation, dictionary substitution, MT pivoting, sy

• magic-linguistic-codeswitch

  Code-switching awareness for ML pipelines: Hinglish, Spanglish, Singlish, MSA + dialect Arabic, Mandarin + Cantonese alternation, and other bilingual / multilingual mixing. Use whenever the user mentions code-switching, code-switch, code-mixing, Hinglish, Spanglish, Singlish, Chinglish, Konglish, mi

• magic-linguistic-corpus

  Curate monolingual corpora for a target language: catalog awareness (OLDI / CulturaX / MADLAD-400 / Glot500 / Wikipedia / Common Crawl segments), language-ID at paragraph granularity (GlotLID / FastText / CLD3), Unicode-safe deduplication with MinHash, two-sided contamination audit, register-balance

• magic-linguistic-discourse

  Discourse-level analysis for the target language: choosing a discourse framework (RST / PDTB / SDRT / GUM), coreference (incl. zero-anaphora in pro-drop languages), discourse markers, and coherence-aware evaluation for long-context LLMs. Use whenever the user mentions discourse, RST, Rhetorical Stru

• magic-linguistic-ethics

  Apply CARE / FPIC / community-sovereignty / license-compliance / sacred-text gating across every linguistic project phase. Use whenever the user mentions FPIC, CARE principles, Indigenous data, data sovereignty, community engagement, license audit, attribution, sacred text, restricted corpus, religi

• magic-linguistic-eval

  Honest evaluation for low-resource LLMs: benchmark choice (FLORES+ / NTREX / Belebele / AfroBench / IndicXTREME / SEACrowd), metric choice (chrF++ / spBLEU / COMET / MetricX / GEMBA-MQM), BLiMP-style grammatical-knowledge probes, contamination check. Use whenever the user mentions evaluation, benchm

• magic-linguistic-historical

  Historical / comparative linguistics primitives for ML data augmentation: cognate sets across related languages, Swadesh lists for cheap bilingual-lexicon bootstrapping, regular sound-correspondence rules. Use whenever the user mentions cognates, Swadesh, comparative method, sound correspondence, pr

• magic-linguistic-lexicon

  Lexicography for ML: dictionary-building methodology, sense splitting vs lumping decisions, MWE inventories for RAG glossary injection + MT post-edit, citation-form conventions, variant handling. Use whenever the user mentions lexicography, dictionary-building, sense splitting, sense lumping, lemma,

• magic-linguistic-morph

  Morphological analysis for the target language: UniMorph paradigm lookup, SIGMORPHON segmenters, FST/HFST analyzer recommendations, morphology-aware data augmentation. Use whenever the user mentions morphology, morpheme segmentation, UniMorph, SIGMORPHON, FST, foma, HFST, agglutinative / polysynthet

• magic-linguistic-orchestrator

  Start here for any linguistic / NLP / LLM-for-low-resource-language task. Coordinates the 5-phase pipeline (Scope -> Acquire -> Analyze -> Evaluate -> Release) and routes to the right magic-linguistic-* specialist skill. Use whenever the user mentions a target language, asks about training/fine-tuni

• magic-linguistic-scope

  Identify a target language precisely and set the strategic direction for any LLM/NLP project on it. Use whenever the user mentions a non-English language by name, asks 'what should I do for [language]', mentions resource class / Joshi / Glottolog / WALS / Grambank / URIEL / typology / transfer sourc

• magic-linguistic-scripts

  Decide Unicode normalization policy, detect script confusables, recommend romanization/transliteration, and protect diacritics for the target language. Use whenever the user mentions Unicode, NFC, NFKC, normalization, romanize, transliterate, IAST, Pinyin, Hepburn, IPA, confusable characters, mixed-

• magic-linguistic-semantics

  Lexical + frame semantics for the target language: WordNet / Open Multilingual WordNet (OMW) coverage, FrameNet / PropBank-style SRL guidance, multi-word expressions (MWE / PARSEME). Use whenever the user mentions WordNet, OMW, synset, sense disambiguation, FrameNet, PropBank, semantic role labeling

• magic-linguistic-speech

  Bridge field-linguistics annotation (ELAN, Praat, FLEx, SayMore) and audio data into ML pipelines (Lhotse, ESPnet, k2/icefall, MMS, Whisper). G2P / IPA workflows; low-resource ASR / TTS recipe selection. Use whenever the user mentions ELAN, EAF, Praat, TextGrid, FLEx, SayMore, Lhotse, CutSet, ESPnet

• magic-linguistic-syntax

  Universal Dependencies (UD) treebank usage, cross-lingual parser transfer (UDify, Trankit, stanza), and agreement-probe construction for grammatical-correctness evaluation. Use whenever the user mentions UD, Universal Dependencies, treebank, dependency parsing, constituency parsing, parser transfer,

• magic-linguistic-tokenize

  Audit tokenizer fertility for a target language and recommend SentencePiece config + vocab-extension strategy (FOCUS / OFA / HyperOfa) + byte-fallback policy. Use whenever the user mentions tokenizer, BPE, SentencePiece, Unigram LM, fertility, vocab extension, byte fallback, OOV explosion, subword r

• magic-linguistic-transfer

  Plan cross-lingual adaptation of pretrained LLMs: LoRA / QLoRA / DoRA config (rank scales with typological distance, not data size), MAD-X / BAD-X adapter stacks, source-language selection via URIEL, continued-pretraining-vs-LoRA decision, catastrophic-forgetting mitigation, tool selection (Unsloth

• magic-report-generation

  Assemble data analysis findings into structured Markdown reports with mandatory sections (Summary, Data Provenance, Methodology, Key Findings, Caveats, Next Steps). Use when creating the final deliverable report after analysis is complete, generating an executive summary, converting findings JSON in

• magic-statistical-analysis

  Perform descriptive statistics, hypothesis testing, and correlation analysis with mandatory uncertainty communication. Use when computing statistics, testing hypotheses, comparing groups, or analyzing correlations with significance.

• magic-workspace-init

  Initialize a MAGIC data processing workspace: directory scaffolding, Python environment verification, dependency installation, and LLM configuration. Use when starting a new data project or setting up the MAGIC environment for the first time.

Slash commands (23)

• /linguistic-skills:data:connect
• /linguistic-skills:data:decide
• /linguistic-skills:data:deliver
• /linguistic-skills:data:explore
• /linguistic-skills:data:findings
• /linguistic-skills:data:help
• /linguistic-skills:data:init-workspace
• /linguistic-skills:data:lifecycle
• /linguistic-skills:data:propose
• /linguistic-skills:data:review
• /linguistic-skills:data:rollback
• /linguistic-skills:data:spec
• /linguistic-skills:data:status
• /linguistic-skills:linguistic:decide

  Force a strategic decision (resource class, transfer source, eval suite, ethics gate).

• /linguistic-skills:linguistic:explore

  Investigate a corpus or language interactively, without committing to a pipeline plan.

• /linguistic-skills:linguistic:findings

  List structured findings (data gaps, ethics flags, fertility issues, eval contamination).

• /linguistic-skills:linguistic:help

  Show all linguistic commands and their natural-language triggers.

• /linguistic-skills:linguistic:lifecycle

  Enter the full linguistic pipeline orchestrator (Scope → Acquire → Analyze → Evaluate → Release).

• /linguistic-skills:linguistic:propose

  Generate a phase plan for the current target language (or for a freshly-named one).

• /linguistic-skills:linguistic:review

  Review state + findings + recent decisions + skill-routing history.

• /linguistic-skills:linguistic:rollback

  Rollback to a prior workspace_state snapshot.

• /linguistic-skills:linguistic:spec

  Create or edit a workspace data/model spec under specs/.

• /linguistic-skills:linguistic:status

  Quick one-line phase + skill-routing summary.