「corpus」の検索結果

Use this skill to run a holistic regrounding pass on the entire microsoft/apm documentation corpus against current source code, page-by-page, and emit surgical fixes for stale claims. Activate when the maintainer wants a WHOLE-CORPUS audit (not per-PR review) -- typical triggers include "audit the docs", "reground the corpus", "check every page against code", "pre-release docs sweep", "the docs have drifted everywhere", or "we just reshaped the TOC, find dead links". Wave-batched and S7-verified; scales to the full ~112-page corpus in ~10 minutes wall-time. This is a SIBLING to docs-sync, not a replacement: docs-sync is per-PR (triggered by a diff); this skill is per-corpus (triggered by a maintainer ask). They share agent personas, schemas, and the docs index, but their triggers MUST NOT collide. Does NOT auto-merge, does NOT push without maintainer review, and does NOT replace per-PR drift detection.

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Ranked content search over a text corpus you point it at, using BM25 (via xhluca/bm25s). Corpus-agnostic: cloned repos, project knowledge stores, uploaded files and archives, any local directory. In-memory BM25 index per invocation, with a session-local disk cache for repeat runs against the same corpus. Use for "rank these documents", "search this corpus", "which files are most about X", "find content about Y", or any multi-word concept query against a known body of text where grep would return everything or nothing. Needs a corpus on disk. Not for searching stored memories or prior-session decisions (remembering), not for a named symbol or a file's structure (tree-sitting), and not for a literal string you could grep.

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Deep-crawl any website from start URLs, return per-page LLM-ready text/markdown/HTML plus metadata (title, description, author, language, canonical URL, OG) and in-scope outbound links. Use when user mentions deep crawl website, recursive crawl, crawl a whole site, scrape entire website, scrape docs site, scrape documentation, scrape knowledge base, scrape blog, build RAG corpus, build vector database from website, knowledge base for chatbot, GPT knowledge files, llms.txt, sitemap crawl, BFS crawl, scrape with depth or page limit, include exclude URL globs, remove boilerplate, strip navigation header footer, website to markdown, website to text, multi-page extraction, bulk page scraping, clean markdown from URL, docs site to markdown corpus, site to clean corpus. Also applies to building RAG pipelines, indexing a customer site, syncing docs into a vector store, generating training corpora from any docs hub, or expanding a single start URL into a clean corpus of every reachable in-scope page.

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Design a generative recipe DSL so agents emit small validated JSON documents and ONE audited interpreter renders them all — data, not code. Covers the N-artifacts threshold for choosing a DSL over bespoke code, designing roles as style slots so a single recipe renders unchanged in every output style, the validator gate (compile + bounds + real render in all styles, plus a --fast mode for corpus-wide sweeps), hand-authored seed recipes, and the emergent conventions that keep a corpus of hundreds consistent without per-item steering. Use when many similar artifacts (icons, status badges, diagram nodes, test fixtures) must be generated by agents, when per-item bespoke code is multiplying unreviewable code paths, or when a whole corpus must be re-rendered cheaply into new styles.

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Verify someone else's code fix against your own live corpora (agent transcripts, sessions, memory files) before trusting or merging it. Use when an external PR or patch touches how transcripts, sessions or agent memory are read. Triggers: /corpus-bench, run it on our corpus, verify the fix on our transcripts, corpus bench.

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Operational guide for the NV oOS Model Foundry (Proposal 067) — the cluster fine-tuning flywheel. Phase 1 shipped: the Corpus Foundry (Pro addon, addons/pro/includes/model-foundry/) — consent-gated, deduplicated, PII-scrubbed corpus exporters (trajectory_v1, preference_v1, docs_v1) with deterministic holdout splits and R12 provenance manifests. Covers the governance contract, row schemas, output layout, filter seams, the registration pattern for foundry tool slices, how to add a new exporter, and the test/validation gates. Use when extending the Corpus Foundry, debugging corpus exports, wiring a new data source, or continuing Phases 2–6 (train-worker, registry/serving, eval gate, fleet sharing).

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Retune a skill corpus for a new model, measurement-first: mine the run archive for a baseline, establish a noise floor, audit the corpus adversarially, then cut in measured passes until a pre-registered bar clears. Requires a benchmark harness that can A/B two builds of the corpus; refuses without one.

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Divergent ideation and corpus expansion via Verbalized Sampling plus extended thinking — single-shot generates several internally diverse candidates and surfaces the strongest, or expands a seed corpus into a diverse dataset for evals and test sets. USE WHEN be creative, brainstorm, divergent ideas, creative solutions, maximum creativity, tree of thoughts, radically different, name this, creative angle, expand this corpus, synthetic data, generate diverse examples, create test set. NOT FOR multi-cycle evolutionary ideation with Lamarckian meta-learning (use Ideate).

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Voert de iteratieve desk-review-&-completion-cyclus uit op een regelrecht-corpus — plannen, harvesten/migreren/uitbreiden, multi-agent valideren, bevindingen 4-weg classificeren (modellering-fout / wetgevings-fout / engine-limitatie / acceptabele untranslatable), synthese + heroverweging, documenteren (wetgevings-/stelselfouten-analyse, fixes-plan, engine-limitaties, diagrammen, corpus-status), verifiëren tegen externe bronnen (resolutie-tracker + bronnen-dossier), en een eindrapport per cyclus. Gebruik dit bij corpus-review op correctheid/coverage zonder live expert-sessie, het opstellen van een wetgevings-notitie-bron, schema-migratie, het harvesten van nieuwe wetten, of het aansturen van een autonome completion-loop. Dossier-agnostisch; de regelrecht-methode is de vaste taal. Voor live-validatie met experts: zie regelrecht-audit-products.

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Produces an attorney-ready memo from a corpus of legal documents supplied by the user. Use when a user shows up with a folder, zip, or vault of case documents and asks for a case summary, case evaluation, litigation package, intake memo, matter overview, or "can you summarize this case for me." The skill ingests the corpus into a searchable index, OCRs anything non-searchable, inventories and diagnoses the practice area, loads the appropriate practice-area playbook module(s) (PI/tort, commercial litigation, IP infringement, or user-authored extensions), iteratively searches the corpus across eight core dimensions plus any module-specific dimensions, defers specialized document clusters (depositions, medical records, discovery, liens) to dedicated sibling skills, and synthesizes a cited memo.

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Build a TYPED citation/reference graph over an ingested corpus — not just embeddings. Flat similarity retrieval cannot tell you that document A *overrules* B, *distinguishes* C, or *relies_on* D. This skill extracts every inter-document reference, classifies the edge TYPE with LLM judgment, and writes first-class typed edges via `gbrain link`, so `gbrain graph-query --type` can walk the argument ("everything this brief relies on, minus anything overruled since"). Every cite-heavy corpus is the same shape: law, academic papers, patents, regulatory filings, a book's bibliography.

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Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google Workspace RAG, or other RAG products like gRAG.

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Privileged applier that LANDS meta-optimize / corpus-audit patches the user approved — the ONLY skill permitted to mutate the skill corpus from a self-modification proposal, with cross-model jury and human approval at landing. Use when the user says "meta apply", "/meta-apply", "land the staged patches", "应用优化", after a /meta-optimize run.

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Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".

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Use this skill to classify the documentation impact of a pull request diff, returning one of three verdicts -- no-change, in-place edit, or structural change -- with bounded LLM cost. Activate as a sibling skill of docs-sync; the orchestrator calls this first, before any panel spawn, to keep cost floor at 1 LLM call when no docs work is needed. Reads .apm/docs-index.yml as the corpus map; never reads the full corpus.

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Use this skill to verify CLAIM-LEVEL grounding of a documentation page (or set of pages) against the source code. Activate when you have specific pages to check for factual accuracy -- not when sweeping a whole corpus (use docs-corpus-audit for that) and not when triaging a PR diff (use docs-sync for that). Trigger nouns: "is this doc accurate", "verify the page against the code", "fact-check this section", "any claims that drifted from source", "fact-checking", "grounding audit", "drift hunt", "claim verification". Returns per-claim verdicts (GROUNDED | PARTIAL | CONTRADICTED | UNSUPPORTED) with file:line evidence citations. Catches paragraph-level inaccuracies that page-level audit averages over -- e.g. a paragraph with 5 claims where 4 are grounded and 1 is fabricated. Does NOT modify files (returns advisory only); does NOT re-architect the docs; does NOT triage PRs.

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Pretrain a fresh KERMT model from scratch on a user-provided corpus. Builds a new vocabulary from the corpus, instantiates the model architecture from defaults, and launches pretrain_ddp.py inside the kermt container (detached for long runs). Unlike kermt-continue-pretrain, no starting checkpoint is loaded — the model is randomly initialized.

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Set up, download, verify, and seed the optional Grida Library developer corpus into local Supabase. Use when Library browse/search, Desktop reference picking, or agent Library tools need realistic local assets and Gemini embeddings; also use when changing the gridaco/grida consumer for gridaco/library developer-corpus releases.

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When you want to capture into, compile, query, lint, or connect your personal Second Brain. Wraps the Karpathy LLM Wiki schema (Obsidian or any markdown vault) — raw/ (unprocessed sources), wiki/ (AI-compiled interlinked topic pages), outputs/ (generated artifacts). Tool-agnostic in design but defaults to a vault at ${SECOND_BRAIN_VAULT:-$HOME/Documents/SecondBrain}/. Six modes — capture (drop something into raw/), compile (process unprocessed raw files into wiki pages, update INDEX.md), query (answer a question from the wiki, save to outputs/), lint (orphans / contradictions / stale / unprocessed raw / topic gaps), connect (suggest new wikilinks between pages), search (quick lookup). Triggers on "/second-brain," "/sb," "capture this," "save this to my brain," "compile the wiki," "process raw notes," "query my wiki," "ask my brain," "lint the wiki," "find connections," "search my notes." Complements deep-research (external corpus) — this is the internal corpus.

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Download a small corpus of open-access arXiv survey/review PDFs about agentic systems and extract text for style learning.

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Answer a question across a corpus of contract documents with verified citations. Use when the user asks what a contract says, which contracts have a clause, what changed between amendments, or any question that needs reading and citing across a set of contract files. The corpus must be on the local filesystem (see README).

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Use near the end of EZ_math_model intake when external/user-corpus contains user-provided papers, notes, PDFs, datasets, or examples that should become a local AGENTS.md reference index.

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Performs a hallucination check on machine_readable sections by verifying every element traces back to the original legal text. Use this skill proactively when: machine_readable sections have been generated or modified, after /law-generate completes, when reviewing corpus YAML files for legal accuracy, or when user mentions "validate", "verify", or "hallucination check" for law YAML files. Activate automatically after editing machine_readable sections in corpus regulation YAML files.

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The measurement surface for ALL Pulp DSP and audio-pipeline work — read it BEFORE writing or gating DSP, not only when something already sounds wrong. Covers the C++ harness (signal generators, metrics, assertions, RenderScenario, contracts), the offline Audio Doctor (magnitude/frequency response, THD/THD+N, phase/group delay), and their Python sibling the Audio Quality Lab (tools/audio/quality-lab — null residual + alignment, LTAS log-spectral distance, spectral flux/centroid, HNR, Theil-Sen drift slope, Kaiser-sinc resampling, license-guarded corpus, regression-net ratchet). TRIGGER on AUTHORING work — "build/design an oscillator/filter/synth/effect", "add a DSP module", "what should the acceptance gate be", "how do I measure aliasing / anti-aliasing / alias floor", "null against a reference", "is this DSP correct", "choose a tolerance", "golden/regression corpus for audio", "measure drift or jitter", "A/B two renders" — AND on DEBUGGING work — "is there sound / no audio / I hear nothing", "does this filter/compressor/synth/delay produce the right signal", "prove the DSP / prove the contract", "measure the frequency response", "what's the THD / is it distorting", "what's the group delay / phase response / measured latency", "magnitude response curve", "render a test tone and assert", "audio regression", "64-frame works but 128 is silent", "sample-rate change pitch-shifted it", "describe what's in this buffer", "audio doctor", "compare before/after a DSP refactor". Reach for this BEFORE hand-rolling any FFT, null test, alias measurement, pitch tracker, or golden-render script — most of it already exists in one of the two lanes. Test/tool layer over HeadlessHost — deterministic, no audio device, no speakers. Off the realtime thread entirely.

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