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Found 108 Skills
Computational provenance audit verifying every number, table, and figure in a manuscript derives from code, not manual entry. Triggers on: "check provenance", "verify reproducibility", "audit my pipeline", "are my numbers from code", "provenance audit". Companion to manuscript-review (prose audit).
Track and reconcile taxonomy updates across NCBI, GTDB, ICTV, and community eukaryote frameworks with versioned provenance.
Verify witness chain integrity and detect provenance gaps
Use for file-driven multimodal image, video, and 3D-asset discovery; taste interviews; distill or apply workflows; style-pack validation; editable EDL/FCPXML export; provenance audits; and offline planning that must fail closed before provider generation.
Map computational materials tasks onto workflow engines such as atomate2, jobflow, AiiDA, pyiron, or a simple one-off script. Use when deciding how to structure a reproducible campaign, DAG, restart strategy, provenance record, storage layout, or migration path from ad hoc scripts to managed workflows.
Run a thorough, source-heavy investigation on any topic. Use when the user asks for deep research, a comprehensive analysis, an in-depth report, or a multi-source investigation. Produces a cited research brief with provenance tracking.
Create an original, provenance-safe Peekling character pack from a description and optional references. Use for authoring, compiling, validating, or reviewing native character art, not for copying third-party designs.
Decompose complex features or settled designs into an acyclic directed graph of independently verifiable vertical slices with explicit parent provenance.
Add or extend a Paperclip Runner protocol evaluation definition, roster, assertion, or report fixture with provenance and narrow validation.
Provider-independent governance workflow for verifying and correcting human- or model-generated video descriptions. Use for pre-caption critique and post-caption revision, aspect-by-aspect factual review, critique precision/recall/constructiveness, second-stage peer review, calibration, appeals, versioned triplets, provenance, and acceptance reporting; not for pixel/audio QA, accessibility captioning, model training, or creative shot direction.
Clean invisible Unicode, zero-width and tag characters, exotic whitespace, and textual AI provenance/watermarks from text the user owns or is authorized to use, and provide a best-effort stylometric rewriting process. Supports Traditional Chinese, Simplified Chinese, English, Markdown, HTML, and plain text. Use when users say “remove text watermarks,” “remove Claude watermarks,” “clean invisible characters,” “remove AI provenance,” or request an inspection for textual markers. Text watermarks are distinct from file C2PA/EXIF metadata and visible image watermarks; this skill does not directly handle the latter two.
Compute a business's CAC and ROAS family metrics from real spend and outcome data, then judge whether the spend is healthy against three references - the business's own break-even, its own trailing history, and provenance-labelled external benchmarks - returning healthy, watch, unhealthy, or insufficient evidence. Use whenever the user asks whether their CAC is too high, whether a ROAS is good, what ROAS to aim for, whether ads are profitable, or mentions MER, blended CAC, payback period, or a spend health check - even if they never say 'benchmark'. Covers B2B/SaaS (cost per SQL, cost per closed-won, cohort lag) and B2C/ecommerce. Do NOT use to set those thresholds as policy - use mbfinotti/advertising-skills@ad-spend-guardrails instead.