pretty-page
Convert markdown to a beautifully styled, shareable HTML page and upload to S3-compatible storage
Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Convert markdown to a beautifully styled, shareable HTML page and upload to S3-compatible storage
Reviews scientific documents for logical clarity, argument soundness, and rigor by auditing hypothesis-data alignment, claim-evidence chains, quantitative precision, hedging calibration, and terminology consistency across any document type. Use when reviewing scientific argumentation, checking claims vs evidence, auditing terminology, or when user mentions check clarity, review logic, scientific soundness, hypothesis-data alignment, or claims vs evidence.
Verifies that an extracted statement is internally consistent by checking that opening_balance + sum(transactions) = closing_balance within a small tolerance, and produces a reconciliation report flagging missing rows, double-counted rows, sign errors, and rounding diffs. Use as the gate between PDF extraction and committing transactions to the data store, when reconciling a statement that does not balance, or when user mentions reconcile, balance check, or statement does not tie out.
Domain-neutral methodology for the second level of Adler-style reading - understanding what a document is about *as a whole* and how its parts relate. Classifies content (practical vs theoretical; sequential / categorical / structured / hybrid), states unity in one sentence, enumerates major parts and their organization, and defines the problems the document tries to solve. Reusable across any extraction workflow - skill creation from a methodology document, Pass-2 content grasp on an academic paper, structural review of a long-form document. Use when an agent has done inspectional reading and now needs to map structure before deeper component extraction. Trigger keywords - structural analysis, document structure, state unity, enumerate parts, Adler Level 2, content classification, define problems.
Take screenshots of the running pgconsole app for documentation. Use when updating docs with screenshots or adding images to .mdx files.
Detects and removes duplicate transactions across overlapping bank, credit-card, and brokerage statement imports using a stable composite key (account_id, date ±1d, amount_cents, description_normalized). Emits a list of new transactions to commit, a list of suppressed duplicates with their reasons, and a list of suspicious near-duplicates that need human review. Use when ingesting financial statements that may overlap prior drops, merging multiple export sources for the same account, or when user mentions duplicate transactions, deduping a transaction file, or reconciling overlapping statements.
Use when publishing, replacing, auditing, or linking cctop promo/demo video assets through GitHub Releases, especially the non-latest media-assets release. Trigger for requests like "upload the launch video", "add another video asset", "replace the README demo video", "where should this video live", "update media-assets", or "generate the README AVIF preview". Covers stable asset names, release selection, AVIF preview generation, README/site links, and avoiding v* product-release tags. Do not use for video narrative/storyboard work; use video-storyboard for that.
AI-powered film creation assistant that transforms a single sentence or image into a complete 30-second film. Automatically generates screenplay with scenes, dialogue, and camera directions, then produces cinematic video. Use when user wants to create a movie, film, or video story from text or image input. Trigger phrases: '创作电影', '生成电影', 'film creator', 'make a movie', 'create a film'.
Multi-shot AI video generation pipeline with face identity consistency. Converts scripts or ideas into complete videos using character extraction, storyboarding, frame generation, and video assembly. 300 experiments validated, 70% face distance improvement. Use when the user asks to create a video from a script, story, idea, or wants multi-shot video with consistent characters.
Use when the user has a messy tabular data dump (CSV/TSV/parquet/Excel/JSON) and wants it iteratively cleaned to an inferred data contract — a checklist of deterministic pass/fail checks, not a quality score. A single agent profiles the table, synthesizes a per-column contract compiled into binary checks (types, nulls, duplicates, inconsistent categories, format/range violations, outliers), then applies one targeted transform at a time, keeping it only if it reduces its target check's violations with no regression and no guardrail breach. Stops deterministically when every check passes, every remaining check is an unfixable residual, or a budget is hit; emits a replayable pipeline and an auditable ledger. Not for open-ended analysis of an already-clean dataset, diagnosing one known anomaly, or verifying a claim against sources — those are analytical loops; this rewrites the data to a contract.