Results for “compare”

Use after the backend follows the model — derive E2E flow scripts from Lean golden traces, replay them against the Lean-backed dev screens and the real REST backend with Playwright, and compare observations. Also use to add a flow when a new command or screen appeared.

The English summary is being prepared. Showing the original description.

Busabase-backed App-in-Skill control-panel/workspace for a deal analyst to model revenue-based-financing (RBF) deals for SME businesses (retail/F&B chain stores). Use when the user invokes $kelly-revshare-simulator or /kelly-revshare-simulator, wants to model a revenue-share or merchant-cash-advance deal, project cash flow and repayment, compute a Cash-Flow Payout Multiple, effective annualized merchant cost, or compare/underwrite multiple financing scenarios. Pure deterministic math, no external calls, no real trading/payment side effects. Scenario create/edit/delete and the underwriting decision are direct writes made by the analyst, not a review/approval queue.

The English summary is being prepared. Showing the original description.

Review board (Busabase App-in-Skill) that runs a fixed suite of mock test cases against a baseline vs candidate agent version and surfaces rubric-scored regressions before a release. Use when the user invokes $kelly-agent-eval or /kelly-agent-eval, wants to review agent-version regressions, compare baseline vs candidate quality, triage a release, or record a release approve/block decision. Deterministic mock rubric scores only — not a real LLM-judge call, and it never deploys anything.

The English summary is being prepared. Showing the original description.

Build and operate a Busabase-backed mainland China A-share strategy experiment desk with a bundled local Hono App-in-Skill, evidence-backed strategy research, strategy-level L1/L2/L3 manual maturity approvals, one CNY virtual ledger per strategy, and dated strategy backtest reports. Use when the user invokes $kelly-invest-stock or /kelly-invest-stock, wants to define or compare A-share strategies, inspect a strategy and its virtual book, record research evidence, manually approve strategy maturity, or review dated backtests, drawdown, and contribution to the total book. It never connects to a brokerage, places orders, moves money, or presents generated analysis as personalized investment advice.

The English summary is being prepared. Showing the original description.

Busabase App-in-Skill dashboard over a fixed, deterministic mock user-behavior funnel dataset (browse → search → compare → booking attempt/abandon → complete) for a generic consumer booking product. Use when the user invokes $kelly-behavior-predict or /kelly-behavior-predict, wants to review funnel drop-off, per-segment predicted next actions, or backtest a rule-based "predicted next action" heuristic against a mock historical sample. Fully deterministic mock data and a hand-recomputable rule — never a real ML/LLM model, never a live system.

The English summary is being prepared. Showing the original description.

Use when asked to map, crosswalk, align, compare, or gap-analyze any two cybersecurity frameworks, control catalogs, or regulatory requirements using NIST IR 8477 Set-Theory Relationship Mapping (STRM). Triggers on terms like "map controls", "crosswalk", "framework alignment", "gap analysis", or producing a STRM CSV output file.

The English summary is being prepared. Showing the original description.

Use after changing prompts, skills, the compiler, or editorial policies — or when the user asks to measure edit quality, run regression evals, check agreement with approved edits, or says '評価して', '一致率を測って', 'eval を回して'. Compares agent output against human-approved golden projects.

The English summary is being prepared. Showing the original description.

Use when an edited Premiere Pro FCP7 XML should be compared against timeline.json or imported back into the project.

The English summary is being prepared. Showing the original description.

当问「这条公式怎么来的」「为什么长这样」，或说「帮我推导／推出 X 公式」时使用。从定义或守恒律出发重建标准公式，每一步标出用的是哪条原理、这一步在物理上做了什么；假设写在它真正被用到的那一步，不堆在开头。终式可用 scripts/compare_formula.py 与标准式做符号比对。会被评分的推导：结构与理由可以给，代数由本人做。要逐步核验一份已写好的推导，用 derivation-step-checker。

The English summary is being prepared. Showing the original description.

照片电影感调色、低饱和冷暖分离、先保原片再调色。用确定性像素处理而不是生成式重绘；先确认用途与色彩输入，输出带 sRGB ICC 的新图和隐私优先的参数/QA 边车。支持 8-bit SDR JPEG/PNG 的 render、固定三选候选小样 candidates 与数值 QA，以及原尺寸前后对比 compare；card、LUT 尚未实现。不负责 RAW 显影、修图换景、冲印交付或精确复刻电影/胶片。

The English summary is being prepared. Showing the original description.

Map advanced persistent threat (APT) group tactics, techniques, and procedures (TTPs) to the MITRE ATT&CK framework using the ATT&CK Navigator and attackcti Python library. The analyst queries STIX/TAXII data for group-technique associations, generates Navigator layer files for visualization, and compares defensive coverage against adversary profiles. Activates for requests involving APT TTP mapping, ATT&CK Navigator layers, threat actor profiling, or MITRE technique coverage analysis.

The English summary is being prepared. Showing the original description.

Investigate AI observability clusters — understand usage patterns in AI/LLM traffic, compare cluster behavior, compute cost/latency metrics, and drill into individual traces within clusters.

The English summary is being prepared. Showing the original description.

Diagnoses CI and pull-request pipeline health for a GitHub repo using the engineering analytics MCP tools — pull-requests (PR list with CI status), workflow-health (per-workflow CI trends), and pr-lifecycle (a single PR's timeline). Use when asked whether CI is getting faster or slower, which GitHub Actions workflow is the slow or flaky long-pole, how long PRs take from open to merge, how an author's merge time compares to the cohort, which open PRs have failing or pending CI, or where a specific pull request is stuck. Triggers on "engineering analytics", "is CI getting slower", "slow workflow", "flaky CI", "time to merge", "cycle time", "PR throughput", "failing checks", "where is PR <n> stuck", "CI long pole", "what's holding up this PR".

The English summary is being prepared. Showing the original description.

Generate multiple radically different interface designs for a module using parallel sub-agents. Use when user wants to design an API, explore interface options, compare module shapes, or mentions "design it twice".

The English summary is being prepared. Showing the original description.

Investigate AI observability evaluations of both types — `hog` (deterministic code-based) and `llm_judge` (LLM-prompt-based). Find existing evaluations, inspect their configuration, run them against specific generations, query individual pass/fail results, and generate AI-powered summaries of patterns across many runs. Use when the user asks to debug why an evaluation is failing, surface common failure modes, compare results across filters, dry-run a Hog evaluator, prototype a new LLM-judge prompt, or manage the evaluation lifecycle (create, update, enable/disable, delete).

The English summary is being prepared. Showing the original description.

Create a PostHog endpoint with the right shape on the first try — covers query kind choice, name conventions, what to expose as variables (HogQL code_name vs insight breakdown), data_freshness_seconds, and whether to materialise on day one. Use when the user says "create an endpoint", "expose this query as an API", "turn this insight into an endpoint", or asks for help structuring a new endpoint. Steers away from common mistakes: materialising a query with cohort breakdowns or compare mode, inline-only variables on a materialised endpoint, unbounded date ranges, ambiguous names.

The English summary is being prepared. Showing the original description.

Analyze session replay patterns across experiment variants to understand user behavior differences. Use when the user wants to see how users interact with different experiment variants, identify usability issues, compare behavior patterns between control and test groups, or get qualitative insights to complement quantitative experiment results.

The English summary is being prepared. Showing the original description.

Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit

The English summary is being prepared. Showing the original description.

Dose-response / concentration-response curve fitting — IC50, EC50, Hill slope, Emax/Emin efficacy, and relative potency from paired concentration vs response data (enzyme/cell assays, drug screening, agonist/antagonist pharmacology). Fits the 4-parameter logistic (Hill sigmoidal) model. Use when you have concentrations + responses and need a potency value, to compare two compounds' potency, or to judge curve quality. NOT for image-derived dose-response (use tooluniverse-image-analysis) and NOT for survival/regression (use tooluniverse-statistical-modeling).

The English summary is being prepared. Showing the original description.

Find commercial sources for chemical compounds — PubChem/ChEMBL identity resolution then vendor catalog search across ZINC, Enamine, eMolecules, Mcule. Compares pricing, availability, and identifies purchasable analogs when an exact compound is not in stock. Use for chemical procurement, virtual library curation, and 'where can I buy X' questions for synthesis planning.

The English summary is being prepared. Showing the original description.

Standardizes molecular structures using ChEMBL chembl_structure_pipeline and RDKit rdMolStandardize covering sanitization, salt/solvent stripping, neutralization, tautomer canonicalization, stereochemistry standardization, mixture handling, and isotope normalization. Explicitly compares ChEMBL pipeline, canSARchem, and PubChem standardization choices. Use when preparing libraries for QSAR training, joining datasets across sources, deduplicating compound collections, or building canonical compound registries.

The English summary is being prepared. Showing the original description.

Performs alchemical free-energy calculations including relative binding free energy (RBFE / FEP+) and absolute binding free energy (ABFE) via OpenFE, FEP+, GROMACS, AMBER pmemd, and OpenMM with explicit lambda window scheduling, soft-core potentials, REST2 enhanced sampling, MBAR/BAR analysis, and cycle closure validation. Compares ML alternatives (Boltz-2 affinity, DeepDock). Use when ranking analogs by binding affinity beyond docking accuracy, performing prospective lead optimization, or validating SAR predictions.

The English summary is being prepared. Showing the original description.

Search academic literature and generate, validate, or compare Knows paper sidecars. Use for claim/evidence analysis and literature synthesis.

The English summary is being prepared. Showing the original description.

Reads and summarises ScenarioRunner output — the criteria pass/fail tables from --output/--file, the machine-readable --json and --junit result files, and the criteria JSON written alongside a --record recording — and runs the metrics module (metrics_manager.py) to compute custom measurements over a recorded run offline, without the simulator. Use when the user asks "did the scenario pass", "why did it fail", "summarise these results", "compare these runs", or wants distance/speed/lane metrics from a recording.

The English summary is being prepared. Showing the original description.