Results for “deterministic”

Busabase App-in-Skill review queue that scores candidate SME financing deals (revenue-based/RBF-style credit) with a deterministic, fully auditable rule-based rubric — never an LLM or API call. Use when the user invokes $kelly-deal-scorer or /kelly-deal-scorer, wants to review a deal-underwriting queue, score financing candidates, compute a composite score breakdown, see a suggested revenue-share rate range, or record approve/send-back/reject decisions for a private-credit or RBF-style lending pipeline.

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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.

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Personal finance anomaly review desk (Busabase App-in-Skill) that imports the three business tables — orders, invoices, payments/receipts (回款) — from CSV/JSON exports, flags anomalies with deterministic rules, and runs a human review queue with auditable decisions. Use when the user invokes $kelly-audit or /kelly-audit, mentions finance audit, order invoice payment reconciliation, 对账, 应收, 回款, 发票异常, receivables aging, overdue receivables, duplicate payments, missing invoices, amount mismatches, or anomaly review.

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Run a Busabase-backed sandbox desk for scoring and triaging course-instructor ("讲师") candidates found on a recruiting-style platform. Use whenever the user invokes $kelly-instructor-sourcing or /kelly-instructor-sourcing, wants a lightweight instructor candidate tracker, needs to score sourcing-platform leads against a three-axis rubric (background endorsement, expertise depth/breadth, teaching-service ability) before reaching out, or wants to log a WeChat add without building a live-connected outreach tool. Three subcommands - setup, criteria, review - plus the desk itself. v1 is sandbox-only — every dataset is deterministic mock data, it never connects to a real sourcing-platform or messaging account, and it never sends a message or automates a WeChat add - that stays entirely human-performed outside the app.

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Busabase-backed App-in-Skill control panel / kanban board for a BD or sourcing team triaging merchant and business financing leads for a lender or investment fund. Use when the user invokes $kelly-lead-funnel or /kelly-lead-funnel, wants to review the deal sourcing pipeline, funnel, lead board, or asks to move a lead's stage, reject a lead, add a note, score a lead, or see funnel conversion rates. Deterministic rule-based lead scoring only — never an LLM call — and never sends outreach, signs term sheets, or moves money.

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Execute a single deterministic Python script to harmonize units, create/update metadata, move files to output_data/, and clean up input_data/.

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Execute a single deterministic Python script to compute all derived financial metrics (EBITA, Invested Capital, Tax Rates, Summary Table) from extracted financial data.

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Execute a single deterministic Python script to compute WACC, DCF assumptions, projected free cash flows, enterprise value, and intrinsic value per share. Updates the metadata markdown.

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Design a GitHub social-preview card (og:image, 1280x640) for a repository. Use when the user asks for a social preview, repo cover, og image, link card, opengraph image, or README hero. Five editorial moods (editorial, poster, blueprint, gallery, terminal), CJK-first typography, one self-contained HTML file, deterministic checks, crisp PNG export. No image model needed.

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当使用者说"把照片做成极简海报"、"做一张几何风格的海报"、"把这张照片抽象成海报"时使用。Turn a photographer-provided description of an existing photo into a minimal, geometric SVG poster with an explicit visual abstraction, restrained palette, grid, typography, bleed, and deterministic validation. Use when the user wants to reinterpret one of their photos as a stylized poster rather than retouch or reproduce the photo.

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Focused Signals scout for PostHog setup health. Reads the project's active health issues — the deterministic findings of PostHog's own health checks (no live events, outdated SDKs, missing reverse proxy, absent web vitals, ingestion warnings, failing data-warehouse models, and more) — and decides which are genuinely worth surfacing. Unlike a one-signal-per-issue push, it bundles kind-clusters into a single finding, weights by real blast radius (cross-referencing actual event volume and reach), and prioritizes issues an agent can resolve via the MCP. Emits only above the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.

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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).

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Plan and execute reproducible computational research with explicit inputs, environments, provenance, deterministic steps, validation, and workspace outputs. Use for analyses that must be rerun, audited, or handed to another researcher.

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Authors reproducible Nextflow DSL2 pipelines built on reactive dataflow, where processes communicate only through channels and execution order is not guaranteed. Use when deciding channel/dataflow (Nextflow) vs rule-based (Snakemake) authoring; wiring queue vs value channels and fixing shared-reference exhaustion with .first(); composing DSL2 modules and subworkflows with take/main/emit; selecting container/conda profiles and pinning images by digest for portability across local/SLURM/LSF/AWS Batch/Google Batch/Kubernetes executors; diagnosing why -resume misses the cache (nondeterministic input order, mtime on network filesystems, mutable :latest tags) with cache 'lenient' and -dump-hashes; managing work/ vs publishDir and dynamic retry escalation; and choosing whether to adopt an nf-core community pipeline or author from scratch.

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Reads and changes a running CARLA server's WorldSettings via apply_settings — synchronous vs asynchronous mode, fixed_delta_seconds (fps), physics substepping, and no_rendering_mode — and keeps the Traffic Manager's sync state matched automatically. Use when the user asks to "enable/disable sync mode", "set synchronous mode", "run at a fixed fps/step", "turn off rendering", "make the sim deterministic", or "restore async mode", and as the prerequisite for keeping settings across a map load.

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Records a running CARLA simulation to a .log file via start_recorder/stop_recorder for later deterministic replay — capturing actor spawns, transforms, traffic-light states and animations (optionally velocities/controls). Use when the user asks to "record the simulation", "start/stop the recorder", "capture this run", or "save a scenario to replay later". Does NOT capture sensor images.

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Inspect README portability, publication assets and command risks with deterministic evidence. Use when reviewing package documentation, preparing an npm release, or diagnosing first run instructions.

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Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution.

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Rebuild the vault dependency graph (_graph.md) via the deterministic generator script. /graph forces a full rebuild; /graph last (run after every /sync) rebuilds only if the vault changed; /graph [N] is treated as last. The generator reads the vault and writes _graph.md directly, so the file never streams through a model response.

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Rebuild the vault dependency graph (_graph.md) via the deterministic generator script. $graph forces a full rebuild; $graph last (run after every $sync) rebuilds only if the vault changed; $graph [N] is treated as last. The generator reads the vault and writes _graph.md directly, so the file never streams through a model response.

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Produce a deterministic, read-only evidence map of the repository for SDD Composy (languages, manifests, commands, modules, boundaries, staleness) and publish the context bundle only on request. Use before a PRD or TechSpec, or when the map is stale — 'mapear o repositório', 'atualizar o contexto do código', 'what does this codebase look like for SDD'. Do NOT use for architecture decisions, code review, or repository exploration without an SDD workspace.

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Use when an approved PRD must become an executable roadmap — 'gerar roadmap do PRD', 'quebrar o PRD em épicos e features', 'cadeia de dependências', 'roadmap with dependencies' — Phase/Epic/Feature/Task with explicit dependencies, waves and critical path, validated by a deterministic checker, with an approval gate. Do NOT use for a PRD that is not approved (prd- create/prd-refine first).

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Design deterministic CI quality gates that read the normalized manifest verdict independently from report-export process status.

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Use when the user wants the existing codebase analyzed for planning — 'mapear o código', 'analisar o projeto', 'atualizar o contexto', 'map codebase' — or when a planning skill reports a missing or stale map. Runs a deterministic read-only evidence scan and writes codebase.md plus the architecture, conventions, testing and concerns context documents under .planning/feat/. Do NOT use for code review (feat-review), architecture decisions, or writing the project instructions file (feat-setup).

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