「iter」の検索結果

Negotiates a file-based sprint contract between generator and evaluator before code is written: turns a spec into testable acceptance criteria with rubric tie-ins.

日本語の概要は準備中です。原文の説明を表示しています。

Loads, lists, composes, or validates graded evaluation rubrics: PASS/WARN/FAIL grading with weighted criteria and balanced anchor examples for evaluator-style review.

日本語の概要は準備中です。原文の説明を表示しています。

Autonomously improve one measurable metric via bounded, git-tracked iterations: one scoped change, verify, keep or revert. NOT for cleanup (mk:cook), known-cause bugs (mk:fix), or shipping (mk:ship).

日本語の概要は準備中です。原文の説明を表示しています。

Read-only spec analysis on a Confluence page (+children): requirements, acceptance criteria, gaps, user stories report. NOT page CRUD (mk:confluence-page) or ticket scoring (mk:planning-engine).

日本語の概要は準備中です。原文の説明を表示しています。

Systematically QA test a web application and fix bugs found. Runs QA testing, then iteratively fixes bugs in source code, committing each fix atomically and re-verifying. Use when asked to "qa", "QA", "test this site", "find bugs", "test and fix", or "fix what's broken". Proactively suggest when the user says a feature is ready for testing or asks "does this work?". Three tiers: Quick (critical/high only), Standard (+ medium), Exhaustive (+ cosmetic). Produces before/after health scores, fix evidence, and a ship-readiness summary.

日本語の概要は準備中です。原文の説明を表示しています。

Deep spec analysis on a Confluence page. Reads page (+ children) via the confluence-as wrapper, produces a structured Spec Research Report with requirements, acceptance criteria, gaps, ambiguities, and suggested user stories for human review. Read-only — does NOT create Jira tickets. Triggers: 'analyze spec for PAGE-ID', 'extract requirements from confluence page', 'spec research on URL'. NOT for raw page CRUD (mk:confluence-page); NOT for ticket complexity scoring (mk:planning-engine).

日本語の概要は準備中です。原文の説明を表示しています。

Create aesthetically beautiful interfaces following proven design principles. Use when building UI/UX, analyzing designs from inspiration sites, generating design images with ai-multimodal, implementing visual hierarchy and color theory, adding micro-interactions, or creating design documentation. Includes workflows for capturing and analyzing inspiration screenshots with chrome-devtools and ai-multimodal, iterative design image generation until aesthetic standards are met, and comprehensive design system guidance covering BEAUTIFUL (aesthetic principles), RIGHT (functionality/accessibility), SATISFYING (micro-interactions), and PEAK (storytelling) stages. Integrates with chrome-devtools, ai-multimodal, media-processing, ui-styling, and web-frameworks skills.

日本語の概要は準備中です。原文の説明を表示しています。

Worker-Reviewer iterative pipeline for quality-critical code with review cycles

日本語の概要は準備中です。原文の説明を表示しています。

Clarify the brief BEFORE any design work to avoid building the wrong thing. Interrogates goal, user, constraints, success criteria, and scope, then writes a short context doc the other design skills consume. Use when the user says "let's design X", "build a UI for Y", "get context", "clarify the brief", or kicks off any product-design task without a clear spec. First stage of the Product Design set — highest leverage for reducing rework.

日本語の概要は準備中です。原文の説明を表示しています。

Turn a reference image or mockup into a working local frontend that matches it closely. Inspects the image, extracts the design system (layout, type, spacing, color, components), implements it, then screenshot-compares and iterates to close the gap. Use when the user says "image to code", "build this mockup", "code this screenshot", "make this design real", or provides a UI image to implement. Part of the Product Design set. Uses a browser tool for screenshot verification.

日本語の概要は準備中です。原文の説明を表示しています。

Reflect subskill for simmer. Records iteration results in trajectory table, tracks best candidate, handles regression rollback, and passes ASI forward to the next round. Supports both single-file and workspace modes. Do not invoke directly — called by simmer orchestrator after each judge round.

日本語の概要は準備中です。原文の説明を表示しています。

Judge subskill for simmer. Scores a candidate artifact against user-defined criteria on a 1-10 scale and produces ASI (highest-leverage direction) for the next generator round. Supports judge-only, runnable evaluator, and hybrid evaluation modes. Do not invoke directly — dispatched as a subagent by the simmer orchestrator.

日本語の概要は準備中です。原文の説明を表示しています。

Use when splitting a domain Skill into Phase-Aware File Structure (SKILL.md / design.md / constraints.md). Defines the splitting criteria, procedures, sync procedures, and validation rules. Invoked by /skill-format command or manually when restructuring domain Skills.

日本語の概要は準備中です。原文の説明を表示しています。

Pattern for progressively refining context retrieval to solve the subagent context problem

日本語の概要は準備中です。原文の説明を表示しています。

Up-to-date Zig programming language patterns for version 0.17.0-dev (0.16.0 stable APIs plus the 0.17 deltas). Use when writing, reviewing, or debugging Zig code, working with build.zig and build.zig.zon files, or using comptime metaprogramming. Critical for avoiding outdated patterns from training data - especially std.net→std.Io.net (requires Io instance), std.time timestamps removed (use clock_gettime), std.Thread.Mutex/Condition/sleep removed (use pthreads), std.crypto.random removed, build system APIs (root_module, Compile methods→Module methods), I/O APIs (buffered writer pattern), container initialization (.empty/.init), allocator selection (DebugAllocator), ArrayList now unmanaged by default, @typeInfo lowercase fields (.@"struct" not .Struct), and removed language features (async/await, usingnamespace). Also covers 0.17.0-dev deltas: b.args→run_cmd.addPassthruArgs(), std.gpu→std.spirv, @bitCast logical-bit (endian-agnostic) semantics, build configurer/maker split, and the zig-pkg/ package directory. Also covers quality tooling Zig does not ship: coverage with kcov (and why in-file tests wreck the denominator), detecting unused private functions (neither the compiler nor zlint's unused-decls finds them), duplicate detection with jscpd, zlint (NOT the same-named X.509 linter in Homebrew), and the built-in fuzzer whose testOne now takes *std.testing.Smith rather than []const u8.

日本語の概要は準備中です。原文の説明を表示しています。

Runnable end-to-end validation playbook for the Rembric TUI installer — catch breakage before merging/deploying, AND a sandboxed manual walkthrough to evaluate/iterate its UI/UX without polluting your real env. Apply before shipping any change touching `apps/plugin/install.sh`, the repo-root `install.sh` shim, any per-client `install.sh`/`uninstall.sh`, or distribution docs, or when you want to preview/try/demo the installer and polish how it looks and feels. Layered: CI-safe headless (vitest + `sh -n`), local install round-trips, operator interactive pty smoke, an optional full Docker `up` smoke, plus a sandboxed manual UX walkthrough. For the installer contract/what-not-to-break, see `rembric-tui-installer`.

日本語の概要は準備中です。原文の説明を表示しています。

Select colormaps and qualitative palettes for scientific figures using perceptual-uniformity, color-vision-deficiency safety, and luminance-monotonicity criteria. Covers Crameri scientific colormaps, viridis/cividis/magma, Okabe-Ito categorical, ColorBrewer, and the rainbow/jet critique. Use when choosing palettes for heatmaps, scatter, networks, or any encoding where color carries quantitative or categorical meaning.

日本語の概要は準備中です。原文の説明を表示しています。

Build interactive HTML/web visualizations with plotly (Python/R), bokeh (Python), and gganimate/plotly frames for animation, with awareness of current Kaleido static-export model (post-orca-EOL), HTML file-size bloat, and the limits of interactive-only output for journal submission. Use when producing zoomable/hoverable plots for notebook EDA, supplementary HTML, dashboards, or animated time-course / iteration visualizations.

日本語の概要は準備中です。原文の説明を表示しています。

Classify constitutional (germline) copy number variants for clinical reporting using the 2019 ACMG/ClinGen technical standards points-based framework, with ClassifyCNV and AnnotSV for semi-automated scoring. Covers the separate copy-number-loss and copy-number-gain rubrics, the five-tier classification, ClinGen haploinsufficiency/triplosensitivity and dosage-sensitive regions, de novo and segregation evidence, and population-frequency benign evidence. Use when assigning pathogenic/likely-pathogenic/VUS/likely-benign/benign to a constitutional CNV, scoring a CNV against ACMG/ClinGen criteria, or distinguishing the automatable evidence from the case-specific evidence requiring manual input.

日本語の概要は準備中です。原文の説明を表示しています。

Process many sequence files in batch (count, merge, split, convert, summarize) with memory-safe streaming and on-disk indexing using Biopython, pysam, or pyfastx. Use when iterating over a directory of FASTA/FASTQ files, merging or splitting datasets, building random access across many or huge files, or automating per-file operations without exhausting RAM.

日本語の概要は準備中です。原文の説明を表示しています。

Search scientific papers via the BGPT MCP server and retrieve structured experimental data — methods, results, conclusions, quality scores, and 25+ metadata fields per paper.

日本語の概要は準備中です。原文の説明を表示しています。

Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.

日本語の概要は準備中です。原文の説明を表示しています。

ATAC-seq library quality control -- TSS enrichment, FRiP, fragment-size periodicity, library complexity (NRF/PBC1/PBC2), mitochondrial fraction, and ENCODE 4 thresholds. Use when assessing whether an ATAC-seq library passes ENCODE acceptance criteria, diagnosing transposition artefacts, comparing Omni-ATAC vs standard prep quality, or selecting which replicates to drop before peak calling.

日本語の概要は準備中です。原文の説明を表示しています。

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.

日本語の概要は準備中です。原文の説明を表示しています。