sdd-dev
無料Spec-Driven Development workflow — enforces sdd/ folder hierarchy with planning-first gates, current-state artifacts, and completion 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。
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概要と使いどころ
Spec-Driven Development workflow — enforces sdd/ folder hierarchy with planning-first gates, current-state artifacts, and completion 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。
Alias for sdd-dev — Spec-Driven Development workflow
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。
Disciplined goal-to-execution workflow for any user task. Parses objective, asks only for materially missing requirements via ambiguity-gate, inspects repo via idea, plans via sdd-dev or deep-plan, executes safely under project conventions, verifies completion per R020, and reports changed files with evidence. Use when user invokes /goal <task>.
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。
YAML-based DAG workflow engine with topological execution and failure strategies
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。
Routes development tasks to the correct language or framework expert agent. Use when user requests code review, implementation, refactoring, or debugging.
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。
Spin up a coordinated multi-agent swarm (hierarchical or mesh topology) to work a complex, multi-file task on a ruOS desktop. Use when a task needs 3+ agents working in parallel with anti-drift coordination; skip for single-file edits.
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。
Monitor reviewed Grafana telemetry and freshness evidence for IoT devices and edge fleets. Use for smart devices, home automation sensors, remote equipment, wind turbines, space hardware, device-fleet freshness, missing telemetry, and bounded edge-health comparisons.
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。
Suggests manual context compaction at logical intervals to preserve context through task phases rather than arbitrary auto-compaction.
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。
Reddit automation skill collection. Supports authentication, content publishing, search & discovery, social interactions, and compound operations. Triggered when a user asks to operate Reddit (post, search, comment, login, analyze, upvote, save).
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。
Archive a completed change in the experimental workflow. Use when the user wants to finalize and archive a change after implementation is complete.
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。
Implement tasks from an OpenSpec change. Use when the user wants to start implementing, continue implementation, or work through tasks.
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。
Check and install prerequisites for the Todo app project. Use when setting up the development environment, when a user first opens the workspace, when troubleshooting missing tools, or when the user mentions setup, install, prerequisites, or environment.
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 friction you just hit into a concrete change to the workflow, so the same problem doesn't recur.
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。
Write the spec before the code, then check the built thing against it — so agents build what was agreed, not what they invented.
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。
Branch and commit conventions that keep an agent's work reviewable — one branch per task, main stays clean, nothing force-pushed.
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。
Triage issues through a state machine driven by triage roles. Use when user wants to create an issue, triage issues, review incoming bugs or feature requests, prepare issues for an AFK agent, or manage issue workflow.
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。
Run independent, task-matched expert attempts, select a base, and synthesize the strongest result.
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。
Implement tasks from an OpenSpec change (Experimental)
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。
Archive a completed change in the experimental workflow
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。
Archive a completed change in the experimental workflow. Use when the user wants to finalize and archive a change after implementation is complete.
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。
Implement tasks from an OpenSpec change. Use when the user wants to start implementing, continue implementation, or work through tasks.
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。
Call this skill when faced with "wicked problems", complex challenges, or when a user asks for strategic innovation, root-cause analysis, or long-term problem solving. Do not use for simple, linear tasks. This skill forces the agent to bypass superficial symptoms, map the entire ecosystem, and anticipate second-order consequences before proposing an intervention.
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。
Call germline SNPs and indels with GATK HaplotypeCaller and the GVCF joint-genotyping workflow. Covers the local-reassembly + PairHMM mechanism (why HC beats pileup callers on indels), the -ERC GVCF reference-confidence model and <NON_REF> allele, BQSR-vs-DRAGSTR and --dragen-mode error modeling, allele-specific (AS_) annotations, and edge cases (ploidy, Mutect2 mitochondria mode, sex chromosomes/PAR, contamination gating). Use when deciding whether to use HaplotypeCaller vs a pileup or DRAGEN caller, whether BQSR still earns its place, whether to call per-sample GVCFs for a cohort, or how to handle non-diploid, mitochondrial, sex-chromosome, or contaminated samples. Not for post-calling filtering depth (see variant-calling/filtering-best-practices) or cohort joint-genotyping scaling (see variant-calling/joint-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。
Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splice-aware mapping, samtools sort/index, IP/input matched-pair tracking, antibody-lot metadata recording, replicate concordance via deepTools multiBamSummary + plotCorrelation, IP enrichment QC via plotFingerprint and per-transcript IP/input ratio distributions, library-complexity saturation curves via PreSeq, and the explicit do-NOT-deduplicate convention for standard non-UMI MeRIP. Use when preparing paired IP and input BAM files for exomePeak2 / MeTPeak / MACS3 peak calling, evaluating MeRIP replicate concordance and IP enrichment, deciding whether to deduplicate (standard MeRIP typically NOT), choosing genome-vs-transcriptome alignment for downstream peak vs m6Anet workflows, recording antibody clone and lot metadata for cross-batch reconciliation, detecting failed IPs via saturation curves and IP/input distribution shape, or generating IP-over-Input bigWig tracks for visualisation.
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。