「pilot」の検索結果

Manage Spec Kit presets via `specify preset`. USE FOR: listing installed presets as text or JSON, installing/removing presets, searching the preset catalog, showing preset info, resolving which template a preset name maps to, enabling/disabling presets, setting resolution priority, or managing preset catalogs. DO NOT USE FOR: full artifact composition stacks (use speckit-artifact), extensions (use speckit-extension), bundles (use speckit-bundle), or workflows (use speckit-workflow).

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SEO & GEO (Generative Engine Optimization) for websites. Analyze keywords, generate schema markup, optimize for AI search engines (ChatGPT, Perplexity, Gemini, Copilot, Claude) and traditional search (Google, Bing). Use when user wants to improve search visibility, search optimization, search ranking, AI visibility, ChatGPT ranking, Google AI Overview, indexing, JSON-LD, meta tags, or keyword research.

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Cognitive copilot for people with ADHD. Use this skill whenever someone mentions paralysis, can't start a task, feels overwhelmed, needs to organize their day, is procrastinating, doing a brain dump, wants to plan their week, or says anything like "I don't know where to start", "I have too much on my plate", "I can't focus", "I'm stuck", or "I finished X but can't start Y". Also triggers on general focus and productivity requests — don't wait for the user to explicitly say "ADHD". If someone seems stuck, scattered, or overwhelmed, this skill applies.

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Find and address all unresolved GitHub PR review comments on the current branch's pull request. Use when the user asks to resolve, address, respond to, or reply to PR review feedback — whether from humans, CodeRabbit, Copilot, Claude Code reviewer, or any other source. Triggers on "/rr", "resolve review comments", "address PR feedback", "reply to review threads", "deal with CodeRabbit", etc.

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Meta-skill that orchestrates the discourse skill suite: routes between steel-man-argument, socratic-questioning, toulmin-argument-analysis, logical-fallacy-detector, bad-faith-rhetoric-detector, discourse-elision-analyzer, forensic-speech-structure, and productive-discourse-facilitator. Use when analyzing conversations, facilitating discourse, or operating as a dialogue copilot. Maintains mode (silent analysis, coaching overlay, active facilitator) and decides which skills to invoke based on conversation dynamics.

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Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.

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Prices research, tests, surveys, dashboards and experiments before running them, by computing how much the result would raise expected utility - which is zero whenever no outcome would change the decision. Use when someone proposes an A/B test, user survey, market study, competitor analysis, analytics build, consultant, pilot, or "let's gather more data", and when deciding how large a study needs to be.

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Subagent flow for deep-reading GitHub issues + their linked/closing PRs to the bottom. Captures CURRENT state, maintainer status updates, reporter repro, blocking reviewer concerns, hidden second bugs, cross-repo duplicates. Invoked by weekly-report orchestrator; runs as a general-purpose subagent so the orchestrator's context stays small.

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Build the locked Slack TL;DR JSON payload from a completed weekly report. Saves to /tmp/slack-msg.json with the exact curl command for Nathan to post manually. Triggers on "draft Slack TL;DR", "Slack post", "build the Slack message".

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After a weekly report is built, diff it against the PRIOR report to find items that appear in BOTH weeks, then print the Owner (and prospect "Passed to") value recorded last week so it can be carried forward. Print-only — never edits the owner fields; Nathan verifies and re-tags in Notion. Invoked by weekly-report near the end, and standalone on "carry forward owners", "who owned this last week", "owner continuity".

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Deep enrichment for community-sourced enterprise PROSPECTS (the 🎯 Prospective enterprise customers subsection). For each prospect, resolve their LinkedIn from their GitHub self-linked accounts FIRST (search only if none is self-linked), verify the employer matches, capture their full name, then list the company website + company size (ARR / latest funding round / employee count). Runs as a general-purpose subagent (many web searches) so the orchestrator's context stays small. Invoked by weekly-report (enterprise section) and the enterprise skill.

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P0 classification rules for community reports. Six categories that auto-headline regardless of reporter count. Reference doc — invoked by weekly-report when classifying.

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Subagent for GitHub author enterprise enrichment. Runs gh api users/<login> for each unique handle and classifies enterprise vs indie. Invoked by weekly-report or enterprise skills; runs as Explore subagent so the orchestrator's context stays small.

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Builds the "Report Sources" child page for a finished Weekly Community Signal report — an evidence-backed defense of WHY every item landed in the column/section/rank it did. The agent argues each placement like a lawyer (claim → evidence → rule → rebuttal) but is under oath: it CANNOT invent, spin, or overstate — every claim ties to a verifiable fact (issue #, label, PR type, quoted comment, npm output, the front-door test). Not CYA — it's so Nathan can defend the report with facts and see the exact reasoning behind each call. Runs after the report (invoked by weekly-report, last steps) and on "report sources", "defend the report", "why is this here".

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Mint a new YYYY-MM-DD-exact-coding-baseline snapshot under research/workflow-dev/export/. Detects the current best correctness- oriented workflow from research/workflow-dev/workflow-construction.md (or takes an explicit source-workflow argument) and transforms it from a lab workflow into a consumer-ready one on three axes: strips lab-only measurement content, re-enables human-in-the-loop checkpoints, and converts auto-loading config into an explicitly-invoked skill. Supports both promoted EXACT Coding lines: Opus/Hybrid and SOL/Predictive TDD. Exports Claude Code, pi, OpenCode, cursor-agent and, for SOL, Copilot. Trigger when the user says "exact-coding baseline export", "neue exact-coding baseline", "exact-coding-baseline-export", or asks to refresh the baseline snapshot.

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Use when reading, creating, or updating agent documentation, module docs, roadmaps, or AGENTS.md. Understands the full .augment/, agents/, and copilot-instructions structure.

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Platform-specific AI search optimization — audit and optimize for Google AI Overviews, ChatGPT, Perplexity, Gemini, and Bing Copilot individually

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Semantic audit of the IDD instruction corpus for leaked session context, cross-file contradictions, fresh-memory completability gaps, automation blockers, and restatement-discipline drift. Use on request to audit .github/instructions, the issue-authoring skill bundle, and the installed agent entry files (CLAUDE.md, AGENTS.md, GEMINI.md, .github/copilot-instructions.md). Read-only — never edits files or mutates issues.

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Use when committing or pushing completed work; automates gate-aware conventional commits with optional signing and CI/CD checks.

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Use after intent is known to route build, fix, review, debug, docs, or Scrum requests into ordered workflow steps with exit criteria.

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Create an atomic PR closing a GitHub issue end-to-end via a 6-step pipeline. Use to resolve, fix, or implement issue #N. Don't use for analysis without fixing (/issue-analysis), reviewing a PR (/issue-pr-review), or bulk backlog work (/auto-pilot).

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AI agent integration layer for the Pump SDK — agent instruction files, .well-known discovery, LLM context documents, 15+ skill files, MCP server prompts, and terminal management rules for GitHub Copilot and Gemini.

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Knowledge base from the DoD Manufacturing Readiness Level (MRL) Deskbook (Version 2022, OSD ManTech / Joint Service–Industry MRL Working Group). Use for assessing and managing manufacturing maturity and producibility risk in defense acquisition: the 10-level MRL scale and its non-linear, target-not-grade nature; the five demonstration environments (laboratory → production-relevant → production-representative → pilot line → production line); the nine manufacturing-risk threads and sub-threads; how MRL targets map onto Adaptive Acquisition Framework milestones (MRL 4/6/7/8/9); conducting an MRL Assessment; building a Manufacturing Maturation Plan; putting MRL maturity into contract language (Section L/M, SOW, AS6500A, DIDs); tailoring criteria for S&T, single-system, ship, sustainment, and industry contexts; and screening operational-technology cybersecurity. Manufacturing-readiness focused — it measures producibility risk and frames mitigation; it is NOT a technology-readiness (TRL) guide, a full systems-engineering process model, a cost-estimating or schedule guide, or a deep cybersecurity audit method (see sibling packs for those).

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Knowledge base from the DoD Digital Engineering Strategy (2018, OUSD/ODASD(SE)). Use for the Department of Defense's five digital engineering goals — (1) formalize the development, integration, and use of models; (2) provide an enduring authoritative source of truth; (3) incorporate technological innovation; (4) establish supporting infrastructure and environments; (5) transform the culture and workforce — plus their focus areas, the document-to-model and design-build-test→model-analyze-build shifts, model formalisms/provenance, governance and access control of the authoritative source of truth, and the coordinate→plan→pilot→sustain rollout. A vision-and-policy strategy, deliberately non-prescriptive. Does NOT provide a how-to method, tool tutorials, MBSE/SysML mechanics, or implementation-plan content; thin on metrics, acquisition-policy detail, and step-by-step procedure. Excludes the CAC-gated Digital Engineering Body of Knowledge (DEBoK).

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