287 skills · sorted by relevance
Create detailed specifications by iteratively clarifying unclear points for Plan mode. Use when: After completing a plan when detailed requirements need clarification before implementation.
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Delegate coding tasks to Codex, Claude Code, or Pi agents via background process. Use when: (1) building/creating new features or apps, (2) reviewing PRs (spawn in temp dir), (3) refactoring large codebases, (4) iterative coding that needs file exploration. NOT for: simple one-liner fixes (just edit), reading code (use read tool), thread-bound ACP harness requests in chat (for example spawn/run Codex or Claude Code in a Discord thread; use sessions_spawn with runtime:"acp"), or any work in ~/clawd workspace (never spawn agents here). Claude Code: use --print --permission-mode bypassPermissions (no PTY). Codex/Pi/OpenCode: pty:true required.
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Iterative deep planning with critiques and alternatives. Use when facing complex design decisions, cross-platform changes, native module modifications, or API changes requiring thorough analysis.
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Iterative Python via live Jupyter kernel (hamelnb).
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Knowledge base from the Expanded Guidance for NASA Systems Engineering (NASA/SP-2016-6105-SUPPL, Volume 1, March 2016) — the practitioner-depth supplement to the NASA SE Handbook. Use for the load-bearing mechanics the handbook compresses: the SE Engine's per-phase cadence and the iterative-vs-recursive distinction, the AS9100 crosswalk, life-cycle tailoring vs. customization and the Compliance Matrix, the recursive system-design consistency loop (ConOps vs. Operations Concept, requirement flow/type/ownership, successive refinement), product realization (verify/qualify/accept/certify cardinality, protoflight, 'test the way we fly' coverage, two-form transition), and the crosscutting technical-management procedures (six-step scheduling, cost-estimating method selection, JCL, interface document family, RIDM/CRM, CM baselines, EVM arithmetic, MOE/MOP/KPP/TPM, the three required leading indicators, and the decision-analysis method catalog). This is the DEPTH layer — it deliberately does NOT restate the base definitions; pair it with the nasa-se-handbook pack. Scope: NASA space-flight SE practice (Vol 1 Practices); thin on the Vol 2 process-appendix detail, on non-NASA/commercial life cycles, and on software-engineering methodology.
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8阶段开发周期：功能需求、现状检查、功能规划、前端实现、后端规划、后端代码、测试与用例。系统化软件开发的迭代框架。
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Ciclo de desarrollo de 8 fases: solicitudes de funciones, estado actual, planificación funcional, frontend, planificación de backend, código de backend, pruebas, casos de uso. Marco iterativo para el desarrollo sistemático de software.
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8-фазный цикл разработки: запросы функций, текущее состояние, функциональное планирование, фронтенд, планирование бэкенда, код бэкенда, тесты, юзкейсы. Итеративный фреймворк для систематической разработки ПО.
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8フェーズの開発サイクル：機能リクエスト、現状確認、機能計画、フロントエンド、バックエンド計画、バックエンドコード、テスト、ユースケース。体系的なソフトウェア開発のための反復フレームワーク。
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Große Kandidatenlisten durch optionale Themen-Pfützen, Filterstufen und wiederholt neu gemischte Bündel schrittweise reduzieren. Nutzen, wenn der User Skills, Aufgaben, Ideen, Dateien oder andere Einträge gruppenweise auswählen, nach Merkmalen filtern oder nur innerhalb gemeinsamer Themenbereiche mischen will, ohne übrige Einträge endgültig zu verwerfen.
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Start, inspect, or continue a persistent SIPS self-improvement loop. Use when the user asks for /selfloop, wants the agent to iteratively improve itself, or wants a goal dedicated only to SIPS and agent capability.
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Iteratively improve a Claude Code skill using the skill-reviewer agent until it meets quality standards. Use when improving a skill with multiple quality issues, iterating on a new skill until it meets standards, or automated fix-review cycles instead of manual editing.
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Comprehensive investigation of complex multi-domain narratives (technology, theology, policy, finance). Systematically decouples factual claims from speculation through iterative verification, multi-source comparison, and contradiction resolution. Use for investigations requiring institutional knowledge, historical context, and handling of conflicting incentives.
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Deep, iterative internet research for current facts, source triangulation, contradiction checks, due diligence, competitive analysis, claim verification, "latest" lookups, and cited reports. Use when the user asks to browse, search, research the web, verify claims, compare sources, gather primary sources, investigate companies, people, products, technical topics, news, events, markets, or create AKBP-ready durable research evidence.
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Run iterative IA deepening, finalize shard, update indexes/tracker, and enforce ambiguity gates before handoff
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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.
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Systematic writing framework for philosophy and interdisciplinary academic papers from optimized outline to submission-ready manuscript. Use when users want to: (1) write a paper from a detailed outline, (2) ensure quality control during writing, (3) maintain consistency across chapters, (4) prepare a submission-ready manuscript, or (5) systematically execute a planned paper. Triggered by phrases like 'write the paper from this outline,' 'compose the full manuscript,' 'execute the outline,' or when users have completed strategic planning (academic-paper-strategist skill) and are ready to write. Takes optimized outline as input; outputs complete manuscript with iterative quality checks.
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Run an iterative Stitch website-building loop using `.stitch/next-prompt.md`, SITE.md, DESIGN.md, generated pages, and verification checkpoints.
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Convergence loop that iteratively improves work until quality thresholds are met. Scores, diagnoses specific weaknesses, rewrites, re-scores, and repeats until all dimensions reach 8/10 or diminishing returns are detected. Use when you want to refine or iterate on work, after the analyst gives a REVISE verdict, or when output quality is below your bar. Part of the architect-system loop. Outputs to system/refinery-log.md.
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Convergence loop that iteratively improves work until quality thresholds are met. Use when: (1) "refine this", "make this better", "iterate on this", (2) After /analyst gives a REVISE verdict, (3) Output quality is below 8/10, (4) "run the refinery loop on this". Reads analyst reviews from system/reviews/. Generates, scores, diagnoses, rewrites, re-scores. Stops when all dimensions >= 8/10 or diminishing returns detected. Outputs to system/refinery-log.md.
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Iterative session history with timestamped checkpoints and LATEST.md symlink
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Iterative Python via live Jupyter kernel (hamelnb).
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Create, synthesize, and iteratively improve agent skills following the Agent Skills specification. Use when asked to "create a skill", "write a skill", "synthesize sources into a skill", "improve a skill from positive/negative examples", "update a skill", or "maintain skill docs and registration". Handles source capture, precision passes, authoring, registration, and validation.
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Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates. Use when asked to improve a prompt, optimize a system prompt, rewrite an agent prompt, tune prompt wording, make a prompt more reliable, port prompts between OpenAI, Claude, or Gemini, or build prompt evals.
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