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cccskills

「iterate」の検索結果

195 件 ・ 関連度順

概要と使いどころ

Use when the user reports a bug they can't reproduce, asks where to start debugging, or mentions a Heisenbug / production-only failure. Drives the observe→hypothesize→predict→test→iterate scientific method.

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。

Tibsfox/gsd-skill-creator712026年7月20日 更新

Persist progress for long-running jobs (batched LLM calls, large ingestions, multi-hour syncs) so that a context reset, crash, or interrupt doesn't lose work. Use whenever a job iterates over N items and completing item K matters independently. Provides a resumable.mjs library pattern plus the skill's invocation heuristics.

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。

Tibsfox/gsd-skill-creator712026年7月20日 更新

mkdocs

無料

MkDocs documentation project reference covering CLI commands, mkdocs.yml configuration, Material theme setup, and plugin integration. Bundled references include complete CLI parameters, all mkdocs.yml settings with valid values, Material theme customization options, and plugin configs for mkdocstrings, mermaid2, mkdocs-gen-files, mkdocs-literate-nav, and mkdocs-typer2. Use when initializing a MkDocs site, configuring mkdocs.yml, customizing the Material theme, integrating plugins, building static docs from Markdown, or generating API documentation from Python docstrings.

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。

Jamie-BitFlight/claude_skills672026年10月13日 更新

Evaluate and iterate on the SDLC Layer Separation Architecture implementation. Runs validation checks (cross-references, doc completeness, layer metadata, integration points), produces a findings report, and supports iterative fixes. Use when validating first-pass implementation, before claiming layer work is complete, or when improving layer docs/schema.

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。

Jamie-BitFlight/claude_skills672026年10月13日 更新

Validate and iterate on the SDLC Layer Separation Architecture implementation across the check categories defined in its Evaluation Checklist — cross-references, doc completeness, knowledge-explorer layer filters, research entry metadata, integration points, and plan consistency. Produces a structured findings report and optionally applies safe fixes. Use when validating a first-pass implementation, before claiming layer work complete, auditing layer docs or schema, or running --dry-run to preview findings without changes.

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。

Jamie-BitFlight/claude_skills672026年10月13日 更新

Use when the user wants to run the optimization loop, improve agent performance, evolve the agent, or iterate on quality. Requires .evolver.json to exist (run harness:setup first).

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。

raphaelchristi/harness-evolver562026年4月18日 更新

QA validation and fix loop workflow — validates implementation completeness then iterates fix cycles until all acceptance criteria pass and quality gates clear

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。

oimiragieo/agent-studio432026年7月14日 更新

Step 5 of the PaperOrchestra pipeline (arXiv:2604.05018). Iteratively refine drafts/paper.tex by simulating peer review and applying targeted revisions, with strict accept/revert halt rules. Maintains a worklog and snapshots each iteration so revert is real, not symbolic. TRIGGER when the orchestrator delegates Step 5 or when the user asks to "refine the draft", "iterate on the paper", or "run peer review on this 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。

woodfishhhh/EZ_math_model422026年7月28日 更新

Run Claude Code in autonomous iteration loops until task completion — PRD-based feature development, test coverage improvement, lint fixing, documentation generation, dataset creation, or framework migrations. Uses in-session loop mechanism with specialized templates. Triggers on autonomous loop, iterate until done, run until passing, keep going until, ralph, PRD execution.

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。

tdimino/claude-code-minoan412026年9月28日 更新

autostar

無料

Generalised autonomous optimisation loop — soft RLVR for any artifact a user can measure. Use this skill whenever a user wants to iteratively improve an artifact — code, prompts, documents, configs, designs, content — by running structured experiments, evaluating results against a multi-dimensional rubric, and learning from each attempt. Triggers include: "optimise this", "keep improving until it's good", "run experiments on", "autoresearch", "iterate on this overnight", "try different approaches and pick the best", or any request implying repeated evaluate-and-improve cycles. Also use when the user wants to improve a system prompt, a data pipeline, a writing style, or any artifact where quality can be decomposed into measurable tracks. For inference optimisation tasks (model latency, throughput, quantization, GPU deployment), a* delegates the low-level tuning to AITune while maintaining quality tracking and learning.

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。

chrisvoncsefalvay/autostar392026年4月10日 更新

Generalised autonomous optimisation loop — soft RLVR for any artifact a user can measure. Web runtime package: uses memory in this order: connector-backed, project-pack, none. Never assumes subprocess access or unrestricted local files. Use this skill whenever a user wants to iteratively improve an artifact — code, prompts, documents, configs, designs, content — by running structured experiments, evaluating results against a multi-dimensional rubric, and learning from each attempt. Triggers include: "optimise this", "keep improving until it's good", "run experiments on", "autoresearch", "iterate on this overnight", "try different approaches and pick the best", or any request implying repeated evaluate-and-improve 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。

chrisvoncsefalvay/autostar392026年4月10日 更新

Optimize a pydantic-ai agent's instructions, tool descriptions, output type, and signature inputs using the gepa CLI. Trigger when the user asks to "optimize this agent", "improve tool descriptions", iterate on prompts driven by eval failures, or otherwise improve a pydantic-ai agent against a dataset. Operates in the user's repo with full filesystem access — YOU are the reflection model; the gepa CLI handles minibatches, evaluation, and Pareto bookkeeping.

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。

indexedlabs/pydantic-ai-gepa362026年10月7日 更新

Systematic methodology for achieving 100% backward dataflow reachability in a new language. Create gauntlet fixture, write trace, diagnose gaps, fix analyzer/algorithm, iterate to 100%. Language-agnostic process. Use when: (1) adding a new language to Grafema, (2) auditing dataflow coverage for existing language, (3) user says "/dataflow-gauntlet".

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。

Disentinel/grafema362026年8月24日 更新

Runs iterate-until-pass loops with ralph-worker agents in a worktree. Use when running [ralph]-marked tasks or looping until verify passes.

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。

tim-hub/powerball-harness352026年8月5日 更新

Multi-agent research that keeps bringing gifts back — like cats! Dispatch multiple agents to research a topic in parallel, compile findings, and iterate on new discoveries.

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。

sstklen/infinite-gratitude312026年3月16日 更新

Design and review autonomous or semi-autonomous agent loops — systems where an agent prompts itself, runs on a schedule or event, and iterates toward a goal without a human at the keyboard. Use this whenever the user wants to build, design, harden, or audit any kind of self-running agent, agentic workflow, control loop, background worker, cron/heartbeat/webhook-driven agent, "Ralph loop", auto-PR-fixer, monitoring agent, or any long-running task that should keep going on its own until done. Trigger this even when the user says "agentic workflow", "automation that runs itself", "an agent that keeps trying until X", or "make my agent stop running away / burning tokens" without using the word "loop". Also use it to review an existing loop's stopping conditions, guardrails, verification, and escalation paths.

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。

maxmilian/loop-engineering292026年6月24日 更新

"Design shotgun: generate multiple AI design variants, open a comparison board, collect structured feedback, and iterate. (gstack)".

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。

charlieviettq/awesome-agent-skill262026年7月20日 更新

Diagnose and fix failed Pharos GitHub Actions, adaptive PR checks, Cloudflare deploys, Pages releases, or scheduled automation. Use when a gate/run failed or the user asks to retrigger and iterate until clear.

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。

TokenBrice/pharos-watch232026年10月13日 更新

Multi-agent exploration-to-PR pipeline for safely exploring, fixing, and shipping a feature or CLI. Use when the user says: "explore X", "test and ship X", "run the pipeline on X", "run /feature-pipeline on X", "let's properly investigate X", "stress-test X and file issues", or "take X through the full pipeline." Also triggers on: "find what's broken in X", "test X end-to-end", "do a sandbox review of X", "file issues for X and fix them." Do NOT use for single-file bug fixes or one-off edits — the overhead is only justified when exploration, planning approval, and CI watching all matter. Manages state via scripts/state.sh (jq required). Five phases: LIGHTER explores → ORCHESTRATOR drafts PR → HEAVIER plans (user approves first) → ORCHESTRATOR commits → HEAVIER sandbox review → LIGHTER iterates CI → LIGHTER drift check + summary. Four mandatory stop hooks (M1–M4) gate each phase transition.

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。

gaia-research/gaia-skill-tree232026年10月14日 更新

fp-drift

無料

Phase 5 of the feature-pipeline suite. Use this after /fp-iterate prints the M4 stop hook confirming CI is green. Fetches origin/main, runs gaia dev diff to surface any skill-registry changes the PR introduced that are unrelated to its stated purpose (accidental drift), then prints the final pipeline summary including a CI churn report (via /ci-churn). Also invoke standalone whenever you want to audit whether a branch has unexpected registry drift — e.g. before merging any review/meta or cli/ branch, or when a reviewer asks "did this PR touch anything it shouldn't?". Triggers: "check for drift", "drift check", "final summary", "pipeline done", "wrap up the pipeline", "fp-drift", "Phase 5".

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。

gaia-research/gaia-skill-tree232026年10月14日 更新

Visual design verification loop for matching a goal PNG to a live render. Use when the user shares a design mock / goal image and asks to match the UI to it, asks to "iterate on this design" or "match this 1:1", or calls out visual drift in a previous UI iteration. Provides histogram color sampling, shape inspection (radius/border/shadow/bg-mode/fill-vs-context), computed-style checking on the live render, snap, and side-by-side diff. After every UI change the assistant MUST snap the live render and Read the snap with the Read tool before reporting the change done — skipping that is the

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。

colbymchenry/frontend-audit-skill232026年5月13日 更新

Build or iterate on Pulp's native Dawn-backed Three.js workflow using the real three.webgpu.js renderer, focused bridge tests, and native demo capture.

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。

Generous-Corp/pulp222026年10月14日 更新

Build or iterate on a Pulp WebView UI using the native WebView bridge, embedded assets, directory-backed dev resources, and focused WebView validation.

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。

Generous-Corp/pulp222026年10月14日 更新

Execute an autonomous development loop that picks one task per iteration, implements it, verifies it, and commits the result — each iteration in a fresh context window. Use when user runs /ralph, mentions "ralph loop", "autonomous loop", "builder verifier", "run tasks automatically", "iterate on tasks", "develop autonomously", or wants an automated build-verify-commit cycle with task tracking.

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。

joaquimscosta/arkhe-claude-plugins212026年8月14日 更新