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cccskills

「viewer」の検索結果

978 件 ・ 関連度順

概要と使いどころ

cavecrew

無料

When to delegate to `cavecrew-investigator` (locate code), `cavecrew-builder` (1-2 file edit) or `cavecrew-reviewer` (diff review) instead of working inline or using `Explore`. Their output is compressed, so main context lasts longer.

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。

codejunkie99/agentic-stack2,2972026年10月13日 更新

Use when receiving code review feedback (especially if unclear or technically questionable), when completing tasks or major features requiring review before proceeding, or before making any completion/success claims. Covers three practices - receiving feedback with technical rigor over performative agreement, requesting reviews via code-reviewer subagent, and verification gates requiring evidence before any status claims. Essential for subagent-driven development, pull requests, and preventing false completion claims.

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。

mrgoonie/claudekit-skills2,2282026年4月3日 更新

Add file-based observability, tracing, and run analysis to LLM/agent experiments — per-turn transcripts, tool calls and results, reasoning traces, token/cost accounting, per-episode artifacts, plus terminal viewers and a run-comparison report. Platform-agnostic: one small adapter covers OpenAI, Gemini/Vertex, Anthropic, LiteLLM, Tinker, vLLM, or any custom inference stack, and the same tooling then works unchanged. Use this skill whenever the user wants observability, tracing, logging, or run tracking for model experiments; asks why an agent failed a task or wants to replay/debug/diff an episode; wants to compare models, arms, or ablations; wants to instrument a new folder, repo, or platform ("add observability here", "do the same for Tinker/Gemini/OpenAI"); wants to import or tie in existing experiment results so old runs show up alongside new ones; or wants eval runner scripts where many models share one protocol. Reach for this even when they only say "log what the model is doing", "I can't tell what happened in this run", or "set up traces for this experiment".

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。

Leanmcp/gateway-skills2,1102026年10月8日 更新

Apply to multi-step work (sweeps, migrations, runs of similar edits) and to how you stack commits and PRs. Break work into small units that each end in a verifiable state, check each before the next, and order delivery so the sequence proves itself to a reviewer.

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。

michael-denyer/pstack-claude1,9662026年10月14日 更新

Apply to any non-trivial work, not just bulk work: edits, migrations, analyses, checks. Build the tool that does it or proves it (codemod, script, generator, or a skill your subagents follow) instead of working by hand. The tool is the artifact a reviewer can rerun.

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。

michael-denyer/pstack-claude1,9662026年10月14日 更新

Prepare PRs for review by cleaning noisy history, improving PR descriptions, and adding reviewer guidance without changing code behavior. Use for "make this easy to review", "tidy this PR", "clean up commits", or "annotate the diff".

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。

michael-denyer/pstack-claude1,9662026年10月14日 更新

Keep a reviewable decision trail for long-running or unattended work: a TSV log with one row per decision (what, why, evidence, result). Local by default; commit it when a reviewer needs the trail to trust the result. Use for /show-me-your-work, autonomous or multi-phase runs, or work a human reviews after stepping away.

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。

michael-denyer/pstack-claude1,9662026年10月14日 更新

Use for "interrogate", "adversarial review", "multi-model review", "challenge this", "stress test this code", "find blind spots", or "tear this apart". Multiple LLM reviewers challenge changes from independent angles.

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。

michael-denyer/pstack-claude1,9662026年10月14日 更新

Polish reviewer-response letters by softening defensive language, preserving factual meaning, and keeping responses professional, concise, and publication-appropriate.

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。

aipoch/medical-research-skills1,9512026年9月17日 更新

Helps organize reviewer comments and generate a standardized Word (.docx) response letter that maps each change to its exact location (page/paragraph/line). Use when revising a manuscript, replying to peer-review feedback, or preparing internal review responses.

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。

aipoch/medical-research-skills1,9512026年9月17日 更新

Assist with biomedical grant proposal drafting, structure, and revision; use when preparing fundable proposal sections, aligning aims and methods, or improving reviewer-facing clarity.

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。

aipoch/medical-research-skills1,9512026年9月17日 更新

Generates complete FAERS pharmacovigilance study designs for multi-drug or class-level safety comparison inside one predefined SOC or AE family using active comparators, disproportionality analysis, subgroup characterization, and reviewer-facing evidence control.

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。

aipoch/medical-research-skills1,9512026年9月17日 更新

Turns reviewer comments into structured, professional point-by-point responses linked to manuscript revisions, clarifications, rebuttals, and additional analyses.

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。

aipoch/medical-research-skills1,9512026年9月17日 更新

A comprehensive auditor for any agent skill — including Manus, OpenClaw/ClawHub, Claude, LobeHub, or custom SKILL.md-based skills. Use this skill whenever a user wants to evaluate, audit, review, score, or quality-check an agent skill before publishing, updating, or deploying. Covers two hard veto gates (structural redlines + research integrity redlines), static quality scoring across 25 criteria (ISO 25010 + OpenSSF + Agent), dynamic test input generation, multi-mode execution testing, multi-layer output evaluation with five specialized category rubrics (Evidence Insight / Protocol Design / Data Analysis / Academic Writing / Other), a Research Veto that applies to all four research categories, human eval viewer generation, actionable P0/P1/P2 optimization recommendations, and automatic skill improvement that outputs a polished, production-ready SKILL.md. Also use whenever a user says "audit my skill", "evaluate my skill", "improve my skill", or wants a corrected version after evaluation.

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。

aipoch/medical-research-skills1,9512026年9月17日 更新

Scrum-inspired paper review, revision, and R&R workflow. Handles docx/tex/md/PDF in English or Chinese. Auto-detects manuscript stage, estimates sprint count, runs multi-lens review (Contribution/Rigor/Writing/Editor), generates prioritized revision backlog, exports MD/DOCX/PDF/HTML reports. Use when asked to review a paper, revise based on reviewer comments, handle R&R, respond to peer review, plan paper revision sprints, or when user types /ps or /papersprint.

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。

aipoch/medical-research-skills1,9512026年9月17日 更新

Address actionable GitHub pull request review comments for xquic. Use when asked to handle PR review feedback, requested changes, unresolved inline threads, or reviewer comments while preserving unrelated 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。

alibaba/xquic1,9242026年10月13日 更新

Fresh adversarial code review with binary PASS/FAIL verdicts, evidence citations, and anchoring bias prevention via fresh reviewer spawning.

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。

a5c-ai/babysitter1,8452026年9月17日 更新

Adversarial plan review by 3 independent reviewers (Feasibility, Completeness, Scope & Alignment) before presenting to user.

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。

a5c-ai/babysitter1,8452026年9月17日 更新

Apply to multi-step work (sweeps, migrations, runs of similar edits) and to how you stack commits and PRs. Break work into small units that each end in a verifiable state, check each before the next, and order delivery so the sequence proves itself to a reviewer.

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。

backnotprop/pstack1,7992026年10月12日 更新

Apply to any non-trivial work, not just bulk work: edits, migrations, analyses, checks. Build the tool that does it or proves it (codemod, script, generator, or a skill your subagents follow) instead of working by hand. The tool is the artifact a reviewer can rerun.

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。

backnotprop/pstack1,7992026年10月12日 更新

Keep a reviewable decision trail for long-running or unattended work: a TSV log with one row per decision (what, why, evidence, result). Local by default; commit it when a reviewer needs the trail to trust the result. Use for /show-me-your-work, autonomous or multi-phase runs, or work a human reviews after stepping away.

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。

backnotprop/pstack1,7992026年10月12日 更新

Use for "interrogate", "adversarial review", "multi-model review", "challenge this", "stress test this code", "find blind spots", or "tear this apart". Multiple LLM reviewers challenge changes from independent angles.

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。

backnotprop/pstack1,7992026年10月12日 更新

Use when the user asks to review, verify, or audit a report, manuscript, or analysis in the workspace for traceability — resolving citations, flagging numbers with no source, and checking figures against the code that generated them. Emits a structured review block the app renders as reviewer findings. Verifies traceability, never "correctness".

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。

ai4s-research/open-science1,7952026年10月14日 更新

Use ONLY when the user asks to check, validate, review or audit analysis code for scientific correctness, or when you are the reviewer agent running its checks. Never run it unprompted as part of ordinary analysis work. Covers physics, earth/geo, biology, chemistry, social science and bioprocess/fermentation. Runs a deterministic domain-correctness gate that catches code which runs but is scientifically wrong (unit/dimension mismatch, Euclidean distance on lat/lon without a CRS, 0-based/1-based coordinate and strand errors, impossible SMILES valence, uncorrected multiple comparisons, averaging a categorical code, unconstrained kinetic-parameter fits, kLa computed from raw DO instead of the driving force, arithmetic mean of raw CFU counts, ANOVA/Tukey with no assumption check, a curvature DOE design fit with a first-order model, an ML model fit across process scales with no correction). Surfaces structured findings; never claims the code is correct.

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。

ai4s-research/open-science1,7952026年10月14日 更新