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Python backend for Databricks Apps — FastAPI (default), Flask, Dash, Streamlit, Gradio, Reflex. **Default for a new Databricks App is `databricks-apps` (AppKit — Node/TypeScript/React) — reach for it first.** Use this skill only when the user asks for a Python backend, extends an existing Python app, or the team is Python-only. Covers OAuth auth, app resources, SQL warehouse and Lakebase connectivity, foundation-model / Vector Search / model-serving APIs (via `databricks-python-sdk`), and deployment via CLI or DABs.

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Read huge, dense, read-only text as rendered PNG pages instead of raw text — image tokens scale with pixels, not characters, so token-dense bulk (big files, vendored code, diffs, logs) costs ~60–75% less as an image. Use when you must skim or reason over thousands of lines you will NOT edit or byte-copy. Lossy on exact strings: never for files you'll Edit, secrets, hashes, or byte-exact values. Fable-class readers only.

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Create and maintain one committed per-project memory file (PROJECT.md) so agents stop re-discovering the same facts every cold session. Stores only the stable, expensive-to-rediscover, not-in-the-code context — architecture + why, build/test/run commands, decisions and dead-ends — as human-readable markdown, versioned with the code so it can't silently rot. Use when asked to set up project memory/context, stop agents re-grepping the codebase, scaffold or refresh a PROJECT.md / CLAUDE.md memory section, or cut cold-start rediscovery cost. Prose context only — never code, config, or data.

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Use when writing, changing or reviewing code that touches the house contract for Jetpack Compose and Compose Multiplatform apps: module graph, MVI/BaseViewModel, error tiers, state ownership, naming, Koin DI, Navigation 3 and coroutines. The compose skill is the entry point. Do NOT use for Gradle-only work (compose-project), new screens (compose-feature), composable-only work (compose-ui), repositories or persistence (compose-data), or expect/actual splits (compose-platform).

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MANUAL-ONLY: challenge a product idea, design, PRD, or architecture from first principles. Use only when the user names this skill. Research first, then write a local review and PRD handoff.

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Review whether a repository's coding-agent harness can reliably carry work from intent through controlled execution, verification, delivery, and learning. Use when asked to assess agent readiness, repeated agent failures, Rules/Skills/Hooks/Memory effectiveness, missing validation or recovery loops, or whether a harness repair improved later outcomes. Do not use for code-only audits, AGENTS-only audits, individual skill reliability reviews, or executing the task itself.

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Applies Han's shared Human-Readable Output Standard to a target you already have — a file on disk, text pasted into the prompt, or a draft already produced in the conversation — by dispatching the readability-editor to rewrite its prose so the main point comes first, headings are descriptive, each paragraph carries one idea, and sentences stay short and active, while preserving every fact. Use when you want to make a document or draft readable, edit or polish prose for readability, clean up writing, tighten wording, or re-apply the readability standard to something already written. Rewrites prose only, leaving code, diagrams, and citation identifiers unchanged. Does not write new feature or system documentation — use project-documentation. Does not restructure code or review it — use refactor to restructure code and code-review to audit it. Does not judge the underlying work or raise findings; it only rewrites the writing.

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Fix "ref.listen can only be used within the build method of a ConsumerWidget" assertion error in Flutter Riverpod 3.x. Use when: (1) ConsumerStatefulWidget crashes with "Failed assertion: line 492 pos 7: 'debugDoingBuild'" in flutter_riverpod consumer.dart, (2) ref.listen is called inside initState, addPostFrameCallback, or any lifecycle method other than build(), (3) All widget tests fail with this assertion from scheduler callback. Applies to flutter_riverpod 3.0+ with ConsumerStatefulWidget.

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Investigate or fix a GitHub issue by number or URL. Keep investigation-only requests read-only.

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JeecgBoot 开发规范（仅手动触发）。⚠️ 本技能只在用户显式输入 /jeecg-dev 或 $jeecg-dev 命令时使用，禁止自动触发——编写/修改 JeecgBoot 代码、应用 GitHub PR/issue 改动、修复 bug、新增功能、重构、代码生成等场景都不要自动调用本技能。内容涵盖 update-begin/end 痕迹注释、命名规范、实体/控制器/服务模式、API 约定、建表规则、MyBatis 多数据库兼容与修改日志实践。MANUAL ONLY: invoke ONLY when the user explicitly runs the /jeecg-dev command. Do NOT auto-trigger on any code editing, bug fix, PR/issue application, refactoring, or code generation.

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Detect somatic and germline copy number variants from targeted, exome, and whole-genome sequencing with CNVkit, a read-depth caller that combines on-target and off-target (antitarget) coverage. Covers panel-of-normals construction, flat-reference tumor-only calling, hybrid/amplicon/WGS modes, CBS vs HMM segmentation selection, purity-aware integer calling, and reconciliation against GATK and allele-specific callers. Use when calling CNVs from hybrid-capture panels or exomes, deciding whether CNVkit (depth-only) is the right tool versus an allele-specific caller, building a panel of normals, diagnosing flat-reference false positives, or interpreting log2 ratios into copy-number states.

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Infer integer allele-specific copy number, tumor purity, and ploidy from tumor sequencing by jointly modeling read depth (logR) and B-allele frequency (BAF) with ASCAT, Sequenza, FACETS, PURPLE, and PureCN (tumor-only). Covers the purity-ploidy identifiability problem, the diploid-baseline (dipLogR) anchor, major/minor copy number, loss of heterozygosity, sunrise/contour fit diagnostics, and reconciliation of conflicting fits. Use when tumor analysis needs absolute copy number rather than relative log2, when estimating purity and ploidy, calling LOH or copy-neutral LOH, resolving whole-genome doubling, running tumor-only allele-specific calling, or choosing among ASCAT, Sequenza, FACETS, and PureCN.

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All-atom protein design using BoltzGen diffusion model. Use this skill when: (1) Need side-chain aware design from the start, (2) Designing around small molecules or ligands, (3) Want all-atom diffusion (not just backbone), (4) Require precise binding geometries, (5) Using YAML-based configuration. For backbone-only generation, use rfdiffusion. For sequence-only design, use proteinmpnn. For structure validation, use boltz.

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Full enhancement pipeline for an existing Lattice skill — rewrites a molecule or atom to modern-capable-model grade: restores degraded grammar, deduplicates, hardens gates and branching to one-unambiguous-action precision, verifies zero behavioral loss against a pre-capture diff, then runs the combined QA gate (skill-review, skill-tighten, skill-validate) and an independent fresh-context verifier, fixing everything found. Consumer-first: optimized for first-time open-source users on fresh repos with minimal .lattice/ setup, running capable models. Use when the user says 'enhance this skill', 'upgrade this molecule', 'modernize this skill', 'optimize this skill', 'make this skill production grade', 'full enhancement', 'polish this molecule like design-blueprint', or names a molecule/atom to overhaul. For gap-finding only use skill-review; for conventions only use skill-validate — this skill runs the whole loop end to end.

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Use for writing, reviewing, debugging, testing, or optimizing Python code that uses PyArrow arrays, schemas, Tables, RecordBatches, RecordBatchReaders, compute kernels, Dataset scans, Parquet, or Arrow IPC and C data interfaces. Trigger on schema drift, null/type semantics, chunking, batch streaming, predicate projection pushdown, conversion, and memory ownership. Do not use for pandas-only transformations, Polars-only expressions, generic SQL, Arrow Flight service design, C++/Rust Arrow, or Parquet work that does not use PyArrow.

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Transparent, itemized impressions of AI-generated WRITING STYLE — the repo's ONLY non-integrity track. Two passes: a deterministic defensive-hedge density screen (tools/check_ai_style.py, AIS-DEFENSIVE-HEDGE) plus a fresh cross-model GROSS-cases-only semantic pass over the 13 AIS-* style tells (broken narrative arc, LLM phrase tics, jargon-stuffing, invented codenames, clause/formula walls, gratuitous pseudocode, bullet overuse, bold-module spam, restatement loops, focus drift, single-style figures, appendix dumping). Every finding is named, LOCATED, span-anchored to the evidence ledger (claims.json), carries not_integrity_finding:true + false_positive_risk:high + an fp_case, and gets ZERO verdict weight: the adjudicator forces it to info, excludes it from overall_verdict, and renders it in a SEPARATE report section. NOT an AI-text classifier — no scores, no "this is AI-written", no authorship probability; a paper can be CLEAN_GIVEN_EVIDENCE and still list many. Emits ai-style-impressions.findings.json; computes NO verdict; if a tell is actually substantive it routes to the integrity auditor. Triggers: "AI style", "vibe check", "writing fingerprint", "AI 文风", "vibe paper".

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Audit experiment integrity against the evidence ledger. At L2 (repo + result files present) a fresh cross-model reviewer reads the eval code line-by-line for fake/derived ground truth, score self-normalization, phantom results (a paper number with no backing file/key), dead/uncalled metric code, verified-scope inflation, method-described ≠ method-evaluated drift, synthesized-looking results, placeholder/fake data still wired into a released result, code-output ≠ reported-number mismatch, and missing reproducibility artifacts (an empirical/agent/LLM paper shipping neither code nor the prompts/configs its results need) — every finding span-anchored to a ledger claim_id. At L0/L1 (PDF / source only) the same patterns are surfaced as info-level 'could-not-verify' signals where the ledger gives an anchor (observability_level_required:2) — NEVER a fraud verdict from a PDF. The reviewer PROPOSES findings; tools/adjudicate_findings.py computes the verdict. Detect-only. Triggers: "experiment forensics", "audit the results", "check the eval code", "实验诚实度".

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anti-reversing-techniques workflow skill. Use this skill when the user needs AUTHORIZED USE ONLY: This skill contains dual-use security techniques. Before proceeding with any bypass or analysis: > 1 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

anti-reversing-techniques workflow skill. Use this skill when the user needs AUTHORIZED USE ONLY: This skill contains dual-use security techniques. Before proceeding with any bypass or analysis: > 1 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Andy Grove 视角. Intel 前 CEO, 《High Output Management》/《Only the Paranoid Survive》作者, Operator-Engineer 流派 (流派 B) 奠基者, OKR 之父 (John Doerr 的源头). 把 CEO 工作当工程系统管: 产出 = 组织产出, 经理杠杆率, OKR, 战略拐点 (SIP), 建设性对抗. 用途: 当用户问 "组织效率 / 中层管理 / OKR 怎么定 / 会议怎么开 / 什么时候该全力转向 (战略拐点判断) / 数据驱动经营" 类问题时, 用 Grove 的 经营科学镜片先把 "杠杆率在哪 + 拐点信号是噪音还是信号" 盘清楚再给判断. 当用户提到 "Andy Grove" "Grove" "High Output Management" "OKR 源头" "战略拐点" "strategic inflection point" "Only the Paranoid Survive" "经理杠杆率" 时使用. 即使用户只是说 "用 Grove 思路看一下" 也应触发.

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红队渗透 / 攻防 — 受授权的红队作业者 + 渗透测试工程师 + 攻击型安全顾问的认知操作系统 (侦察 OSINT / 外网渗透 / 内网 AD 渗透 BloodHound + Kerberoasting + ADCS 利用 + 横向移动 / Web 应用渗透 OWASP WSTG / 移动 OWASP MASTG / 云渗透 AWS Azure GCP IAM 路径 + 容器逃逸 + K8s / C2 操作 Cobalt Strike Sliver Mythic Havoc + OPSEC / 初始访问 + AV EDR 绕过 (仅授权场景) / 无线 RF / 物理社工 / 报告与整改 / 框架 MITRE ATT&CK + D3FEND + PTES + OSSTMM + NIST 800-115 + Kill Chain / 法律伦理 CFAA + 网络安全法 + 刑法 285 286 + 数据安全法 + GDPR + 授权书 + 范围 + 交战规则 — 不含 黑产 / 未授权攻击 / 大规模 exploitation / 供应链投毒 / 未授权 DoS — 这是 重罪 + 行业封杀 + 律师吊销, 本 skill 严守 authorized-only 边界 — 也不含 蓝队 SOC + 恶意软件 即服务 / 僵尸网络 / 勒索软件作者 — 这是 cybercrime 不是 红队) (Cybersecurity Red Team / Offensive Security Operations — the cognitive operating system of authorized red team operators, penetration testers, and offensive security consultants covering (a) reconnaissance & OSINT (passive + active discovery, asset surface mapping), (b) external network pentest (perimeter, exposed services, web), (c) internal network / Active Directory pentest (AD enumeration via BloodHound, Kerberos abuse — Kerberoasting / AS-REP-roasting / Unconstrained delegation / S4U2self, NTLM relay, ADCS abuse, GPO abuse, lateral movement, privilege escalation), (d) web application pentest (OWASP WSTG, authentication, authorization, SSRF, XXE, deserialization, SSTI, prototype pollution, GraphQL, JWT, API), (e) mobile pentest (OWASP MASTG, iOS / Android, instrumentation Frida / Objection, MASVS), (f) cloud pentest (AWS / Azure / GCP — IAM enumeration, privilege escalation paths, container escape, K8s RBAC, serverless), (g) C2 operations & post-exploitation (Cobalt Strike / Sliver / Mythic / Havoc, beacon ops, malleable profiles, OPSEC), (h) initial access & evasion (phishing infrastructure, payload development, AV / EDR evasion, BYOVD, AMSI / ETW bypass — strictly for authorized engagements), (i) wireless / RF (WPA2/3, evil twin, Wi-Fi pivots), (j) physical / social engineering (badge cloning, pretexting, vishing — under engagement letter), (k) reporting & remediation (executive summary, technical findings, CVSS, MITRE ATT&CK mapping, retest), (l) frameworks & methodology (MITRE ATT&CK, MITRE D3FEND, PTES, OSSTMM, NIST SP 800-115, OWASP WSTG / MASTG, Cyber Kill Chain, Unified Kill Chain, Diamond Model), (m) law & ethics (CFAA US, Computer Misuse Act UK, 中国 刑法 285/286 + 网络安全法 + 数据安全法, GDPR for tested EU systems, engagement letter, scope, rules of engagement, safe harbor for bug bounty); NOT criminal hacking / 黑产 / unauthorized targeting / mass exploitation / supply-chain compromise / DoS against unconsented systems (这是 重罪 + 业内开除 + 律师执照吊销, 本 skill 严守 authorized-only 边界), NOT pure defensive blue team / SOC analyst tradecraft (是 平行学科, 仅做 边界标注 + ATT&CK 反推方向), NOT malware-as-a-service development / botnet ops / ransomware authoring (是 cybercrime 不是 红队), NOT 'ethical hacking' 在 'just curious 看看' 自我合理化的灰色操作 (违反 authorization 原则即不是 红队).) Master OS — automated mastery of Cybersecurity Red Team / Offensive Security Operations — the cognitive operating system of authorized red team operators, penetration testers, and offensive security consultants covering (a) reconnaissance & OSINT (passive + active discovery, asset surface mapping), (b) external network pentest (perimeter, exposed services, web), (c) internal network / Active Directory pentest (AD enumeration via BloodHound, Kerberos abuse — Kerberoasting / AS-REP-roasting / Unconstrained delegation / S4U2self, NTLM relay, ADCS abuse, GPO abuse, lateral movement, privilege escalation), (d) web application pentest (OWASP WSTG, authentication, authorization, SSRF, XXE, deserialization, SSTI, prototype pollution, GraphQL, JWT, API), (e) mobile pentest (OWASP MASTG, iOS / Android, instrumentation Frida / Objection, MASVS), (f) cloud pentest (AWS / Azure / GCP — IAM enumeration, privilege escalation paths, container escape, K8s RBAC, serverless), (g) C2 operations & post-exploitation (Cobalt Strike / Sliver / Mythic / Havoc, beacon ops, malleable profiles, OPSEC), (h) initial access & evasi

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Binding-resolved Python symbol queries via pyright — every true caller (--refs), the real definition (--def), or an inferred signature (--hover) of a .py symbol, excluding the same-named false positives text search cannot tell apart. Use when a task needs ALL callers or users of a named Python symbol and grep would over-match. Python only, and only for the caller/definition question: measured 2026-07-04 on real issue-localization tasks, plain ripgrep tied or beat the semantic and indexed-regex tiers at 4-60x less wall-clock, so those tiers survive below without being a default.

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Discover and load skills on demand from /mnt/skills/user/. Use when you need a capability but don't know which skill provides it, when the boot-emitted skill list is names-only and you need a full description, or when you want to list the catalog. Verbs are list (names only), search (rank by name/description match against a query), and show (emit the full SKILL.md for a named skill).

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The lifecycle gate for a local Codespace-equivalent sandbox. Routed to when the user invokes /ca-sandbox:sandbox to pull an untrusted repo into an ephemeral, host-FS-isolated Docker container, or any of the interaction commands (/ca-sandbox:sandbox-shell, /ca-sandbox:sandbox-exec, /ca-sandbox:sandbox-cp, /ca-sandbox:sandbox-destroy) against an existing box. Five gated phases — pre-flight, clone+build, isolated run, interact, teardown. The load-bearing invariant is structural: untrusted code in the box can never reach the host filesystem (no bind mount, no docker socket, never --privileged, cap-drop ALL, non-root, read-only root). Network defaults to offline; egress out is host-initiated only. Every object is labeled ca.sandbox=1 and torn down on exit.

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。