Arcticdb Timeseries

管理大规模时序数据存储与查询，支持十亿行级数据高效聚合，提供 DataFrame 懒加载与批量拼接，兼容 AWS S3 等多种存储后端。。

Security Scan

The skill's description (ArcticDB timeseries) is plausible, but the runtime instructions reference local Python tooling, ZVT environment directories, and AWS/S3 credentials while the registry declares no binaries, no env vars, and no install steps — the declared surface doesn't match what the skill actually asks the agent to do.

Purpose & Capability

SKILL.md describes an ArcticDB timeseries integration (S3 backends, DataFrame lazy loading, billion-row use cases) which legitimately may need Python, zvt, and AWS credentials; however the registry metadata lists no required binaries, no required env vars, no install spec. That mismatch (claimed runtime requirements vs declared requirements) is incoherent and should be clarified.

Instruction Scope

The instructions direct the agent to run precondition Python checks (import zvt, run zvt.recorders, check/write ~/.zvt), to read environment variables (ZVT_HOME) and to set up AWS/S3 credentials for specific use cases. Those actions access local environment, filesystem, and credentials but none of those accesses are declared in requires.env or required config paths. The SKILL.md also instructs the agent to reload seed.yaml and to cite internal AP/CW/SHARED IDs — broad operational directives beyond a simple query helper.

Install Mechanism

This is instruction-only (no install spec), which limits disk-write risk. However the SKILL.md states 'Requires Python 3.12+ with uv package manager' yet provides no install instructions. The lack of an explicit, reproducible install or verification step is an omission that reduces transparency.

Credentials

Registry shows no required environment variables or credentials, but the skill text explicitly references AWS S3 credential setup and reads/creates ZVT home directory files (checks/writes ~/.zvt). The skill asks for and expects access to credentials and local config at runtime without declaring them — disproportionate and unclear.

Persistence & Privilege

always:false and no install means it does not request forced global persistence. It does, however, instruct frequent re-reads of seed.yaml and a reload-on-decision execution protocol (in seed.yaml) which increases its runtime footprint; this is notable but not a direct privileged flag under platform rules.

What to consider before installing

This skill appears to be a fairly complete ArcticDB / ZVT workflow document, but the package metadata omits important runtime requirements. Before installing or enabling it: (1) ask the publisher for an explicit install spec and a list of required environment variables (especially any AWS_* creds and ZVT_HOME) and for the LICENSE file referenced; (2) do not provide AWS credentials or other secrets until you confirm why they are needed and whether they can be scoped (read-only, limited S3 prefixes, least privilege IAM role); (3) if you plan to run the skill in an agent that can execute commands, run it in a sandbox or VM first — the SKILL.md instructs the agent to run local Python checks that will read/write ~/.zvt and may attempt network calls; (4) request that the author declare required binaries (python3.12, zvt, uv) and preconditions in the registry metadata to match the SKILL.md; (5) if you cannot verify the origin (source is 'unknown' and homepage is missing), prefer not to grant credentials and prefer manual review. Providing the authored install spec, a short changelog, and an explicit list of env vars would raise confidence and could change the assessment to benign.

ArcticDB 时序存储 (arcticdb-timeseries)

管理大规模时序数据存储与查询，支持十亿行级数据高效聚合，提供 DataFrame 懒加载与批量拼接，兼容 AWS S3 等多种存储后端。

Pipeline

data_collection -> data_storage -> factor_computation -> target_selection -> trading_execution -> visualization

Top Use Cases (17 total)

AWS S3 Configuration for Public Blockchain Data Access (UC-101)

Setting up AWS credentials to enable secure access to public blockchain data stored in S3, allowing integration with ArcticDB for time-series storage Triggers: aws, s3, credentials

Billion Row Challenge - Large Scale Data Performance (UC-102)

Demonstrates ArcticDB's ability to handle massive datasets (1 billion rows of temperature data) with efficient aggregation, serving as a performance b Triggers: billion rows, large scale, performance

Batch DataFrame Concatenation with Lazy Loading (UC-103)

Demonstrates efficient concatenation of multiple DataFrames stored in ArcticDB using lazy loading to minimize memory consumption during batch operatio Triggers: concat, batch, lazy

For all 17 use cases, see references/USE_CASES.md.

Execute trigger: When user intent matches intent_router.uc_entries[].positive_terms AND user uses action verb (run/execute/跑/执行/backtest/fetch/collect)

What I'll Ask You

• Target market: A-share (default), HK, or crypto? (US stocks in ZVT are half-baked — stockus_nasdaq_AAPL exists but coverage is thin)
• Data source / provider: eastmoney (free, no account), joinquant (account+paid), baostock (free, good history), akshare, or qmt (broker)?
• Strategy type: MACD golden-cross, MA crossover, volume breakout, fundamental screen, or custom factor?
• Time range: start_timestamp and end_timestamp for backtest period
• Target entity IDs: specific stocks (stock_sh_600000) or index components (SZ1000)?

Semantic Locks (Fatal)

ID	Rule	On Violation
SL-01	Execute sell orders before buy orders in every trading cycle	halt
SL-02	Trading signals MUST use next-bar execution (no look-ahead)	halt
SL-03	Entity IDs MUST follow format entity_type_exchange_code	halt
SL-04	DataFrame index MUST be MultiIndex (entity_id, timestamp)	halt
SL-05	TradingSignal MUST have EXACTLY ONE of: position_pct, order_money, order_amount	halt
SL-06	filter_result column semantics: True=BUY, False=SELL, None/NaN=NO ACTION	halt
SL-07	Transformer MUST run BEFORE Accumulator in factor pipeline	halt
SL-08	MACD parameters locked: fast=12, slow=26, signal=9	halt

Full lock definitions: references/LOCKS.md

Top Anti-Patterns (14 total)

• AP-DATA-SOURCING-001: Missing or invalid User-Agent headers for SEC API requests
• AP-DATA-SOURCING-002: Ignoring external API rate limits causing IP blocking
• AP-DATA-SOURCING-003: No HTTP timeout configuration causing indefinite hangs

All 14 anti-patterns: references/ANTI_PATTERNS.md

Evidence Quality Notice

[QUALITY NOTICE] This crystal was compiled from blueprint finance-bp-103. Evidence verify ratio = 79.0% and audit fail total = 19. Generated results may have uncaptured requirement gaps. Verify critical decisions against source files (LATEST.yaml / LATEST.jsonl).

Reference Files

File	Contents	When to Load
references/seed.yaml	V6+ 全量权威 (source-of-truth)	有行为/决策争议时必读
references/ANTI_PATTERNS.md	14 条跨项目反模式	开始实现前
references/WISDOM.md	跨项目精华借鉴	架构决策时
references/CONSTRAINTS.md	domain + fatal 约束	规则冲突时
references/USE_CASES.md	全量 KUC-* 业务场景	需要完整示例时
references/LOCKS.md	SL-* + preconditions + hints	生成回测/交易代码前
references/COMPONENTS.md	AST 组件地图（按 module 拆分）	查 API 时

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Compiled by Doramagic crystal-compilation-v6.1 from finance-bp-103 blueprint at 2026-04-22T13:00:48.376963+00:00. See human_summary.md for non-technical overview.