causify-ai/helpers

Última atividade de origem registrada: 18 de março de 2026 às 15:30

Catálogo do SkillsMP atualizado: 17 de outubro de 2026 às 00:09

skills coletadas: 26

Estrelas no GitHub: 146
Forks no GitHub: 162

README do repositório

Arquivo de origem do README: README.md

# helpers — Causify’s Python Toolkit for High-Leverage Engineering `helpers` is a public collection of Python utilities, configuration patterns, and developer tooling extracted from real production work at Causify. It exists for one reason: > **Make engineering repeatable.** > Turn "tribal knowledge" into composable primitives: less glue code, fewer one-off scripts, and more reliable systems. This repo is useful in three common contexts: - **Platform/product engineering**: predictable building blocks for I/O, debugging, system operations, Git/Docker, dates/time, dataframes, and more. - **Repo hygiene at scale**: linting, import validation, CI utilities, pre-commit workflows; things large repos need to stay healthy. - **LLM/agentic workflows**: lightweight wrappers for completions, structured outputs, caching modes, and cost tracking. ## One-minute map (how the repo fits together) ```mermaid flowchart LR subgraph Lib["`**Python Library** (helpers/)`"] direction TB Core["Core Helpers (hdbg, hio, hsystem, hgit, hdocker, hdatetime, …)"] Data["Data Helpers (hpandas and related modules)"] LLM["LLM & Agentic Helpers (hllm, hllm_cost, hllm_cli, hchatgpt, …)"] end subgraph Config["`**Configuration Patterns** (config_root/)`"] Conf["Env-aware configuration objects and builders"] end subgraph Tooling["`**Tooling & Automation**`"] Scripts@{ label: "Dev Scripts (dev_scripts_helpers/)" } Import@{ label: "Import Hygiene (import_check/)" } Linters@{ label: "Linting Framework (linters/, linters2/)" } Tasks@{ label: "Repo Tasks & Automation (tasks.py, invoke.yaml)" } end subgraph Docs["`**Documentation & Examples**`"] Human@{ label: "Human Docs (docs/)" } Mk@{ label: "MkDocs Site (docs_mkdocs/)" } NB@{ label: "Tutorial Notebooks (helpers/notebooks/)" } end subgraph CI["`**Continuous Integration & Hygiene**`"] GH@{ label: "GitHub Workflows (.github/)" } PC@{ label: "Pre-commit & Scanning (.pre-commit-config.yaml, semgrep, …)" } end Core -- provides base utilities to --> Conf Data -- feeds data into --> Conf LLM -- adds AI logic to --> Conf Scripts -- triggers tasks in --> GH Import -- enforces rules in --> GH Linters -- runs in --> GH Tasks -- executes workflows in --> GH Human -- documented in --> GH Mk -- deployed via --> GH NB -- tested in --> GH PC -- validates via --> GH Core@{ shape: rounded} Data@{ shape: rounded} LLM@{ shape: rounded} Conf@{ shape: rect} Scripts@{ shape: rect} Import@{ shape: rect} Linters@{ shape: rect} Tasks@{ shape: rect} Human@{ shape: doc} Mk@{ shape: doc} NB@{ shape: doc} GH@{ shape: rect} PC@{ shape: rect} Core:::lib Data:::lib LLM:::lib Conf:::config Scripts:::tooling Import:::tooling Linters:::tooling Tasks:::tooling Human:::docs Mk:::docs NB:::docs GH:::ci PC:::ci classDef lib fill:#e0f7fa,stroke:#0097a7,stroke-width:2px,color:#004d40 classDef config fill:#fff9c4,stroke:#fbc02d,stroke-width:2px,color:#795548 classDef tooling fill:#e1bee7,stroke:#7b1fa2,stroke-width:2px,color:#4a148c classDef docs fill:#c5cae9,stroke:#3949ab,stroke-width:2px,color:#1a237e classDef ci fill:#c8e6c9,stroke:#2e7d32,stroke-width:2px,color:#1b5e20 ```` ## Why this exists (the philosophy) Most engineering orgs accumulate dozens of tiny scripts and helper snippets. Over time that becomes: - duplicated logic across repos, - inconsistent behavior, - hard-to-test workflows, - brittle operations. `helpers` is the opposite: small, well-scoped primitives that are easy to discover and safe to reuse. The code is intentionally "boring"; because boring tools are what make fast teams. ## Micro-examples These examples are deliberately small. The repo is optimized for everyday leverage. ### 1) Defensive checks & clear failures ```python import helpers.hdbg as hdbg hdbg.dassert_eq(2 + 2, 4) hdbg.dassert(10 > 3, "Math still works") ``` ### 2) Safe system command execution (without subprocess boilerplate) ```python import helpers.hsystem as hsystem out = hsystem.system_to_string("python --version") print(out) ``` ### 3) Simple JSON I/O helpers ```python import helpers.hio as hio payload = {"hello": "world"} hio.to_json("tmp.json", payload) loaded = hio.from_json("tmp.json") print(loaded) ``` ### 4) DataFrame utilities (example: convert DF to JSON string) ```python import pandas as pd import helpers.hpandas as hpandas df = pd.DataFrame({"a": [1, 2], "b": [3, 4]}) print(hpandas.convert_df_to_json_string(df)) ``` ### 5) LLM structured output + cost tracking (agentic building blocks) `helpers.hllm` supports providers like OpenAI and OpenRouter (via `openrouter/<model>`). It also supports caching modes and structured output. ```python from pydantic import BaseModel import helpers.hllm as hllm import helpers.hllm_cost as hllmcost class Summary(BaseModel): summary: str risks: list[str] # Track cost for a specific provider/model. tracker = hllmcost.LLMCostTracker(provider_name="openai", model="gpt-4o-mini") result = hllm.get_structured_completion( "Summarize key risks of shipping without observability.", response_format=Summary, system_prompt="Be concise and practical.", model="gpt-4o-mini", cache_mode="DISABLE_CACHE", print_cost=True, cost_tracker=tracker, ) print(result.summary) print("Cost so far ($):", tracker.get_current_cost()) ``` ## What’s in here (a guided tour) ### Core Python helpers (`helpers/`) The main Python library lives in `helpers/`. Many modules follow the `h<name>.py` naming pattern (e.g., `hdbg.py`, `hio.py`, `hsystem.py`) to make discovery fast and avoid collisions. A few high-signal examples: - `helpers/hdbg.py`: defensive checks and assertions - `helpers/hsystem.py`: safe system command execution - `helpers/hio.py`: practical file I/O helpers - `helpers/hgit.py`: git utilities - `helpers/hdocker.py`: docker helpers - `helpers/henv.py`: environment utilities - `helpers/hdatetime.py`: date/time utilities ### Data utilities (`hpandas` and friends) `helpers/hpandas.py` re-exports a suite of pandas helpers implemented across `hpandas_*` modules (conversion, compare, display, cleaning, assertions, IO, etc.). This makes the most common "pandas hygiene" tasks consistent across projects. ### LLM & agentic utilities This repo includes lightweight building blocks for LLM workflows: - `helpers/hllm.py`: completions + structured outputs + caching modes - `helpers/hllm_cost.py`: cost tracking and provider cost utilities (incl. `LLMCostTracker`) - `helpers/hllm_cli.py`: CLI-oriented LLM workflows Tutorial notebooks live under `helpers/notebooks/` (see `hllm.tutorial.py`). ### Config patterns (`config_root/`) Reusable configuration patterns: config objects/builders and environment-aware configuration. ### Tooling and repo hygiene This repo also ships "how we keep repos healthy" primitives: - `dev_scripts_helpers/`: automation scripts for common workflows - `import_check/`: import hygiene validation - `linters/`, `linters2/`: lint framework and configs - `.github/`: CI workflows and checks - `.claude/`: Claude Code configuration, hooks, and skills (`.claude/skills/`) - See `docs/tools/ai/all.ai_workflows.how_to_guide.md` for how to run skills - `.claude/task_instructions.md`: Development instructions and task specifications - `CLAUDE.md`: Architecture overview and development patterns for Claude Code - `conftest.py`: Pytest configuration and shared test fixtures - `main_pytest.py`: Main pytest runner and test execution controller - `tasks.py`: Entry point for pyinvoke task automation system - pre-commit and scanning configs (`.pre-commit-config.yaml`, `.semgrepignore`, etc.) ## Design principles (what makes this repo useful) - **Small modules > big frameworks** Helpers should be composable and easy to understand. - **Safety by default** Defensive checks, predictable failures, sensible defaults. - **Fast discovery** Naming conventions encourage "find the right tool quickly." - **Reproducibility** If it matters, it should be runnable in CI and documented. - **Low dependency footprint** Dependencies are added only when they buy real leverage. ## Getting started ### Use as a git submodule (common in larger repos) ```bash git submodule add https://github.com/causify-ai/helpers.git helpers_root git submodule update --init --recursive ``` ### Use locally (development / experimentation) ```bash python -m venv .venv source .venv/bin/activate pip install -U pip pip install -e . ``` If contributing, enable pre-commit hooks: ```bash pip install pre-commit pre-commit install ``` ## Tests & quality checks Run tests: ```bash pytest -q # or python main_pytest.py ``` Run pre-commit: ```bash pre-commit run -a ``` List repo automation tasks (if using invoke): ```bash pip install invoke invoke -l ``` ## Documentation Human docs live under `docs/`. A browsable site is maintained under `docs_mkdocs/`. Serve MkDocs locally: ```bash pip install mkdocs mkdocs-material cd docs_mkdocs mkdocs serve ``` ## Contributing - Keep changes small and reviewable. - Add tests when behavior changes. - Prefer reusable utilities over one-off scripts. - Keep backwards compatibility in mind for downstream consumers. ## Security This repo includes secret-scanning and standard hygiene. **Do not commit secrets.** If you suspect a leak: rotate credentials and open an incident. ## License See [`LICENSE`](LICENSE).

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Skills neste repositório

Desenvolvedores de software Analistas de garantia de qualidade de software e testadores Professores do ensino superior, todos os outros Administradores de redes e sistemas de computador

8 categorias ocupacionais · 100% classificado

Redatores técnicos

Remove AI-style writing patterns from text to make it sound more natural and human

Idioma do texto original: inglês

2026-02-28

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