doclogs-cli 0.1.0

CLI for capturing engineering work and turning it into career artifacts.

DocLogs

DocLogs is an engineer career operating system for capturing and reusing technical work.

What it solves

Engineers do valuable work every day: incident response, CI/CD and platform debugging, automation, infrastructure changes, PR reviews, and migrations. Most of that work is forgotten when it matters most: performance reviews, promotion packets, interviews, and professional storytelling.

DocLogs helps you turn day-to-day engineering activity into durable artifacts by:

• capturing evidence automatically from git, branches, PRs, and optional histories
• summarizing weekly progress and surfacing strong stories
• generating markdown artifacts for blog posts, LinkedIn, resumes, and interview prep
• keeping model usage provider-agnostic and safe with sanitization

Core commands

• doclog capture
  • collect commits, repository activity, PR titles, tickets, and optional notes
  • store structured daily entries in local storage
• doclog weekly
  • review weekly work
  • surface candidate stories worth expanding
• doclog generate <type>
  • create reusable artifacts such as blog, linkedin, resume, interview, or changelog
• doclog sanitize
  • sanitize captured content before any LLM request
  • redact internal URLs, tokens, IP addresses, and other sensitive details

Recommended repository structure

./
├── README.md
├── config.yaml
├── commands/
│   ├── capture.py
│   └── config.py
├── docs/
│   ├── vision.md
│   └── architecture.md
├── doclog/
│   ├── entries/
│   ├── posts/
│   └── cache/
├── prompts/
│   ├── blog.md
│   ├── linkedin.md
│   └── resume.md
└── .copilot

Architecture overview

DocLogs keeps the capture layer local and the generation layer model-agnostic. It is intentionally not a scheduler; use OS-level schedulers like cron, systemd --user, launchd, or Task Scheduler to invoke doclog capture at reminder times.

For implementation, start by focusing on:

1. evidence collection and local storage
2. weekly summaries and story selection
3. safe prompt generation
4. provider adapters for OpenAI, Ollama, Anthropic, Gemini, or other APIs

Getting started

Install from PyPI

pip install doclogs-cli
doclog --help

Install from source (development)

git clone https://github.com/MridulTi/DocLogs.git
cd DocLogs
python -m venv .venv
source .venv/bin/activate
pip install -e .
doclog --help

First run

Data is stored under ~/.doclog/ (entries, posts, config). On first use, a default config.yaml is created there.

To keep using a project-local folder instead:

export DOCLOG_HOME="$PWD/doclog"
doclog capture

Daily use

1. Configure ~/.doclog/config.yaml with your preferred LLM provider (created automatically on first run).
2. Add a scheduler entry outside the CLI to invoke doclog capture at your preferred check-in time.
3. Capture daily progress and generate reusable career artifacts from the same captured story.

Publish to PyPI (GitHub Actions)

Publishing runs via GitHub Actions — no local twine upload needed.

1. Configure trusted publishing on PyPI (leave Environment blank)
2. Push a version tag:

git tag v0.1.0
git push origin v0.1.0

The workflow uploads to PyPI automatically.

See docs/publishing.md for full setup.