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Without it the pipeline still works, but\nkeywords come from the model's general knowledge instead of live postings.\n\nDifferent temperatures are deliberate: extraction must be literal, copywriting benefits\nfrom range. The analyzer is additionally constrained by explicit verbatim rules so it\ncannot fabricate a title, date, or metric that you would then paste onto a real profile.\n\nThe final report is printed to the terminal and saved to `output/linkedin_report_<timestamp>.md`.\n\n### Output guardrails\n\nTasks 3–5 are validated before their output is accepted; a failure is sent back to the\nagent as feedback and the task re-runs (see [guardrails.py](guardrails.py)):\n\n- Headline ≤ 220 characters and About ≤ 2600 — LinkedIn's actual field limits\n- All three `[Copy & Paste into …]` markers present with text under each\n- The roadmap contains exactly 5 numbered strategies\n- The audit has both a Strengths and an Areas of Improvement section\n\n## Setup\n\n```bash\ngit clone https://github.com/<your-username>/linkedin-profile-enhancer.git\ncd linkedin-profile-enhancer\n\npython -m venv venv\nvenv\\Scripts\\activate        # Windows\n# source venv/bin/activate   # macOS / Linux\n\npip install -r requirements.txt\n```\n\nThen add your API key:\n\n```bash\ncp .env.example .env\n```\n\nOpen `.env` and choose a provider.\n\n**Anthropic Claude** (default) — key from the [Anthropic Console](https://console.anthropic.com/settings/keys):\n\n```\nANTHROPIC_API_KEY=sk-ant-your_key_here\nMODEL=anthropic/claude-opus-5\n```\n\n**Google Gemini** — free key from [Google AI Studio](https://aistudio.google.com/apikey):\n\n```\nGEMINI_API_KEY=your_key_here\nMODEL=gemini/gemini-2.5-flash\n```\n\nThe provider is inferred from `MODEL`; the matching key is selected automatically, and a\nmissing one fails fast with a message naming the variable and where to get it.\n\nOptionally add a free key from [serper.dev](https://serper.dev) (2500 searches) to enable\nthe live job-market research step:\n\n```\nSERPER_API_KEY=your_serper_key_here\n```\n\n### Supported models\n\n| `MODEL` | Input / Output per 1M | Notes |\n|---|---|---|\n| `anthropic/claude-opus-5` | $5 / $25 | Default. Highest quality |\n| `anthropic/claude-sonnet-5` | $3 / $15 | Cheaper, close in quality |\n| `anthropic/claude-haiku-4-5` | $1 / $5 | Cheapest |\n| `anthropic/claude-sonnet-4-6` | $3 / $15 | The newest model that still accepts `temperature` |\n| `gemini/gemini-2.5-flash` | free tier | |\n\n> **Why the temperatures may not apply.** Claude Opus 5, Sonnet 5, Opus 4.8/4.7 and Fable 5\n> **reject** `temperature` — a request carrying it returns a 400. On those models\n> [llm_config.py](llm_config.py) omits the parameter and the precise/creative split is\n> enforced by the explicit instructions in [Tasks.py](Tasks.py) instead (verbatim-copy rules\n> for extraction, \"never invent an achievement\" for copywriting). On Gemini and\n> Claude 4.6-and-older the temperatures below are applied as written. The startup banner\n> prints which mode is active.\n\n## Usage\n\n1. On LinkedIn: **Profile → Resources → Save to PDF**\n2. Run:\n\n```bash\npython Linkedin_main.py\n```\n\n3. Paste the full path to the downloaded PDF when prompted.\n4. Enter the role you are targeting (e.g. `Machine Learning Engineer`), or press\n   Enter to accept the default. Every rewrite and growth tactic is tailored to it.\n\n## Project structure\n\n```\nLinkedin_main.py   # entry point: reads the PDF, wires the crew, saves the report\nllm_config.py      # provider/model selection, key resolution, temperature gating\nagent.py           # agent definitions\nTasks.py           # task definitions and their context chain\nguardrails.py      # output validators wired into the tasks\ntools.py           # optional Serper search tool\nrequirements.txt\n.env.example\n```\n\n## Requirements\n\n- Python 3.10+\n- An Anthropic API key, or a Gemini API key (free tier is enough)\n\n## Notes\n\n- The PDF must contain selectable text. Scanned/image-only PDFs will fail — the script tells you if so.\n- Profile text is truncated to 20,000 characters to stay within context limits.\n- A full run costs 4–5 LLM calls, plus retries when a guardrail rejects an output.\n- Never commit your `.env` file. It is already listed in `.gitignore`.\n\n## License\n\nMIT\n","readmeExcerpt":"LinkedIn Profile Enhancer 🚀 A multi-agent $1 system that reads your exported LinkedIn profile (PDF) and returns a full rewrite plan: an optimized Headline, a story-driven About section, impact-focused Experience bullets, and an organic growth roadmap — tailored for Data Analyst / Data Science / AI roles. 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