startup_profile ยท diff
git:20260530.37826dc to git:20260906.f9cef19
43 added, 10 removed. Audit A to A.
---
name: startup_profile
- description: Generates a neutral, objective 5-point diagnostic of a startup. It bypasses marketing narratives to expose the structural reality of the business, prioritizing external risks and identifying specific tasks for an investment analyst. Use this skill when the user asks "Profile this startup", "Run startup diagnostic", or "What does this startup do?". Note that if no context/document is provided via the GUI, the <STARTUP_NAME> must be clearly specified in the query.
+ description: Generate a neutral five-part startup diagnostic from dataset evidence or supplied files. Use to explain the business, technology, market and current challenges.
---
- # Startup Profile
+ # Startup profile
- This skill generates a neutral, objective 5-point diagnostic of a startup using the 5-Point Framework.
+ Describe what the startup does and the structural questions requiring review.
- ## Framework
+ ## Inputs and outputs
- 1. **Oneliner:** Cold, objective description of what they actually do.
- 2. **Core industry:** The specific industry/market.
- 3. **Technology:** Technical reality, highlighting dependencies and technical single points of failure.
- 4. **Business model:** How they claim to make money, highlighting structural risks.
- 5. **Current challenges:** Critical data gaps, barriers to entry, and domains requiring expert due diligence.
+ The async `startup_profile(startup, files=None)` returns one
+ `InsightFile` in a list, named `startup-profile-<startup>-<model>.md`.
+ The configured framework covers oneliner, industry, technology, business model
+ and current challenges; keep detailed assessment instructions in configuration.
+ ## Workflow and dependencies
+
+ Prepare the startup dataset and synchronize it, even when files are supplied.
+ With no files, select a manual override or reusable generated report using indexed
+ startup revisions and the configured query/instructions.
+
+ With files, prepare the existing ephemeral dataset and analyze those documents.
+ This path bypasses output reuse and still saves under the startup's normal
+ profile identity. Its cache metadata does not include the supplied file content;
+ do not interpret that metadata as proof of file-input freshness.
+
+ Use `dataset_chat` with the configured questions and prompt. The registry has
+ no profile prerequisite; direct preparation may import Dealum source data.
+
+ ## Side effects and failure behavior
+
+ Preparation and retrieval may convert/index documents; generation calls a model
+ and saves a report. Supplied files can replace the current model's startup report.
+ Technical failures and empty generated output raise before saving.
+
+ A nonempty insufficient-context sentinel can be saved as the profile; it is not
+ a successful substantive diagnosis. Consumers can inspect
+ `InsightFile.has_insufficient_context()`.
+
## Usage
```bash
- conda run -n sictic-env python -m skills.harness /startup_profile "<STARTUP_NAME>"
+ conda run -n sictic-env python -m skills.harness '/startup_profile "<STARTUP>"'
```
+
+ The direct CLI accepts comma-separated `--startup` names and repeated `--files`
+ options. It continues after individual startup failures, prints successful
+ reports, then exits with code 1 if any failed. The harness handles one startup.
+
+ ## References
+
+ - [Implementation](startup_profile.py), [direct CLI](__main__.py)
+ - [Configuration](../../config/startup_profile/)
+ - [Dataset chat](../dataset_chat/SKILL.md)