16 added, 13 removed. Audit A to A.
---
name: suggested_startups
description: Rank a provided list of startups against a list of investors by matching startup value propositions with investor professional backgrounds and interests.
---
## Skill Prompt: `suggested_startups`
**Objective:** Rank a provided list of startups against a list of investors by matching startup value propositions with investor professional backgrounds and interests.
**Inputs:**
* `dataset_name` (Optional, Default=`"sictic_members"`): Community dataset containing investors.
* `startups` (Optional): Startup names to consider. If omitted, discover startup datasets dynamically.
- * `investors` (Optional): Investor names to process. If omitted, resolve all persons from the dataset with `LinkedInResolver`.
+ * `investors` (Optional): Investor names or LinkedIn IDs to process. If omitted, use all canonical persons with LinkedIn IDs from `persons_in_dataset`.
* `max_startups` (Optional, Default=5): Maximum suggested startups per investor.
**Procedure:**
1. **Input Resolution:**
* Slugify `dataset_name`.
- * If `investors` is empty, resolve all persons with `LinkedInResolver(dataset_slug).get_all_persons()`.
+ * Resolve investors to canonical `Person` objects from `persons_in_dataset(dataset_slug)` and preserve their LinkedIn IDs throughout profile lookup.
* If `startups` is empty, discover startup datasets with `list_dataset_names("startups")`, excluding configured community and ignored datasets from `config_load()["bulk_refresh"]`.
2. **Configuration & Sync:**
- * Load `prompt_template = config_load()["suggested_startups"]["suggested_startups_prompt"]`.
- * Build `datasets_to_check = [dataset_slug] + [slugify(startup) for startup in startups]`.
- * Run `sync_datasets(datasets_to_check, raise_on_error=True)` so investor and startup indexes are current.
+ * Load the prompt and JSON response schema from `config_load()["suggested_startups"]`.
+ * Build one `config_key()` from the complete suggested-startups config section so future config files participate in Insight freshness automatically.
+ * Build `datasets_to_check = [dataset_slug, "available-startup-profiles"]`.
+ * Run `sync_datasets(datasets_to_check, raise_on_error=True)` so investor and selected startup-profile indexes are current.
3. **Per-Investor Insight Cache:**
- * For each investor, construct `lib.insights.InsightFile(dataset=dataset_slug, skill="suggested_startups", model=llm_model(), identifier=investor, subdir=True, source_datasets=datasets_to_check, prompt_key=prompt_template)`.
+ * For each investor, construct `lib.insights.InsightFile(dataset=dataset_slug, skill="suggested_startups", model=llm_model(), identifier=investor, subdir=True, source_datasets=datasets_to_check, config_key=suggested_config_key)`.
* Use `insight.find(selection="reusable")` to skip investors whose suggested-startups report is already fresh.
- 4. **Startup and Investor Profile Preparation:**
- * Compile startup profiles in memory with `compile_startup_profiles(startups)`, which calls `startup_profile(startup)` for each selected startup and combines the profile text into a single prompt context.
- * Refresh investor profiles with `investor_profile(source_dataset=dataset_slug)`.
- * Load the selected investors' reusable investor profiles with `read_investor_profiles(dataset_slug, names_to_process)`. These profiles combine person profiles with investment track records and preferences.
+ 4. **Stored Profile Preparation:**
+ * Select startup-profile insights with `dataset_from_insight("available-startup-profiles", startups, "startup_profile")`; fail before any LLM call if a requested profile is missing or duplicated.
+ * Compile those selected `InsightFile` objects into one prompt context without regenerating startup profiles.
+ * Load each pending investor's stored investor profile by canonical LinkedIn ID; fail before any LLM call if any profile is missing. Do not refresh investor profiles as a side effect.
5. **Per-Investor Startup Selection:**
- * For each investor with an available investor profile, call `process_single_investor(investor, profile_text, compiled_startups, prompt_template, max_startups)`.
- * `process_single_investor` calls `llm_chat`, parses the JSON-like ranking response with `repair_json_payload`, sorts by rank, and keeps the top `max_startups`.
+ * For each pending investor, call `generate_report` with the stored profile, prompt, schema, candidate startups, and `max_startups`.
+ * Supply the schema to LiteLLM as a strict structured-output response format, always run `repair_json_payload`, then apply JSON Schema and request-specific business validation.
6. **Output Generation:**
- * Save one Markdown table per investor with `insight.save(content)`.
+ * Save one Markdown table per investor with `insight.save(content)` only after all validation succeeds.
+ * Catch generation, validation, and save errors per investor. Log the full error, do not save that investor's invalid output, and continue processing the remaining investors.
+ * Log a final summary with cached, generated, and failed counts. Keep this operational summary out of the return value.
* Log each `insight.path` and return all generated or reusable artifacts as
a flat `list[InsightFile]`. Do not hardcode `<REPO_PATH>/insights/...`
paths.
## Usage
```bash
conda run -n sictic-env python -m skills.harness /suggested_startups --startups "<startup1>,<startup2>" --investors "<name1>,<name2>"
```