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---
name: person_profile
description: Collate a comprehensive profile on a specific person by searching a given dataset, returning the full synthesized report.
---
## Skill Prompt: `person_profile`
**Objective:** Collate a comprehensive profile on a specific person by searching a given dataset, returning the full synthesized report.
**Inputs:**
* `name`: A string representing the person's name (e.g., `"John Doe"`).
* `dataset_name`: The target dataset to search (e.g., `"fabas"`).
**Procedure:**
1. **Insight File & Caching:**
* Resolve the person to its canonical identifier through the standard person-resolution flow.
* Construct the profile insight with `lib.insights.InsightFile(dataset=dataset_slug, skill="person_profile", model=<model>, identifier=<person_identifier>, subdir=True, config_key=<query_and_instructions>)`.
* Use `insight.find(selection="reusable")` and `insight.content()` to reuse a fresh existing profile when available; otherwise generate the profile and persist it with `insight.save(...)`. Do not hardcode `<REPO_PATH>/insights/...` paths.
2. **Data Retrieval & Synthesis:**
* Read the existing manual roster and enrich its people through `LinkedInResolver`.
* Synchronize the dataset before checking profile freshness.
* Load `query`, `llm_instructions`, and `founder_traits_instructions` via
`load_repository_config("person_profile")`; both instruction sections always
form the standard prompt and freshness key.
* On a cache miss, use `build_person_dossier` for dataset evidence when
`include_dataset_context` is enabled, add the resolved LinkedIn payload,
and synthesize through `generate_markdown`.
3. **Output Generation:**
* Save every synthesized profile through `InsightFile`.
* `person_profile(...)` returns a flat `list[InsightFile]`.
* `person_profile_as_person_objects(...)` runs the same workflow and returns
the populated `list[Person]` required by person-oriented composition.
**CLI Interface:**
* Expose this skill through the shared slash-command harness.
## Usage
Both `person_profile(...)` and `person_profile_as_person_objects(...)` use the
same standard generation workflow: read the authoritative manual roster, resolve
LinkedIn profiles, gather dataset evidence, and apply the configured biographical
and founder-trait instructions. Founder traits are assessed for explicitly
identified active founders; unsupported assessments report insufficient information.
A missing roster raises an error directing the caller to run `persons_in_dataset`;
profile generation never discovers people.
Every profile uses `<identifier>-<model>.md`, with the identifier selected by
`Person.identifier`: LinkedIn ID, otherwise email address, otherwise full name,
using standard slugification. The retained `include_dataset_context` option affects
freshness metadata, never filenames. Manual overrides retain their normal precedence.
```bash
conda run -n sictic-env python -m skills.harness /person_profile "<DATASET_NAME>" "<NAME>"
conda run -n sictic-env python -m skills.person_profile \
--dataset "<DATASET_NAME>" \
--person "<NAME_1>, <NAME_2>"
```
Person discovery is owned by `skills/persons_in_dataset`. This skill uses the library roster reader, then gathers evidence and writes individual profiles. Only an explicit discovery run creates a missing roster.