agentleFS
QaliAIQaliAI/ai-job-agent/skills/research-target-person/SKILL.md

research-target-person

Skill0 starsChanged 3 months agoMITView on GitHub

Research the likely human owner of a verified opportunity and produce an evidence-backed contact hypothesis without guessing identity or contact details.

The file

We split this skill into the rules it gives an agent

6 rules. About 0 are common, 6 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈6

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use after an opportunity has already been qualified.

    line 10
  2. Distinctive:

    Start with the highest-priority target-person role category.

    line 20
  3. Distinctive:

    Search the company's own leadership/team pages first.

    line 21
  4. Distinctive:

    Use reputable professional/public sources to confirm current title and company.

    line 22
  5. Distinctive:

    If no reliable person can be identified, return unresolved rather than guessing.

    line 25
  6. Distinctive:

    Never infer private email patterns and never fabricate phone numbers or addresses.

    line 39

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

research-target-person

Use after an opportunity has already been qualified.

Inputs

  • normalized opportunity record;
  • target-person role categories from target_person_roles;
  • company name and source evidence.

Workflow

  1. Start with the highest-priority target-person role category.
  2. Search the company's own leadership/team pages first.
  3. Use reputable professional/public sources to confirm current title and company.
  4. Require at least one source supporting the person's current role.
  5. Record only contact information that is publicly provided or obtained through an authorized enrichment source.
  6. If no reliable person can be identified, return unresolved rather than guessing.

Output

Return:

  • name;
  • exact current title;
  • company;
  • why this person is relevant to the specific opportunity;
  • source URL(s);
  • confidence: high / medium / unresolved;
  • public/authorized contact fields if actually available;
  • suggested next action: apply, ask for intro, LinkedIn connection, email draft, or no action.

Never infer private email patterns and never fabricate phone numbers or addresses.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.