agentleFS
selfishprimateselfishprimate/seekter/.claude/skills/seekter-report/SKILL.md

seekter-report

Skill80 starsChanged 5 days agoMITView on GitHub

Summarise the job search from the markdown tracker - funnel, response rate, what's working, where applications come from, what's pending on the user. Use when the user runs /seekter-report or asks how the search is going, for weekly numbers, or what to change.

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 0 are distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Read-only. Data comes from applications/ via scripts/seekter.mjs, plus runs/*.md for per-day source tables.

    line 8
  2. Very specific:

    Every record with status: pending is a hand-off (node scripts/seekter.mjs list --status pending.

    line 24
  3. Very specific:

    Write 3–5 plain observations with the number behind each ("C-track: 14 sent, 0 replies in 3 weeks").

    line 25

≈ “Common” is an estimate: at least 1 in 5 of the 308,232 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

/seekter-report — how is the search going

Read-only. Data comes from applications/ via scripts/seekter.mjs, plus runs/*.md for per-day source tables.

  1. node scripts/seekter.mjs stats (all time) and node scripts/seekter.mjs stats --since <7 days ago>.
  2. node scripts/seekter.mjs list --since <period start>, then read the front matter of the rows you need (source, ats, location_fit, fit).
  3. Compute, for the period and all time:
    • applications sent, skips, pending hand-offs;
    • replies: interviewing + offer + rejected, as a share of sent; median days from applied to the first log line after it;
    • the breakdowns, each one command (node scripts/seekter.mjs stats --by <field> [--since …] [--min 5]), which print sent, replies and interviews per group:
      • source (freehire, linkedin, board names, manual) and apply_type (company form, Easy Apply, email);
      • ats, the form system the application went through;
      • location_fit (A/B/C). A track with many sends and no replies is a targeting signal;
      • title and seniority, derived from the role ("Senior Product Designer (Growth)" counts as product designer · senior);
      • fit and resume, once they are filled: whether the fit score predicts replies is the question the score exists to answer, and the resume type is what the run uploaded;
      • month, to see whether the funnel is moving. An interview is a record that reached interviewing or offer, even if it was later rejected or closed. Replies include rejections. Rates are over decided applications (a reply, or closed); one still applied is "in flight", has its own column and counts in no rate, so a week of fresh sends does not pull a group down. A group with fewer than 5 decided shows n<5 instead of a rate (--floor changes it): quote its counts, not a percentage. With only a handful of interviews, compare groups on replies and say how many interviews there are rather than quoting interview rates;
    • top skip reasons (group the Reason column of applications/<YYYY-MM>/skipped.md by its first clause).
  4. Every record with status: pending is a hand-off (node scripts/seekter.mjs list --status pending; also the "Needs you" table in applications/README.md): list each with its link and the exact action.
  5. Write 3–5 plain observations with the number behind each ("C-track: 14 sent, 0 replies in 3 weeks"). Suggest at most two changes, each tied to a file (profile/settings.json query, profile/profile.md rule). Don't change them without the user saying yes.

Save as runs/report-<YYYY-MM-DD>.md and show the user the short version.

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.