meta-job-search-pipeline

# meta-job-search-pipeline

Self-improver persona meta-skill. Handles 4 modes via an `llm_classify`
router — `TAILOR_NEW` (the default, pastes-JD-gets-application-pack
flow), `INTERVIEW_PREP`, `COMPARE_ROLES`, and `STATUS_DIGEST`. Each
mode unlocks only its relevant steps via `when:` conditions on the
classifier output, so a single composition handles all four without
forking the DAG into separate skills.

## Composition philosophy — multi-skill bundled orchestration

This meta-skill uses **only OpenSquilla-bundled atomic skills** plus the
five built-in step kinds — no external dependencies. The point of a
meta-skill is to *orchestrate* multiple skills, so this DAG calls into
**7 distinct bundled atomic skills**, each at the right point in the
pipeline:

| Skill | Step(s) | Role in the DAG |
|---|---|---|
| `multi-search-engine` | `web_research` | Web research per target company |
| `deep-research` | `deep_research` | Extra-context round for `INTERVIEW_PREP` only |
| `memory` | `recall_company`, `store_pack` | Cross-session memory of past company research and prior application packs — recalled before web research, stored after deliverable |
| `pptx` | `interview_deck` | Generate an interview-prep slide deck when `INTERVIEW_PREP` and the user mentions "deck" / "slides" / "幻灯" |
| `xlsx` | `tracker_xlsx` | Export the application ledger as a spreadsheet when `STATUS_DIGEST` |
| `docx` | `export_docx` | Optional final-deliverable export when the user picks `EXPORT_DOCX: YES` |

Step kinds used: `llm_chat`, `llm_classify`, `user_input`, `skill_exec`,
`agent`.

## What got dropped from the original 17-step design

The original draft had two ClawHub-shaped `skill_exec` steps that
turned out to be unnecessary:

- A `skill_exec: lead-enrichment` step that produced a structured
  company brief. The current `enrich_company` step (an `llm_chat`
  reading `outputs.web_research`) covers exactly the same contract
  with no external dependency. The original was the substitute path;
  it's now the primary path.
- A `skill_exec: notion-api-skill` step that POSTed the application
  pack to Notion. The deliverable is the markdown emitted by
  `deliver_jobpack`; the user copies it wherever they want.
  Convenience does not justify the dependency.

## Honest limitations

- **No application-ledger persistence.** `STATUS_DIGEST` mode is
  paste-driven: the user pastes their current ledger every turn. Once
  the proposed `state:` primitive ships, the ledger can persist across
  turns automatically.
- **No auto-apply.** The skill produces text for the user to send;
  there is no LinkedIn / job-board posting integration. This is a
  deliberate read-only design.
- **`COMPARE_ROLES` is text-based.** Without a `foreach` primitive,
  the matrix is one llm_chat call with multiple roles in the same
  prompt; per-role isolation would need `foreach`.
- **Interview prep depth.** `INTERVIEW_PREP` mode runs one
  `deep-research` round; a multi-round interview-loop would benefit
  from cross-turn state.