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Run Intake

ASecurity

Starts an intake run over a folder of model files, acting as director for the catalogue department. A person starts it; it spawns catalogue-lead and writes report.md in the run's directory under runs/.

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  • Added October 16, 2026
ai-agentspythongobashdebuggingapi

Works with

  • api

Security analysis

A100/100

Pro scans all 2 files and shows the line behind each finding

Scanned October 16, 2026

npx -y skills add 3DAPI-Inc/dapilot --skill run-intake --agent claude-code

Installs into .claude/skills of the current project.

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SKILL.md
---
name: run-intake
description: Starts an intake run over a folder of model files, acting as director for the catalogue department. A person starts it; it spawns catalogue-lead and writes report.md in the run's directory under runs/.
disable-model-invocation: true
argument-hint: "[folder] [run-id]"
allowed-tools: Read, Write, Bash, Agent(dapilot:catalogue-lead)
---

# run-intake

You are the director for this run. You route; you do no domain work.

Prefer `/dapilot:quickstart` for a person's run: it starts the headless
director (`run_intake.py`), whose workers run with permissions skipped and
whose only stop is the price gate. This skill runs the same stages as
subagents inside the session, and every script call then goes through the
session's permission mode, which is where "approve this command" prompts
come from. It is for development and debugging, not the product path.

## Procedure
1. `FOLDER=$0`, `RUN=${1:-intake-$(date -u +%Y%m%dT%H%M%S)}`,
   `PY=${DAPILOT_PYTHON:-python3}`, `L=${CLAUDE_PLUGIN_ROOT}/scripts/ledger.py`.
2. Check `store/config.json` exists (else say so and stop) and that
   `$PY -c "import trimesh, scipy, networkx, jsonschema"` succeeds (else
   report which import failed and stop). Do not check `DAPI_API_KEY` here;
   the enabled stages say whether it is needed.
3. `$PY $L init --run $RUN --folder $FOLDER --department catalogue`. Zero
   items: report and stop.
4. Spawn one `catalogue-lead` with the prompt `run: $RUN`. Wait for it.
5. Validate the lead's last line with `validate_result.py --expect run=$RUN
   --expect stage=lead`. If the lead produced no result line (it was cut
   off, for example by an API overload) run `$PY $L status --run $RUN`; if
   `work_left` is true, spawn the lead once more. The ledger is idempotent,
   so a second lead resumes rather than repeats. Never a third time.
6. `$PY $L report --run $RUN`, then read `runs/$RUN/report.md` and give the
   person: the table, anything waiting for them with the exact command to
   clear it, failures, and the cost line. Nothing else.

## Do not
- Do not analyse, render, quote or write copy yourself.
- Do not spawn workers directly. Only the lead does.
- Do not re-run the lead more than once. Report what the ledger shows.

Stage table: `reference/stages.json`. Only stages with `enabled: true` run.

Files in this skill

  • SKILL.md2.3 KB
  • reference/stages.json3.7 KB

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