gauntlet-curate

Gauntlet Curate

Survey the DSA problem bank, identify coverage gaps, and propose new YAML entries for human review.

When NOT To Use

• Annotating this codebase rather than the problem bank (use gauntlet:curate)
• Rebuilding the knowledge base (use gauntlet:extract)

When This Skill Fires

Invoke this skill manually with Skill(gauntlet:gauntlet-curate) when the problem bank needs a coverage review. The skill is intended to participate in /update-plugins runs but is not yet wired into that command (see openpackage.yml registration). It is distinct from gauntlet:curate, which handles per-annotation knowledge capture and is what /gauntlet-curate invokes today.

Steps

1. Locate the problem bank at plugins/gauntlet/data/problems/. Read _manifest.yaml to load the expected NeetCode counts per category.

2. Survey current coverage by counting problems in each YAML file (skipping _manifest.yaml). Run the analysis script:

   cd plugins/gauntlet
   python scripts/curate_problems.py data/problems/ --output /tmp/gauntlet-curate-report.md
   

3. Identify gaps: categories whose actual count falls below the neetcode_count in the manifest. The script sorts gaps largest-first so the worst shortfalls appear at the top.

4. Review existing problems in each gap category to understand what is already covered before proposing additions.

5. Propose new YAML entries following the schema below. Add proposals to the report under "Proposed New Problems". Do NOT write proposals directly into data/problems/*.yaml.

6. Validate proposals by running:

   python -c "
   import yaml, sys
   sys.path.insert(0, 'src')
   from gauntlet.models import BankProblem
   proposals = yaml.safe_load(open('proposals.yaml'))
   for p in proposals:
       BankProblem.from_dict(p)
   print('All proposals valid.')
   "
   

7. Present the report to the human for review. The report includes the coverage table, gap list, and proposed entries. The human decides which proposals to merge into the YAML files.