/problem-bank

name: problem-bank
description: Audits the DSA problem bank for coverage gaps and proposes new YAML entries. Use when the problem bank needs a coverage review.

Problem Bank

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 with `Skill(gauntlet:problem-bank)` when the problem bank needs a coverage review. It is distinct from `gauntlet:curate`, which handles per-annotation knowledge capture and is what the `/gauntlet-curate` command invokes.

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/problem-bank-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.

Problem Schema

Each proposed entry must follow this schema:

- id: category-NNN
  title: Problem Title
  difficulty: easy       # easy | medium | hard | extra_hard
  prompt: |
    Problem statement with constraints and examples.
  hints:
    - First hint.
    - Second hint.
  solution_outline: |
    Approach and time/space complexity.
  tags: [tag1, tag2]
  neetcode_id: neetcode-NNN
  challenge_type: explain_why  # explain_why | multiple_choice | trace
                                # | code_complete | debug | rank

Required fields: `id`, `title`, `difficulty`, `prompt`. Optional fields default to empty values.

Safety Constraints

• Never modify files under `data/problems/` directly.
• Never run with a `--write` or `--fix` flag: the script

intentionally has none.

• All output is a proposal report for human approval.
• Existing hand-curated problems are never touched.

Output

A markdown report at the path specified by `--output`, containing:

• Coverage summary table (expected vs. actual per category)
• Gaps list sorted by missing count
• YAML schema template for new proposals
• Space for the human reviewer to add proposed entries

Human review is required before any YAML file changes.

Exit Criteria

• [ ] Script runs without error and produces a markdown report at the

path given to `--output` (e.g., `/tmp/problem-bank-report.md`)

• [ ] Report contains a coverage summary table comparing expected

`neetcode_count` versus actual count for every category in `_manifest.yaml`, with gaps sorted largest-first

• [ ] No files under `plugins/gauntlet/data/problems/` are modified;

all proposals appear only in the report under "Proposed New Problems"

• [ ] Every proposed YAML entry passes `BankProblem.from_dict()`

validation (required fields: `id`, `title`, `difficulty`, `prompt`) before appearing in the report