HK IPO Review Optimizer

Review past Hong Kong IPO decisions, update actual outcomes, export review datasets, and accept or reject tuning suggestions. Use when the user wants to learn from completed IPO calls and improve later scoring behavior.

Use this skill for the post-decision feedback loop.

Runtime

This publish bundle includes the required CLI runtime under runtime/hkipo-next.

From the skill folder:

bash

cd <skill_dir>
uv run --directory runtime/hkipo-next hkipo-next ...

By default review history is stored in ~/.hkipo-next/data/hkipo.db.

Workflow

1. Use review list to find target records.
2. Use review show before updating a record.
3. Use review update to add actual results and variance notes.
4. Use review export when another tool needs a JSON dataset.
5. Use apply-suggestions show before accepting or rejecting imported suggestions.

Commands

List recent review records:

bash

cd <skill_dir>
uv run --directory runtime/hkipo-next hkipo-next review list --limit 20 --format json

Show a record:

bash

cd <skill_dir>
uv run --directory runtime/hkipo-next hkipo-next review show rvw_123 --format json

Update actual results:

bash

cd <skill_dir>
uv run --directory runtime/hkipo-next hkipo-next review update rvw_123 \
  --allocated-lots 2 \
  --listing-return-pct 14.2 \
  --exit-return-pct 9.8 \
  --realized-pnl-hkd 1860 \
  --notes "Sold into first-day strength" \
  --variance-note "Grey market was weaker than expected but sponsor demand held" \
  --format json

Export a review dataset:

bash

cd <skill_dir>
uv run --directory runtime/hkipo-next hkipo-next review export --from 2026-04-01 --to 2026-04-16 --output /tmp/hkipo-review.json --format text

Preview and accept a suggestion:

bash

cd <skill_dir>
uv run --directory runtime/hkipo-next hkipo-next apply-suggestions show sgg_123 --format json
uv run --directory runtime/hkipo-next hkipo-next apply-suggestions accept sgg_123 --reason "Matches observed listing-day slippage" --format json

Output Cues

• Review records preserve the original prediction payload, data status, and source issue count.
• Suggestion detail output shows whether a proposed change would create a new parameter version.

Companion Skills

• Use $hkipo-parameter-manager when a review conclusion needs manual tuning work.
• Use $hkipo-decision-engine to rerun fresh decisions after a rule change.