agentsclimarketplace

Researching gourmet venues

Skill narumiruna/skills/deprecated/researching-gourmet-venues

Install
npx -y skills add narumiruna/skills --skill researching-gourmet-venues

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  • 8 stars8 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

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Deprecated internal workflow for auditable city-based food research with multi-source evidence, scope-checked rankings, standardized scoring, and synchronized candidate, recommendation, and exclusion files.

SKILL.md

3.1 KB, as published. Nobody here has run it

Researching Gourmet Venues (Deprecated Reference)

This rarely used workflow is excluded from active discovery but retained for explicit local compatibility. Keep evidence, scores, and decisions traceable across a six-file city research pack.

Initialize

  1. Resolve city and output language. Ask once only when language is unspecified, then record it in overview.md.
  2. Copy assets/templates/ into gourmet/<city-slug>/ to create overview.md, inbox.md, candidates.md, notes.md, top-places.md, and excluded.md; replace placeholders before research.
  3. Preserve original-language venue names unless the user requests translation.

Never fabricate sources, ratings, hours, or claims. Use unknown. Never delete a candidate: mark it rejected and record the reason in excluded.md.

Research and Score

  1. Capture raw discoveries in inbox.md, then move viable entries to candidates.md with status.
  2. Build a notes.md evidence block for each candidate with practical constraints. Require four independent source roles by default: official channel, maps/aggregator, local reviews, and guide/editorial.
  3. In an information-sparse locale, use three sources only after recording evidence: limited, why, and attempted sources. Do not score or publish a recommendation with fewer sources outside this exception.
  4. When repeated service complaints, hygiene/safety concerns, tourist-trap claims, extreme queues, inconsistent ratings, or unclear access appear, add a focused negative-review section and reflect it in scoring.
  5. Score and justify each component: Taste/Quality, Value, Convenience, Consistency, and Risk (0–10 each; higher Risk score means lower practical risk).
  6. Classify totals: Top Pick ≥35, Backup 30–34, Reject <30 or a hard safety/tourist-trap exclusion.
  7. Synchronize status and score across notes.md, candidates.md, top-places.md, and excluded.md. Leave no unresolved inbox status in the final pack.

Ranking Retrieval

Before returning “highest score” or top-N results, confirm:

  • exact geographic boundary, including suburbs, islands, or neighboring regions
  • overall versus cuisine/category scope
  • source URL/page title and any area identifier

Handle consent, language, or location gates so list items actually render. If static extraction fails, use available browser tooling; do not substitute a nearby ranking scope. Record exclusions needed to enforce the requested boundary.

Verification

Check all six files exist; language and original-name policy are recorded; claims and ratings have sources or unknown; limited evidence and negative-review triggers are documented; score arithmetic and thresholds are correct; and every candidate appears consistently in recommendation or exclusion outputs.

Return the requested recommendations first, then source coverage, score rationale, practical caveats, and any unresolved evidence gap.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.