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LangChainKnown publisherlangchain-ai/langsmith-agent-lifecycle-workshop/agents/pricing_research/skills/competitive-pricing/SKILL.md

competitive-pricing

Skill52 starsChanged 11 days agoApache-2.0View on GitHub

Produce a competitive pricing report comparing TechHub's catalog prices against current market prices, with flagged products and recommendations. Use when asked to benchmark prices, review pricing, or recommend price changes.

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 0 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Write the report to /reports/<category>_pricing.md.

    line 14
  2. Very specific:

    Only recommend; changes go through propose_price_change and need approval.

    line 28

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Competitive pricing report

Steps

  1. Get our current prices for the requested products or category from the catalog-analyst.
  2. For each product, ask the market-researcher for current prices at 3+ major US retailers (for example Amazon, Best Buy, the manufacturer's store), with URLs and whether each is a sale price.
  3. For each product compute: market median, our price, and the gap ((ours - median) / median).
  4. Flag products more than 5% above or 10% below the median.
  5. Write the report to /reports/<category>_pricing.md.

Report format

ProductOur priceMarket medianGapFlagSources

Sources are citation numbers, e.g. [1][2][3].

Then:

  • Recommendations: one line per flagged product (raise, lower, hold), with the reason.
  • Watch list: promotions or model refreshes that could move prices soon.
  • Sources: ### Sources, one line per number: [1] Retailer - product: URL.

Only recommend; changes go through propose_price_change and need approval.

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