Pricing Strategy Advisor

Turn pricing notes, margin pressure, competitor context, discount behavior, and channel constraints into a practical pricing strategy brief with objective fr...

Security Scan

This skill is internally consistent: it generates heuristic pricing briefs from user-supplied context, contains no network calls or credential requests, and its implementation aligns with the written SKILL.md.

Purpose & Capability

The name/description (pricing strategy advisory) matches the code and SKILL.md. The skill requires no credentials, no binaries, and does not attempt to integrate with unrelated services; all required inputs are user-supplied pricing/context data which is appropriate for the stated purpose.

Instruction Scope

SKILL.md explicitly forbids fetching live competitor or analytics data and the handler.py implementation follows that: it uses only local text normalization and keyword heuristics to create a markdown brief. Note: the logic is heuristic (keyword matching) and may misclassify objectives or underweight nuance without richer user data — this is a functional limit rather than a security issue.

Install Mechanism

No install specification is provided (instruction-only), so nothing is downloaded or written to disk by an installer. The included code files are simple, self-contained Python with no external package pulls or shell/network operations.

Credentials

The skill requests no environment variables, no credentials, and references no config paths. There are no tokens, keys, or secrets required or accessed by the code.

Persistence & Privilege

always is false and the skill does not attempt to persist configuration, modify other skills, or request elevated platform privileges. It follows normal, limited runtime behavior.

Assessment

This skill appears safe and does what it says: it produces heuristic pricing briefs from user-provided context and asks for no credentials or external data. A few practical notes before installing or using it: (1) verify the full handler.py file in the package (the manifest shown to you had a truncated snippet — confirm the complete source on disk or in the registry before trusting it); (2) outputs are heuristic keyword-based recommendations — do not auto-apply price changes without human review or A/B testing; (3) avoid pasting sensitive credentials or unneeded PII into prompts (the skill does not need secrets); (4) if you need rigorous elasticity or live competitor data, feed validated analytics or run experiments rather than relying solely on this advisor; (5) the agent can be invoked autonomously by default on the platform, but here that is not a high risk because the skill has no network or credential access — still consider limiting autonomous runs if you prefer manual invocation.

Overview

Use this skill to structure pricing decisions when the team has partial information but still needs a clear commercial recommendation. It helps with pricing architecture, margin recovery, launch pricing, and discount discipline.

This MVP is heuristic. It does not fetch live competitor prices, cost feeds, marketplace data, or conversion dashboards. It relies on the user's supplied context and assumptions.

Trigger

Use this skill when the user wants to:

• decide whether to raise, hold, narrow, or tier prices
• recover margin without blindly killing conversion
• tighten discount discipline or promo hygiene
• design a price ladder, bundle, or good-better-best structure
• prepare a pricing memo before a launch, seasonal reset, or negotiation

Example prompts

• "Help me decide whether we should raise prices or cut discount depth"
• "Design a better pricing ladder for our core line"
• "We have competitor pressure and rising costs. What pricing moves make sense?"
• "Create a pricing test plan for this product launch"

Workflow

1. Clarify the pricing objective and what business trade-off matters most.
2. Normalize the strongest signals, such as cost, conversion, competitor pressure, and discount behavior.
3. Separate structural pricing issues from temporary promo, inventory, or channel noise.
4. Recommend a short list of moves with guardrails and experiment ideas.
5. Return a markdown strategy brief with risks, metrics, and assumptions.

Inputs

The user can provide any mix of:

• current and target prices, margins, or cost changes
• competitor references or marketplace context
• conversion, volume, AOV, or bundle behavior
• promo cadence, coupon usage, or markdown history
• channel constraints such as MAP, retailer parity, or marketplace conflict
• inventory pressure, premium positioning, or launch goals

Outputs

Return a markdown pricing brief with:

• primary pricing objective
• pricing posture summary
• recommended moves and trade-offs
• test design ideas and guardrails
• metrics to monitor and escalation notes
• assumptions, confidence notes, and limits

Safety

• Do not claim access to live competitor or conversion data.
• Do not present price elasticity as proven unless the user supplies evidence.
• Avoid auto-approving permanent price increases, channel exceptions, or MAP violations.
• Keep final pricing changes human-approved.
• Downgrade confidence when channel conflict or unit economics are unclear.

Best-fit Scenarios

• DTC and marketplace brands reviewing price strategy quarterly or around major campaigns
• operators who need a structured recommendation before changing discount or ladder logic
• founders balancing growth, margin, and positioning without a full pricing stack
• consultants preparing a first-pass pricing memo

Not Ideal For

• real-time competitor repricing systems
• heavily regulated pricing environments that require legal review
• highly quantitative elasticity modeling with experimental control data
• workflows that must write prices directly into commerce systems

Acceptance Criteria

• Return markdown text.
• Include objective, recommendation, guardrail, and monitoring sections.
• Keep the advisory framing explicit.
• Make trade-offs practical for operators and decision-makers.