Positioning Mapper
Define the alternatives, differentiated value, beachhead segment, and launch positioning before writing launch copy.
The file requires interviews, exports, and scraped content to be treated as untrusted data, restricts registry changes to authorized propose flows, and asks before writing working memory. It uses keyless Tier-1 connectors and discloses third-party data egress through Firecrawl/Tavily. 7 points are deducted because least-privilege isolation, rollback, secret-inaccessible deployment, and enforcement by the referenced runtimes cannot be verified from the supplied files alone.
The trigger, inputs, outputs, done conditions, failure stops, and routing when audience evidence is missing are specific and internally consistent. 1 point is deducted because the referenced connectors, registry runtime, and shared protocol files are not supplied, so key-path reproduction is unavailable; static calibration also caps this dimension at 10.
The evidence gives clear scenarios, argument hints, capability boundaries, non-fit cases, and semantic triggers, with bilingual metadata and guidance. 5 points are deducted because geo-relevance is explicitly low and the material does not establish mainland-China reachability, Chinese-input quality, or a clear fallback when overseas retrieval services are unavailable.
The evidence includes a stable name and slug, version 18.0.0, Apache-2.0 licensing, Quick Starts, contract sections, references, save rules, next-skill guidance, and termination rules. 5 points are deducted because maintenance ownership, an explicit update path, and a changelog are not sufficiently shown in the selected skill or supplied evidence; publisher identity is not registry-verified.
The expected canvas is concrete and includes alternatives, attributes, value chains, segment scoring, and an onlyness statement, with defined downstream use. 9 points are deducted because no representative output or third-party execution evidence is supplied, leaving correctness, completeness, and marginal benefit over manual work for human review; static calibration caps this dimension at 7.
The skill requires verifiability labels, separates Measured/User-provided/Estimated data, and routes unsupported claims to a claims candidate ledger. 6 points are deducted because there are no submitted examples, independent reproduction records, or selected-skill key-path tests; repository-wide tests do not establish execution of this skill.
- The core workflow depends on unsupplied shared protocols, registry runtime, and Firecrawl/Tavily connectors; verify their actual permissions, reachability, and failure behavior before use.
- Competitor retrieval creates third-party data-egress and compliance risks; do not submit confidential URLs, unauthorized material, or sensitive interview content.
- Do not treat Estimated or [needs source] items as competitive or market facts until human approval and supporting evidence are added.
What does this skill do, and when should you use it?
Positioning Mapper is the launch-research skill in Aaron Marketing Skills. It produces a Dunford-style positioning canvas covering the alternatives users actually consider, verifiable unique attributes, attribute-to-benefit-to-value themes, and a scored beachhead segment. It also writes a one-sentence onlyness statement and hands the canvas to message-house-builder. It does not build the message house, personas, SEO keyword positioning, or final claims and RAMP scores.
It reads user-provided product facts, capability lists, win-loss reasons, and interview notes, plus available competitor-analysis findings and launch-stage records. It can retrieve competitor public messaging through the repository's Firecrawl or Tavily connectors. It produces named alternatives, including non-vendor options; unique attributes with verification status; attribute→benefit→value chains; beachhead scores for serviceability, pain intensity, and reachability; an onlyness statement; and a standard handoff summary. Unverifiable or comparative attributes are marked “[needs source]” and may be submitted as claims proposals through registry-events.py.
- A product team needs defensible positioning before creating launch assets or copy.
- A founder wants to turn win-loss notes and user interviews into a positioning canvas.
- A launch lead wants to test whether an existing one-liner survives comparison with named alternatives.
- A marketing team needs to choose a beachhead segment based on serviceability, pain intensity, and reachability.
What are this skill's strengths and limitations?
- Produces a concrete canvas spanning alternatives, differentiated attributes, value chains, segment scoring, and an onlyness statement.
- Requires non-vendor alternatives such as spreadsheet, manual process, and do nothing.
- Separates verifiable attributes from unsupported or comparative claims.
- Works with Claude Code and compatible Agent Skills hosts and is licensed under Apache-2.0.
- It only produces the positioning canvas, not the message house, taglines, or per-channel launch copy.
- It cannot score a beachhead without sufficient audience or persona evidence.
- Connector and registry workflows may require local files, scripts, and network access.
- The supplied source does not provide independent test results or a standalone execution example for this skill.
How do you install this skill?
To install the collection in Claude Code, run /plugin marketplace add aaron-he-zhu/aaron-marketing-skills, then /plugin install aaron-marketing@aaron. On another Agent Skills-compatible host, run npx skills add aaron-he-zhu/aaron-marketing-skills, or clone the repository with git clone https://github.com/aaron-he-zhu/aaron-marketing-skills.
How do you use this skill?
Use a prompt such as: Map the positioning for [product]. Users today solve this with [alternatives]. Candidate segments: [list or "help me choose"]. You can also paste win-loss notes and interviews and ask the skill to build the canvas and pick the beachhead. Confirm the product, stage, and launch scope first. If audience evidence is missing, route to audience-mapper before scoring segments. The primary next skill is message-house-builder.
How does this skill compare with similar options?
Compared with message-house-builder, this skill is the upstream positioning step rather than the messaging-hierarchy and PR-FAQ step. Compared with audience-mapper, it does not build persona or audience profiles. Compared with keyword-research, it does not perform SEO keyword positioning. Its alternative set must include spreadsheet, manual process, an adjacent tool, and do nothing.