repeat-purchase-flow-review-ecommerce
Reviews e-commerce lifecycle flows from email/SMS exports, screenshots, product cycle data, and purchase timing to find second-purchase and retention…
Skill by mardab96
Chalk Labs
Need help deploying repeat-purchase-flow-review-ecommerce?
Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/mardab96-repeat-purchase-flow-review-ecommerce
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
Get the weekly diff
The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.
About
Use cases: data, productivity
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/repeat-purchase-flow-review-ecommerce
What's inside
- Skill name
repeat-purchase-flow-review-ecommerce- Description
- Reviews e-commerce lifecycle flows from email/SMS exports, screenshots, product cycle data, and purchase timing to find second-purchase and retention opportunities. Use when repeat purchase is weak, email revenue is low, or post-purchase flows need review.
- License
- MIT
- Instructions
- 117 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Repeat Purchase Flow Review - E-commerce ## Use this skill when The user wants to improve retention, repeat purchase, or lifecycle marketing. Common requests: - "Review our Klaviyo flows." - "How do we get more second purchases?" - "Audit post-purchase email/SMS." - "Find missing lifecycle flows." ## Required input Minimum useful input: - Email/SMS flow screenshots or exports. - Flow names, triggers, timing, and message summaries. - Revenue, open rate, click rate, conversion rate where available. - Product category and typical replenishment or repurchase cycle. - Customer segments if available. Optional: - Cohort repeat-purchase data. - First-order products and second-order products. - Review, support, or return themes. - Promo calendar. ## Before analysis 1. Confirm whether products are replenishable, seasonal, gifting, fashion, durable, or one-time purchases. 2. Confirm the target outcome: second purchase, cross-sell, replenishment, loyalty, review generation, or win-back. 3. Avoid generic flow advice when purchase cycle is unknown. ## Analysis workflow 1. Map existing flows: - welcome - browse abandonment - cart abandonment - post-purchase - education/use - replenishment - cross-sell - win-back 2. Compare timing to product usage and buying cycle. 3. Check message fit: - reason to buy again - product education - next-best product - proof - objection handling - customer care 4. Find missing segments and over-message risks. 5. Recommend the next 3 retention tests. ## Decision and evidence standard Every finding should include: - Evidence tag: `export`, `screenshot`, `url`, `review_cluster`, `support_ticket`, `return_reason`, `policy`, `feed_diagnostics`, `margin_csv`, `inventory_export`, `hypothesis`, or `needs_data`. - Severity: low, medium, high, or critical. - Confidence: low, medium, or high. - Business impact: revenue, margin, cashflow, retention, conversion, support_load, or risk. - Effort: XS, S, M, or L. - Owner decision: do_now, test, investigate, monitor, ignore, or approval_needed. If the evidence is weak, mark the finding as `hypothesis` or `needs_data` and lower confidence. ## Output format ### Retention verdict Short summary of the biggest lifecycle gap. ### Flow table | Flow | Status | Evidence | Gap | Recommended change | |---|---|---|---|---| ### Second-purchase opportunities List product and timing hypotheses. ### Measurement plan Metrics to monitor for each proposed change. ## Example input and output Input: - flow screenshots - flow performance export - product replenishment cycle - first and second order product mix Good output excerpt: | Finding | Evidence | Severity | Confidence | Business impact | Effort | Owner decision | |---|---|---|---|---|---|---| | No post-purchase education before likely second-use moment | `screenshot`, `hypothesis` | medium | low | retention/support_load | S | test | | Replenishment flow timing is later than the product usage cycle | `export`, `policy` | high | medium | retention | M | investigate | What not to do yet: default to discounts before checking product cycle, customer education, and cross-sell fit. ## Guardrails - Do not write final customer emails unless brand voice and offer rules are provided. - Do not recommend heavy discounting as the default retention tactic. - Do not assume one purchase cycle fits all products. - Do not send or schedule messages. - Do not invent segmentation data.
From mardab96/ecommerce-claude-skills, MIT.
More from mardab96/ecommerce-claude-skills
- 0.584score
returns-reason-miner-ecommerce
mardab96
Mines e-commerce returns exports, refund reasons, support notes, and product page data to find preventable return patterns and fixes for product pages…
SkillBFreshness: B★ 7Claude Code +9 - 0.584score
weekly-ecommerce-growth-readout
mardab96
Produces a weekly e-commerce owner readout from exported store, analytics, ads, email/SMS, inventory, support, and returns summaries.
SkillBFreshness: B★ 7Claude Code +9 - 0.584score
ai-shopping-readiness-ecommerce
mardab96
Checks whether a store's product data, policies, and structured markup can be read and represented correctly by AI shopping assistants and agentic checkout…
SkillBFreshness: B★ 7Claude Code +9 - 0.584score
catalog-content-audit-ecommerce
mardab96
Audits product content across a whole catalog rather than one page, finding missing attributes, thin descriptions, duplicate copy, weak imagery coverage, and…
SkillBFreshness: B★ 7Claude Code +9 - 0.584score
chargeback-fraud-pattern-review-ecommerce
mardab96
Reviews chargebacks, disputes, and fraud declines to separate genuine fraud from service failures and friendly fraud, and to find the store-side causes behind…
SkillBFreshness: B★ 7Claude Code +9 - 0.584score
contribution-margin-check-ecommerce
mardab96
Turns COGS, fee, shipping, refund, and ad spend exports into per-SKU contribution margin so the store can see which products actually make money after all…
SkillBFreshness: B★ 7Claude Code +9 - 0.584score
inventory-risk-brief-ecommerce
mardab96
Turns e-commerce inventory and sales exports into a practical risk brief for stockouts, overstock, promo conflicts, and traffic allocation.
SkillBFreshness: B★ 7Claude Code +9 - 0.584score
launch-readiness-check-ecommerce
mardab96
Runs a go/no-go readiness check before a product launch, restock, promo, or spend increase, covering stock, page, feed, tracking, lifecycle flows, support…
SkillBFreshness: B★ 7Claude Code +9 - 0.584score
ltv-cohort-read-ecommerce
mardab96
Reads customer cohorts from order exports to show repeat rate, time to second order, payback window, and which acquisition sources or first products produce…
SkillBFreshness: B★ 7Claude Code +9 - 0.584score
onsite-search-merchandising-review-ecommerce
mardab96
Turns internal site search queries, zero-result searches, and collection performance into merchandising, naming, and assortment decisions.
SkillBFreshness: B★ 7Claude Code +9 - 0.584score
paid-traffic-waste-triage-ecommerce
mardab96
Reviews paid traffic exports and store-side data to identify whether performance issues come from ads, product pages, offers, feed quality, tracking, margins…
SkillBFreshness: B★ 7Claude Code +9 - 0.584score
product-feed-qa-ecommerce
mardab96
Audits e-commerce product feed exports, Google Merchant Center diagnostics, catalog screenshots, and product page samples to find data quality issues that can…
SkillBFreshness: B★ 7Claude Code +9
Signals
- Stars
- 7
- Forks
- 3
- Open issues
- 0
- Last commit (this folder)
- Jun 1, 2026 (5 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from mardab96
- 0.584score
weekly-ecommerce-growth-readout
mardab96
Produces a weekly e-commerce owner readout from exported store, analytics, ads, email/SMS, inventory, support, and returns summaries.
SkillBFreshness: B★ 7Claude Code +9 - 0.584score
ai-shopping-readiness-ecommerce
mardab96
Checks whether a store's product data, policies, and structured markup can be read and represented correctly by AI shopping assistants and agentic checkout…
SkillBFreshness: B★ 7Claude Code +9 - 0.584score
catalog-content-audit-ecommerce
mardab96
Audits product content across a whole catalog rather than one page, finding missing attributes, thin descriptions, duplicate copy, weak imagery coverage, and…
SkillBFreshness: B★ 7Claude Code +9 - 0.584score
chargeback-fraud-pattern-review-ecommerce
mardab96
Reviews chargebacks, disputes, and fraud declines to separate genuine fraud from service failures and friendly fraud, and to find the store-side causes behind…
SkillBFreshness: B★ 7Claude Code +9 - 0.584score
contribution-margin-check-ecommerce
mardab96
Turns COGS, fee, shipping, refund, and ad spend exports into per-SKU contribution margin so the store can see which products actually make money after all…
SkillBFreshness: B★ 7Claude Code +9 - 0.584score
returns-reason-miner-ecommerce
mardab96
Mines e-commerce returns exports, refund reasons, support notes, and product page data to find preventable return patterns and fixes for product pages…
SkillBFreshness: B★ 7Claude Code +9
Done for you
Rather not set it up yourself?
Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
See how we build AI agents →Read the case studies
Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
- launches shipped
- 50+
- launches shipped
- media placements
- 500+
- media placements
- founders on every account
- 2
- founders on every account
Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
Get repeat-purchase-flow-review-ecommerce running in your business
Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.
Cite this page
repeat-purchase-flow-review-ecommerce skill. Agent Mart. https://agentmart.dev/skills/mardab96-repeat-purchase-flow-review-ecommerce (last updated 2026-10-24)