Impact Tracker
Capture a pre-intervention baseline of store metrics, snapshot weekly performance (sessions, conversion, units, revenue per ASIN), and diff over time to prove whether changes worked. Use when the user asks "did it work", "track the changes", or before making any batch of store changes.
Install
$ agentstack add skill-ebrahimelbagory-claude-fba-impact-tracker ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README — it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-ebrahimelbagory-claude-fba-impact-tracker) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming — see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps — measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Measure everything: baseline → intervene → diff
Changes without a baseline are anecdotes. This skill makes every intervention attributable.
1. Baseline BEFORE intervening
Save data/baseline-.json with, per ASIN: BSR (category + subcategory), review count, rating, listing quality score, top-10 keyword count + captured search volume, key keyword ranks, price, velocity, stock. Plus:
interventions: [] — every change gets appended with {date, action, detail,
expected_effect}. This is what turns a later diff into attribution.
success_criteria_30d: concrete targets ("ASIN X reviews ≥ 75", "keyword Y
top-3") the next audit checks mechanically.
2. Automated weekly snapshots (SP-API side)
python3 scripts/track_impact.py pulls the Sales & Traffic report (child-ASIN granularity, trailing 7 days) and appends {units, sessions, cvr_pct, revenue} per ASIN to data/weekly_tracking.jsonl. Install automation/com.example.track-impact.plist (Mondays 09:00) or a cron line. --report prints first-vs-latest.
Conversion (unit-session %) is the fastest truth signal: it moves within days of a listing change, long before rank or reviews do — and it separates traffic problems (sessions down) from listing problems (conversion down).
3. Session-side re-pulls (Helium 10 MCP; needs a Claude session)
Weekly: get_listing_details (reviews/rating/BSR) + get_top_keywords (rank + coverage) for the main ASINs. These can't run from cron — the MCP needs an authenticated session — so pair the automated tracker with a weekly /store-audit.
4. Attribution discipline
- Compare against an in-catalog control (an ASIN you did NOT touch) to separate
seasonality from effect — category-wide dips fool absolute numbers.
- Expected lag: conversion moves in days; backend-keyword indexation in days;
review counts in 1–3 weeks; organic rank in 2–4 weeks (faster with a CPR-sized push).
- Log EVERY intervention (even small ones) in the baseline's
interventions
array with a timestamp — future you cannot reconstruct this.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: EbrahimElbagory
- Source: EbrahimElbagory/claude-fba
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.