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store-signals

store-signals is a skill for Claude Code from rshankras/claude-code-apple-skills. It costs 105 tokens per session (1,450 once invoked), scanned A, original, MIT.

A workflow for studying what a live app’s App Store data is saying. It collects reviews, analytics, sales, crashes, and listing-conversion signals, then turns them into a dated backlog and checks earlier improvement ideas.

In plain words
What is it for?
Reviewing post-launch performance, grouping feedback and problems, creating metric-linked backlog items, and checking whether previous releases achieved their intended results.
Why use it?
It connects shipped changes to measurable results so the next development plan is based on observed user and business signals.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: names the AskUserQuestion tool.

Part of the apple-skills plugin — 147 skills shipped together , and of apple-skills

not rated 719repo +13 1mo ago A scan SkillSpector: pass 105 tokens original MIT

Good fit Reviewing post-launch performance, grouping feedback and problems, creating metric-linked backlog items, and checking whether previous releases achieved their intended results.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/rshankras/claude-code-apple-skills/store-signals
View source ↗ rshankras/claude-code-apple-skills
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add rshankras/claude-code-apple-skills --skill store-signals
Clone the repo
git clone --depth 1 https://github.com/rshankras/claude-code-apple-skills

Made for: Claude Code.

Or install apple-skills, the plugin that ships this one along with the rest of its 147 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for store-signals

README.md
[![agentmods](https://agentmods.dev/badge/skills/rshankras/claude-code-apple-skills/store-signals/github.svg)](https://agentmods.dev/skills/rshankras/claude-code-apple-skills/store-signals)
Your own site
<a href="https://agentmods.dev/skills/rshankras/claude-code-apple-skills/store-signals"><img src="https://agentmods.dev/badge/skills/rshankras/claude-code-apple-skills/store-signals/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for store-signals

Your own site · 80×15
<a href="https://agentmods.dev/skills/rshankras/claude-code-apple-skills/store-signals"><img src="https://agentmods.dev/badge/skills/rshankras/claude-code-apple-skills/store-signals.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 105 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,450 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00105 $0.01450
Opus 5 $0.00053 $0.00725
Sonnet 5 $0.00021 $0.00290
Haiku 4.5 $0.00011 $0.00145

Measured 7d ago against content hash f0332edb1f44, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

store-signals scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/growth/store-signals/SKILL.md · 80 lines

How it starts

The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Store Signals

Pull what the shipped app is actually telling you and convert it into the next backlog — then verify whether last cycle's bets paid off.

This is the missing arc that turns build → ship into a loop: ship → MEASURE → DIAGNOSE → next PLAN → build → ship → measure again… The ledger (SIGNALS.md) is what makes it a loop and not a monthly report.

Where it fits (read the seams)

  • Not analytics-interpretation. That interprets a metric you hand it (is 14% D7 good?). This is the end-to-end operate loop: gather every signal → cluster → diagnose → write a metric-tagged backlog → close last cycle's hypotheses. It uses analytics-interpretation's benchmarks.
  • Read-only on ASC. Never responds to reviews, never mutates metadata/pricing. It surfaces, gates on explicit OK, and routes the change to the right command (next-version, bugfix, metadata).
  • Feeds planning. Output is a dated backlog appended to ROADMAP.md + rows in SIGNALS.md, consumed by /apple:next-version / /apple:release.

Prerequisites

  • A live (or TestFlight) app; resolve its appId from .planning/STATE.md, else list_apps + confirm.
  • .planning/ context: STATE.md, APP.md, POSITIONING.md (job-to-be-done + guardrails).
  • .planning/SIGNALS.md if present — the OPEN hypotheses from prior runs (each with a target metric, recorded baseline, and "check-after" date). See signals-ledger.md for the ledger + backlog formats.

Flow

  1. Load prior hypotheses. Read SIGNALS.md → the OPEN rows to verify in step 5.
  2. Pull the signals (read-only), this period vs trailing:
    • Reviews / ratings — list_reviews (recent, lowest-star first; flag unanswered), get_review for detail.
    • Analytics — get_analytics_report: retention, funnel/conversion, acquisition, impression→download. No report configured yet → setup_analytics_reports and note "retention/funnel lands next cycle."
    • Sales — get_sales_report: proceeds/units vs trailing 7/30-day.
    • Stability / perf — get_diagnostics (crash/hang signatures) + get_perf_metrics (launch, memory, energy).
    • Beta — list_beta_feedback_crashes if in TestFlight.
    • Listing — get_metadata to spot ASO conversion problems against current copy.
  3. Normalize & cluster. Dedupe reviews into recurring themes (requests / complaints / praise) with frequency; attach magnitude (users / revenue / retention implicated). Weight by frequency × revenue impact, not by how loud one reviewer is.
  4. Diagnose, filter, prioritize. Map each cluster to the core metric it moves (rating · D7 · Pro conversion · crash-free rate · ASO conversion · proceeds); score impact × confidence ÷ effort. Strategy filter: cross-check POSITIONING.md — on-strategy → backlog; off-strategy → list under "Declined (why)" (never silently drop, never silently build). Carry the app's guardrails forward. Small-N (new app): say so, lean on qualitative reviews, flag low confidence.
  5. Close the prior loop. For each OPEN hypothesis whose change shipped and whose "check-after" date passed: compare the target metric now vs its baseline → WIN / REGRESSION / NEUTRAL. WIN → resolve; REGRESSION → open a revert/rethink task; NEUTRAL → keep watching or retire.
  6. Write the backlog. Append a dated, metric-tagged section to ROADMAP.md and update SIGNALS.md (one row per hypothesis; formats in signals-ledger.md). Then output a ranked digest (top 3-5 "what's hurting most, why, the proposed move"), the loop-closure results, and a suggested next command (/apple:next-version, /apple:bugfix for a hot crash, /apple:metadata for an ASO fix).

Read the full file on GitHub · 80 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 7d ago First seen · 80 lines · 105 tokens per session scan A f0332edb1f44

Subscribe to this mod's changes

store-signals is a skill published in the GitHub repository rshankras/claude-code-apple-skills (719 stars, last pushed 1mo ago), licensed MIT. It adds 105 tokens to every session and 1,450 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

Analytics and metricsPlanningagent-skillsai-toolsanthropicapp-storeappleaso
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