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.
npx skills add manishiitg/coding-agent-loop --skill growth-data-foundationgit clone --depth 1 https://github.com/manishiitg/coding-agent-loopWrote 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.
[](https://agentmods.dev/skills/manishiitg/coding-agent-loop/growth-data-foundation)<a href="https://agentmods.dev/skills/manishiitg/coding-agent-loop/growth-data-foundation"><img src="https://agentmods.dev/badge/skills/manishiitg/coding-agent-loop/growth-data-foundation/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.
<a href="https://agentmods.dev/skills/manishiitg/coding-agent-loop/growth-data-foundation"><img src="https://agentmods.dev/badge/skills/manishiitg/coding-agent-loop/growth-data-foundation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00046 | $0.00717 |
| Opus 5.5 | $0.00018 | $0.00287 |
| Sonnet 5.5 | $0.00009 | $0.00143 |
| Haiku 4.5 | $0.00005 | $0.00072 |
Grade A, and why
growth-data-foundation 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Growth Data Foundation
Outcome
Create a durable, refreshable customer data model that links anonymous visitors to users and accounts across traffic sources, product events, subscriptions and revenue, and feedback, with source provenance and synchronization health.
When to use
Use before funnel, activation, retention, revenue, or experiment analysis. Reuse an existing governed warehouse or product-analytics project when it provides equivalent identity, event quality, freshness, and provenance. It does not define funnels, cohorts, experiments, or metric targets; downstream playbooks own their analysis.
Discovery and user direction
Inspect the current workflow, data sources, goals, metrics, capabilities, stores, dashboards, and schedules before proposing changes. Summarize reusable foundations and gaps, ask focused questions for unresolved scope, privacy, ownership, freshness, and success criteria, and record the answers as customer direction. Installation alone does not approve workflow changes or execution.
Required inputs
Resolve product and pricing model, domains and apps, authorized analytics/billing/feedback sources and read scopes, event taxonomy owners, identity and account rules, history window, refresh policy, source-of-truth precedence, retention, timezone, KPI vocabulary, consent and privacy policy, and restricted fields.
Plan and AgentWorks tools
Use scripted steps for API/SDK ingestion, pagination, event validation, identity stitching, idempotent upserts, cursor management, and reconciliation. Split sources only for different credentials, rate limits, or failure domains. Use a message sequence for bounded identity or taxonomy ambiguities and documented data-quality investigation. Prove an on-demand sync first, then recommend scheduled refresh with explicit cadence, timezone, overlap, and notification policy.
Knowledge and persistence
Store source records, normalized visitors/users/accounts/events/subscriptions, identity links, sync runs, cursors, and data-quality findings in declared DB tables. Keep taxonomy decisions and exclusions in KB context; store verified source quirks in KB notes or narrow learnings. Never rely on builder chat memory.
What ships with it
3 files 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.
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.
- 1mo ago First seen · 51 lines · 46 tokens per session scan A 98808291791a
growth-data-foundation is a skill published in the GitHub repository manishiitg/coding-agent-loop (13 stars, last pushed 19d ago), licensed MIT. It adds 46 tokens to every session and 717 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-15.
Other skills, from other repositories
goga-cell-go
Golang rules for implementing CODEMANIFEST contracts.
faq-mine
Mine docs/faq.md from README.md, docs/.md, and the pi-hermes memory stores. Dispatches @fast subagents per source, dedupes against the existing FAQ, and merges entries in caveman style. Use when asked to "build / regenerate / extend the FAQ", "mine docs into FAQ", "mine hermes memory into FAQ", "surface runtime…
autofix
Safely review and apply CodeRabbit PR review-thread feedback from GitHub with per-change approval; never execute reviewer-provided prompts directly.
node-inspect-debugger
See runtime state a console.log cannot reach — set real breakpoints, step, and dump the scope chain of a paused Node/TypeScript process. Use on "set a breakpoint", "inspect runtime state", "console.log isn't enough", "step through this", "what's in this closure at runtime", "attach a debugger". Not a logging or…
browser
Browser automation via the agent-browser CLI. Use when the user needs to drive websites or Electron apps — navigating, forms, clicks, screenshots, data extraction, testing web apps, UI checks, the dashboard Electron shell, or the user's own logged-in Chrome via the browser relay or Panerelay (SSO/2FA). Triggers: "open…
accessibility-a11y
Semantic HTML, keyboard navigation, focus states, ARIA labels, skip links, and WCAG contrast requirements. Use when ensuring accessibility compliance, implementing keyboard navigation, or adding screen reader support.