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 agentmods add skills/codejunkie99/agentic-stack/data-layernpx skills add codejunkie99/agentic-stack --skill data-layergit clone --depth 1 https://github.com/codejunkie99/agentic-stackWhat 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 | $0.00002 | $0.01099 |
| Opus 5 | $0.00001 | $0.00549 |
| Sonnet 5 | $0.00000 | $0.00220 |
| Haiku 4.5 | $0.00000 | $0.00110 |
Grade A, and why
data-layer 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 2d 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Layer - cross-harness monitoring for the portable brain
Use this skill when the user wants to measure agent activity across Claude Code,
Hermes, OpenClaw, Codex, Cursor, OpenCode, Windsurf, Pi, Antigravity, or any
custom loop using .agent/.
The goal is local business intelligence for the whole agent suite:
- what harnesses are active
- how many agent events are happening
- when cron/scheduled agents fire
- which crons started/finished and how long they ran
- how many agents are active
- tokens and estimated cost by hour/day/week/month
- resource usage by user-defined category
- workflow success/error rates
- KPI summary rows for cron cadence, run volume, reliability, active agents, workflow breadth, token usage, and estimated cost
- terminal dashboard visible directly in the user's coding tool
- screenshot-ready daily resource reports
Hard Rules
- Stay local-first. Do not add telemetry or remote sync.
- Do not store raw prompts, raw code, client names, emails, phone numbers, or unredacted business records in shared examples.
- Do not commit
.agent/data-layer/exports unless the user explicitly reviewed and sanitized them. - Do not send dashboard screenshots to email, Slack, webhooks, or any other channel unless the user explicitly approves the destination.
Inputs
Default inputs:
.agent/memory/episodic/AGENT_LEARNINGS.jsonl
.agent/data-layer/harness-events.jsonl optional
.agent/data-layer/cron-runs.jsonl optional
.agent/data-layer/category-rules.json optional
AGENT_LEARNINGS.jsonl is the shared activity log. Optional files let users add
events from harnesses that do not automatically write rich events yet.
Agent behavior
When this skill is injected, decide whether the user is asking to see local agent activity. Natural prompts such as "what did my agents do", "show me the dashboard", "how many tokens did we use", or "show last week by hour" should render the terminal dashboard directly. Do not make users remember flags.
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.
- 2d ago First seen · 142 lines · 2 tokens per session scan A 7d5f77659ba6
data-layer is a skill published in the GitHub repository codejunkie99/agentic-stack (2,241 stars, last pushed 26d ago), licensed Apache-2.0. It adds 2 tokens to every session and 1,099 once invoked, about $0.0000 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…