agentic-stack is a portable layer of agent memory, skills, and protocols stored in a .agent/ folder that can be used across multiple coding-agent tools. It is for people who switch between agent harnesses while keeping shared knowledge and monitoring data. The catalogue entries provide skills and rules for this shared agent layer.
Borrowing it
Nothing to install: this file belongs to codejunkie99/agentic-stack. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/codejunkie99/agentic-stack/master/.agent/skills/data-layer/SKILL.mdgit clone --depth 1 https://github.com/codejunkie99/agentic-stackWrote 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/codejunkie99/agentic-stack/data-layer)<a href="https://agentmods.dev/skills/codejunkie99/agentic-stack/data-layer"><img src="https://agentmods.dev/badge/skills/codejunkie99/agentic-stack/data-layer/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/codejunkie99/agentic-stack/data-layer"><img src="https://agentmods.dev/badge/skills/codejunkie99/agentic-stack/data-layer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Rogue Agent · line 138 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
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 | $0.00002 | $0.01099 |
| Opus 5.5 | $0.00001 | $0.00440 |
| Sonnet 5.5 | $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 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 — 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.
- 1mo 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 (2,302 stars, last pushed 9d ago), licensed Apache-2.0. It adds 2 tokens to every session and 1,099 once invoked, about $0.0000 per session on Opus 5.5. 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.
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