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 Fozikio/cortex-engine --skill learn-projectgit clone --depth 1 https://github.com/Fozikio/cortex-engineWrote 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/fozikio/cortex-engine/learn-project)<a href="https://agentmods.dev/skills/fozikio/cortex-engine/learn-project"><img src="https://agentmods.dev/badge/skills/fozikio/cortex-engine/learn-project/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/fozikio/cortex-engine/learn-project"><img src="https://agentmods.dev/badge/skills/fozikio/cortex-engine/learn-project.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00021 | $0.00527 |
| Opus 5.5 | $0.00008 | $0.00211 |
| Sonnet 5.5 | $0.00004 | $0.00105 |
| Haiku 4.5 | $0.00002 | $0.00053 |
Grade A, and why
learn-project 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.
What it actually says
Learn Project
Quickly populate your agent's project memory by reading the codebase.
When to use
Run this when cortex is new and you want the agent to understand your project without waiting for it to learn organically over several sessions.
What it does
Read these files (skip any that don't exist):
- package.json — framework, key dependencies, scripts, project name
- README.md — stated purpose, setup instructions
- tsconfig.json / jsconfig.json — language config, strict mode
- Directory structure (1 level deep) — architecture shape
- Config files — .eslintrc, prettier, vite/next/webpack config
- Last 10 git commits — current momentum, commit style
- CI config — .github/workflows, Dockerfile, deploy scripts
How to observe
For each file read, extract 1-3 key facts. Observe into the project namespace:
observe("Next.js 14 app with App Router, TypeScript strict mode", namespace: "project")
observe("Tests in __tests__/ using vitest, coverage threshold 80%", namespace: "project")
observe("Monorepo: packages/api (Hono) and packages/web (Next.js), shared types in packages/shared", namespace: "project")
Rules
- 8-15 observations max. This is a first impression, not a full audit.
- Architectural facts only. "Uses React" matters. "Has 847 files" doesn't.
- Skip dependency lists. Don't observe every package.json dependency.
- Skip generated files. Lock files, dist/, node_modules/ tell you nothing useful.
- Ask: would my future self search for this? If not, don't store it.
After scanning
Show the user what you learned in a brief summary:
"Got it — Next.js 14 app with Drizzle ORM, deployed to Vercel, monorepo with packages/api and packages/web. Tests use vitest. I'll pick up patterns as we work together."
If anything is wrong, the user corrects it. Corrections are high-signal — observe those too.
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 · 58 lines · 21 tokens per session scan A ab88d4400ba6
learn-project is a skill published in the GitHub repository Fozikio/cortex-engine (50 stars, last pushed 2d ago), licensed MIT. It adds 21 tokens to every session and 527 once invoked, about $0.0001 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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