aizen-tech-learning
Research a technology in depth for one concrete workload - trace one operation from the application layer down through runtime, OS/kernel, network and…
Skill by tankhangkm12
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/tankhangkm12-aizen-tech-learning
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: documents, frontend, backend, devops
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
- Runs as
- Python package
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/aizen-tech-learning
What's inside
- Skill name
aizen-tech-learning- Description
- Research a technology in depth for one concrete workload - trace one operation from the application layer down through runtime, OS/kernel, network and hardware to explain the algorithm and mechanism that make it efficient there, draw Mermaid architecture diagrams linked by component IDs, compare alternatives architecture-to-architecture, then write a checked markdown note and publish it as a tree page in Notion. Use when the user wants to learn, research, deep-dive or evaluate a technology, framework, database or tool, or asks why X is fast or how X compares to Y (nghiên cứu công nghệ, học sâu, tìm hiểu bản chất, vì sao X nhanh, so sánh kiến trúc X và Y, ghi vào Notion). Not for: designing a schema, writing or reviewing code, or building CI/CD (all aizen-build), or turning a video into a skill (aizen-video-to-skill).
- License
- MIT
- Instructions
- 80 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Agent Skills spec issues
- files: referenced but missing: references/core/mcp.md, references/core/verifier.md
Files (10)
- SKILL.md
- evals.json · evals/
- manifest.json
- compare-method.md · references/
- diagram-guide.md · references/
- layer-guide.md · references/
- tree-template.md · references/
- research.md · rules/
- check_tree.py · scripts/
- to_notion.py · scripts/
SKILL.md (first lines)
# aizen-tech-learning — understand why a technology wins, layer by layer (v4)
You explain **the mechanism**, not the brochure: follow one real operation of the user's workload down the stack,
show where the time and copies go, and compare alternatives on the same path. The note is a tree: thesis → a
component map (IDs `C1…Cn`) → one branch per layer → core algorithm → comparison, all linked by those IDs.
**Read first:** `rules/research.md`, `references/core/mcp.md` (aizen-core). Use context7 for library docs when available.
## Run contract (enforced by the guard)
`<CORE_DIR>` = the aizen-core folder next to this skill. Step 0 starts the run: `uv run "<CORE_DIR>/scripts/core/guard.py" start --skill aizen-tech-learning --goal "<technology> for <workload>" --output <note path>`; after the intake answers, `uv run "<CORE_DIR>/scripts/core/guard.py" go --run <RUN> --text "<the owner's answers>"`. Writing the note before `go` is refused.
- Fill `.aizen/runs/<RUN>/sheet.md` as you go: tick a step only with evidence the guard can check —
`file:<path>` · `cmd:<the command you ran>` · `out:"<a line it printed>"` · `sha:<commit>` · `url:<source cited in the output>`.
- The Stop hook runs the contract (`manifest.json` → `contract`): open items → you continue with the exact list.
`uv run "<CORE_DIR>/scripts/core/guard.py" check --run <RUN>` shows it any time. A step that truly does not apply:
`uv run "<CORE_DIR>/scripts/core/guard.py" waive --run <RUN> --step <id> --reason "…" --evidence <file | real output>`.
- When everything deterministic passes, dispatch a **fresh** verifier (another model if you can) with
`references/core/verifier.md` + the output of `uv run "<CORE_DIR>/scripts/core/guard.py" verify-brief --run <RUN>`. It writes `verdict.json`
itself; ≥ 80% of the expectations, each pass citing `path:line`, or you fix and dispatch it again.
- Never write `run.json`, the ledger, waivers or evidence yourself, and never say "done" — the guard marks the run
done and archives it.
## Workflow
0. **Intake (one round, then stop and wait).** Ask only what is missing, each with your proposal:
- technology and version (propose the latest stable);
- **the reference workload** — one sentence the whole note hangs on, e.g. "cache, 100k GET/s, 1 KB values,
1 node" (propose the most common one for this technology);
- alternatives to compare (propose 2–3 that solve the same workload differently);
- Notion parent page, and the markdown path (default `./tech-tree/<technology>.md`).
1. **Sources.** Collect the official docs, the source repo at the version's tag, the design paper or talk.
Locate in the source the files that implement the hot path of the operation — you will link them.
2. **Thesis** (3 lines): the problem before it, the one or two design choices that win this workload (C-IDs),
the price paid.
3. **Component map.** Table of `C1…Cn` (component · data structure/state · thread/process) and a mermaid
`flowchart` whose edges say what flows. Every later section refers to these IDs — that is what links the
architecture together. `references/diagram-guide.md`.
4. **Descend the layers** L1 application/API → L2 runtime/engine → L3 OS/kernel → L4 network → L5 hardware, for
the one operation. Per layer: a `sequenceDiagram`, **Why it is efficient here** (the mechanism and its
counted cost: copies, syscalls, round-trips, O(…), locks), **Observe** (a command that shows it, e.g.
`strace -c`, `perf`, `ss -ti`) and a source link. A layer that does not matter gets one line
`Not relevant: <reason>` — never skip silently. What to look for: `references/layer-guide.md`.
5. **Core algorithm.** The key structure or protocol (event loop, LSM-tree, B+tree, Raft, …): steps,
invariants, complexity, and the **breaking point** — the workload that makes it slow and why.
6. **Compare by architecture.** Each alternative runs the same operation through the same layers, with its own
diagram; a table per layer with mechanism + cost per cell; the verdict as "choose X when <condition>".
`references/compare-method.md`.
7. **In practice (short).** Quick start, the production settings that tune the mechanisms above (say which
C-ID/layer each one affects), SDKs; AI agent / MCP integration only when it exists.
8. **Write and check.** Fill `references/tree-template.md` in the user's language into the markdown file, then
`uv run "<SKILL_DIR>/scripts/check_tree.py" <file>` (add `--min-alternatives 1` when the user named only one
alternative). Fix until PASS.
9. **Publish to Notion.** `uv run "<SKILL_DIR>/scripts/to_notion.py" <file>` → `<file>.notion.md` + the title
(the `#` heading becomes the page title; tables become Notion `<table>`; URLs become links; markup
characters are escaped; mermaid stays a `mermaid` code block, which Notion renders). Then `notion-search`
for that title under the parent: found → `notion-update-page`, else `notion-create-pages` with the
converted file as `content`, parent = the page the user named. Fetch the page once to confirm tables and
diagrams rendered. Notion missing or failing → keep the markdown file and tell the user once.
10. **Report:** the file path and Notion link, the thesis in 3 lines, layers marked not relevant, and every
claim left as `not found`, `[projected]` or `[unverified]`.
## Quality bar
- Depth means a mechanism with a cost, linked to source code — not more adjectives or more sections.
- One operation, followed all the way down, beats a survey of every feature.
- If the reader cannot say after reading why it is fast **in this workload** and when it stops being fast, the
note is not done.
## Knowledge
| Need | Read |
|---|---|
| what to look for per layer, common mechanisms, observe commands | `references/layer-guide.md` |
| comparison method and table | `references/compare-method.md` |
| mermaid conventions that render in Notion | `references/diagram-guide.md` |
| note skeleton (the checker keys on its headings) | `references/tree-template.md` |
| source and publishing rules | `rules/research.md` |From tankhangkm12/Aizen-Skills, MIT.
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Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Oct 6, 2026 (23 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/tankhangkm12/Aizen-Skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
aizen-tech-learning skill. Agent Mart. https://agentmart.dev/skills/tankhangkm12-aizen-tech-learning (last updated 2026-10-24)