# Architect

Research-backed evolution advice for your knowledge system.

- Repository: https://github.com/agenticnotetaking/arscontexta (3518 stars)
- Category: Writing
- License: MIT
- Last updated: 2026-02-24
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add agenticnotetaking/arscontexta --skill architect -g -a claude-code -y`
Codex: `npx skills add agenticnotetaking/arscontexta --skill architect -g -a codex -y`
Cursor: `npx skills add agenticnotetaking/arscontexta --skill architect -g -a cursor -y`
Gemini CLI: `npx skills add agenticnotetaking/arscontexta --skill architect -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add agenticnotetaking/arscontexta`

## Description

Research-backed evolution advice for your knowledge system. Analyzes health reports, friction patterns, and derivation history to propose specific changes with research justification. Never auto-implements, proposals require your approval.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

## Runtime Configuration (Step 0, before any processing)

Read these files to configure domain-specific behavior:

1. **`ops/derivation-manifest.md`**, vocabulary mapping, platform hints
   - Use `vocabulary.notes` for the notes folder name
   - Use `vocabulary.note` / `vocabulary.note_plural` for note type references
   - Use `vocabulary.topic_map` / `vocabulary.topic_map_plural` for MOC references
   - Use `vocabulary.inbox` for the inbox folder name
   - Use `vocabulary.cmd_reflect` for connection-finding command name
   - Use `vocabulary.cmd_reweave` for backward-pass command name
   - Use `vocabulary.cmd_verify` for verification command name
   - Use `vocabulary.architect` for the command name in output

2. **`ops/config.yaml`**, processing depth, pipeline chaining, automation settings

3. **`ops/derivation.md`**, original derivation record (the design intent baseline)

If these files don't exist, use universal defaults and warn the user.

---

## EXECUTE NOW

**Target: $ARGUMENTS**

Parse immediately:
- If target names a specific area (e.g., "schema", "processing", "MOC structure"): focus analysis on that area
- If target is empty: run full-system analysis across all dimensions
- If target is `--dry-run`: run analysis but do not offer implementation

**Execute these phases sequentially:**

1. Locate system files and detect platform
2. Read derivation record for design intent
3. Analyze health data (recent report or live check)
4. Scan for friction patterns across operational surfaces
5. Consult research to ground evidence in specific claims
6. Generate 3-5 ranked recommendations with full evidence chains
7. Present to user and implement on approval

**START NOW.** Reference below defines the seven-phase workflow.

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## Philosophy

**Evidence beats intuition. Research beats habit.**

The rest: https://github.com/agenticnotetaking/arscontexta/blob/HEAD/skills/architect/SKILL.md