/architect

name: architect
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.
version: "1.0"
generated_from: "arscontexta-v1.6"
user-invocable: true
context: fork
model: opus
allowed-tools: Read, Write, Edit, Grep, Glob, Bash, mcp__qmd__search, mcp__qmd__vector_search, mcp__qmd__deep_search, mcp__qmd__get, mcp__qmd__multi_get
argument-hint: "[optional: specific area to focus on, e.g. 'schema', 'processing', 'MOC structure']"

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.

---

Philosophy

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

Every rule in the context file was a hypothesis. Every skill workflow was a design choice. Hypotheses need testing against operational reality. This skill connects three evidence streams — health data, friction patterns, and research claims — to produce specific, actionable recommendations.

You are not guessing what might help. You are diagnosing what IS happening (health + friction) and prescribing what research says SHOULD happen. Every recommendation traces to specific evidence. "I think this would be better" is not a recommendation. "Health shows 12 orphans, friction log shows repeated orphan complaints, research claim [[orphan notes decay faster than connected ones]] supports adding a condition-triggered reweave pass" — THAT is a recommendation.

**The 25% meta-work budget:** In a 60-minute session, at most 15 minutes should be spent on system evolution. If a recommendation estimates >15 minutes to implement, the recommendation should be "defer to next session." The system serves the work, not the other way around.

**INVARIANT: Architect NEVER auto-implements.** Every recommendation requires explicit user approval before any files are modified. This prevents the cognitive outsourcing failure mode — the human must remain in the judgment loop for system evolution.

---

PHASE 1: Locate

Automated. No user interaction needed.

Detect the platform and find the system's key files:

Check filesystem:
  .claude/ directory exists         -> platform = "claude-code"
  Neither                           -> platform = "minimal"

Locate these files (paths vary by domain vocabulary):

| File Type | What To Find | Typical Locations | |-----------|-------------|-------------------| | Context file | System methodology and rules | CLAUDE.md, README.md | | Self space | Agent identity and memory | self/identity.md, self/methodology.md, self/goals.md | | Ops directory | Operational infrastructure | ops/derivation.md, ops/config.yaml, ops/sessions/, ops/observations/, ops/health/ | | Notes directory | Primary knowledge directory | {vocabulary.notes}/ (may be domain-named: reflections/, concepts/, etc.) | | Queue system | Pipeline state | ops/queue/queue.yaml or ops/queue/queue.json | | Templates | Note schemas | ops/templates/ or templates/ | | Methodology | Learned patterns | ops/methodology/ |

**If `ops/derivation.md` does not exist:** Warn the user: "No derivation record found. Recommendations will be based on current state analysis only, without historical context of design decisions."

**If `ops/config.yaml` does not exist:** Warn: "No config file found. Using observed behavior to infer current configuration."

Record all file locations for use in subsequent phases.

---

PHASE 2: Read Derivation

Read `ops/derivation.md` to understand the system's original design intent. This is the baseline against which drift is measured.

**Extract from derivation:**

| Element | What To Look For | Why It Matters | |---------|-----------------|----------------| | Dimension positions | The 8 configuration dimensions and their derived values | Baseline for drift detection | | Conversation signals | What the user said that drove each choice | Understanding original intent | | Personality | Warmth, formality, opinionatedness, emotional awareness | Voice consistency check | | Vocabulary mapping | Universal-to-domain term translations | Output must use domain language | | Coherence validation | What constraints were active at derivation time | Know which constraints to re-check | | Failure mo