repo-status

Repo Status Skill

Version: 1.0 Created: 2026-02-08 Author: Cruz + Manus (Cowork) Origin: Codified from a live repo-context-sync + strategic-scout + status-writing session on Dojo Genesis. Lineage: status-writing + file-management + health-audit, distilled into a single repeatable workflow.

---

I. The Philosophy: See the Whole Before Touching a Part

Most codebases are explored piecemeal — someone reads a file, then another, then another, building a fragmentary picture that drifts as the project evolves. This skill takes the opposite approach: see the whole system first, as a living organism, then record what you see in a structured artifact that any future agent or human can use as a starting point.

The output is a .status.md file — a comprehensive, versioned snapshot that bridges three perspectives:

• Status Writer: Emoji-driven health indicators, workstream tracking, blockers, next steps. The "heartbeat" view.
• File Management: Annotated directory tree with per-folder status. The "anatomy" view.
• Health Supervisor: Critical/security/sustainability assessment. The "checkup" view.

A single document. One place to look. Updated over time like a living record.

---

II. When to Use This Skill

• Onboarding to a new project: Before writing a single line, run this skill to build your mental model.
• Periodic health check: Monthly or per-release, re-run to track drift and detect decay.
• Before a major refactor: Understand what exists before deciding what to change.
• When handing off context: Give another agent or team member a single file that tells them everything.
• When a project feels "big" or "messy": Structure reduces anxiety. This skill imposes structure on chaos.

---

III. The Workflow

This is a 5-phase process. Each phase builds on the previous one.

Phase 1: Grounding (Read Before You Judge)

Objective: Understand what the project is before evaluating how well it works.

1. List the root directory. Get the top-level shape.
2. Read core documents. In priority order:
   • README.md (what it is, how to run it)
   • STATUS.md or equivalent (where it is right now)
   • CHANGELOG.md or release notes (where it's been)
   • package.json, go.mod, Cargo.toml, pyproject.toml (tech stack, deps)
   • ARCHITECTURE.md, PHILOSOPHY.md if they exist (intent, design)
3. Identify the tech stack. Language(s), framework(s), database(s), infra.
4. Note the project's self-stated purpose. This becomes Section 1 of the output.

Time budget: ~10% of total effort. Resist the urge to explore deeply here.

Phase 2: Deep Inventory (Parallel Exploration)

Objective: Build a complete picture of everything in the repository.

Launch parallel exploration across the major layers. For most projects, these are:

Layer	What to Inventory
Frontend	Components, routes, state management, styling, tests, LOC estimates
Backend	Packages, handlers, services, models, LOC estimates
Data	Migrations, schemas, seed data, vector stores
Knowledge	Docs, skills, prompts, thinking artifacts, compressions
Infra	CI/CD, Docker, scripts, deployment configs
Tests	Unit, integration, e2e, coverage estimates

For each layer, collect:

• File count
• Approximate LOC (use wc -l or cloc if available)
• Package/module count
• Status (active, legacy, deprecated, empty)

Time budget: ~30% of total effort. Parallelize aggressively.

Phase 3: Semantic Clustering

Objective: Group everything by what the system does, not where files happen to live.

This is the distinctive step. Instead of just listing directories, map every feature to an action verb — a cluster that describes a behavioral capability of the system.

Read references/semantic-clusters.md for the full cluster framework and examples.

The process:

1. Review the inventory from Phase 2.
2. For each significant component, ask: "What verb describes what this does?"
3. Assign it to a cluster. Create new clusters if needed.
4. For each cluster, build a component table: Component | Location | Status | LOC
5. Write a health assessment per cluster.
6. Identify cross-cluster components (things that serve multiple verbs).
7. Identify orphans (components that don't fit any cluster) — these may signal architectural confusion.

Time budget: ~25% of total effort. This is where the insight lives.

Phase 4: Write the .status.md

Objective: Assemble everything into a single, structured document.

Read references/status-template.md for the complete output template.

The document has 10 sections:

1. Vision & Purpose — One sentence + core principles (from Phase 1)
2. Current State — Emoji table of major areas (from Phase 2)
3. Directory Structure — Annotated tree with per-folder status (from Phase 2)
4. Semantic Clusters — One subsection per verb, with component tables (from Phase 3)
5. File Importance Ranking — 4 tiers (Critical, Important, Supporting, Knowledge)
6. Health Assessment — Critical issues, security, sustainability (health-audit framework)
7. Active Workstreams — What's being worked on right now
8. Blockers & Dependencies — What's preventing progress
9. Next Steps — Immediate actionable items
10. Aggregate Statistics — Summary numbers table

Save to .status.md at the project root (dot-prefixed to stay out of the way but discoverable).

Time budget: ~25% of total effort.

Phase 5: Verification

Objective: Catch errors, fill gaps, and harden the document.

1. Cross-check statistics programmatically. Run find and wc -l to verify LOC claims. Check file counts against actual filesystem.
2. Identify unmapped components. Walk the component directories and confirm every significant directory appears in at least one semantic cluster.
3. Validate status emojis. Ensure every area marked "complete" actually has evidence of completeness.
4. Apply corrections. Update the .status.md with any discrepancies found.
5. Note confidence level. If you couldn't verify something, say so.

Time budget: ~10% of total effort. This step prevents the document from being fiction.

---

IV. The Semantic Cluster Framework

The clusters are action verbs that describe what a system does. For the full reference with starter verbs and mapping guidance, read:

references/semantic-clusters.md

Starter set (adapt to your project):

Verb	Meaning	Example Components
CONVERSE	Chat, messaging, streaming	Chat UI, SSE handlers, message models
REASON	Thinking, planning, deciding	Agent, intent classifier, orchestration
REMEMBER	Storage, recall, learning	Memory system, embeddings, seeds
OBSERVE	Monitoring, tracing, metrics	Trace logger, event bus, cost tracker
LEARN	Adaptation, feedback, calibration	Calibration engine, preference system
ACT	Tool execution, side effects	Tool registry, tool implementations
PROTECT	Auth, security, boundaries	Middleware, secure storage, auth context
CONNECT	External integrations	Plugins, APIs, bots, webhooks
PRESENT	UI, rendering, layout	Shell, sidebars, panels, components
PERSIST	Database, migrations, storage	DB manager, migrations, schemas
BUILD	CI/CD, testing, deployment	Workflows, Docker, scripts, tests
THINK	Meta-cognition, knowledge	Skills, prompts, documentation
ORCHESTRATE	Multi-step coordination	DAG engine, task decomposition

Not every project will use all 13. Some projects may need verbs not on this list. The framework is a starting point, not a constraint.

---

V. File Importance Ranking

Rank files into 4 tiers based on runtime criticality and development importance:

Tier	Criteria	Typical Count
Tier 1: Critical	System won't function without these. Core agent, main state, primary API endpoint, database manager.	10 files
Tier 2: Important	Core features break without these. Supporting services, secondary state, routing.	10 files
Tier 3: Supporting	System degrades gracefully without these. Integrations, utilities, background tasks.	20-30 files
Tier 4: Knowledge	Essential for development, not runtime. Docs, specs, skills, prompts.	Variable

For each file in Tiers 1-2, include Rank | File | Why — a one-line justification.

---

VI. Health Assessment Framework

Borrowed directly from the health-audit skill. For each category, use a table with Area | Status | Notes.

Category	What to Check
Critical Issues	Can it build? Critical dependency vulnerabilities? Main branch broken?
Security	Secrets hardcoded? Auth implemented? RLS policies set? Encryption at rest?
Sustainability	Test coverage adequate? CI/CD automated? Technical debt managed? Docs current? Manual processes documented?

Be honest. Use emoji status indicators: ✅ good, ⚠️ concern, ❌ blocked.

---

VII. Best Practices

• Dot-prefix the output file. .status.md stays discoverable but doesn't clutter the root alongside README and STATUS.
• Separate from STATUS.md. The lightweight STATUS.md (per status-writing skill) is the human-facing dashboard. .status.md is the comprehensive agent-facing audit. They complement each other.
• Update incrementally. After the first generation, subsequent runs should diff against the existing .status.md and update rather than regenerate from scratch.
• Statistics drift is normal. LOC counts change every commit. The goal is "accurate enough to be useful" — within 10% is fine.
• Semantic clusters are the crown jewel. The directory tree tells you where things are. The clusters tell you what things do. Prioritize cluster quality over tree completeness.
• Parallelize Phase 2. The deep inventory is the slowest phase. Use subagents or parallel exploration to cut time.
• Verification is not optional. A beautiful document full of wrong numbers is worse than no document. Always verify.

---

VIII. Quality Checklist

Before delivering the .status.md, confirm:

• [ ] Vision statement is present and accurate
• [ ] Every major directory appears in the annotated tree
• [ ] Every significant component maps to at least one semantic cluster
• [ ] No orphan directories (unmapped components) remain unexplained
• [ ] File importance ranking has at least 10 Tier 1-2 files
• [ ] Health assessment covers critical, security, and sustainability
• [ ] Aggregate statistics are programmatically verified (within 10%)
• [ ] Active workstreams reflect actual current work
• [ ] Next steps are concrete and actionable
• [ ] The document reads coherently from top to bottom

Output

• .status.md saved at the project root (dot-prefixed, stays discoverable without cluttering the root alongside README).
• Ten sections: Vision and Purpose, Current State (emoji table), Directory Structure (annotated tree), Semantic Clusters (per-verb component tables), File Importance Ranking (4 tiers), Health Assessment, Active Workstreams, Blockers and Dependencies, Next Steps, Aggregate Statistics.

Examples

Scenario 1: "Give me a complete overview of the Gateway repo before the sprint" → .status.md generated with all 10 sections. Semantic clusters reveal REASON and ORCHESTRATE are the most complex clusters. Health assessment surfaces a missing CI/CD config as a sustainability concern. Scenario 2: "Prepare a handoff doc for the HTMLCraft Studio" → .status.md produced for a Wails v2 + Go project. 8-module architecture mapped into 5 clusters. Tier 1 files identified. Handed off to incoming agent who begins from the status doc rather than a 30-minute walkthrough.

Edge Cases

• If the project has fewer than 50 files, skip semantic clusters (Section 4) and file importance ranking (Section 5) — they add little value at small scale.
• On subsequent runs, diff against the existing .status.md and update incrementally rather than regenerating from scratch — especially critical for aggregate statistics that change with every commit.

Anti-Patterns

• Conflating this skill with health-audit — repo-status produces status documents; health-audit produces engineering commissions. The outputs are complementary but distinct.
• Verifying statistics by estimation rather than running find and wc -l — a beautiful document full of wrong numbers is worse than no document.