scbe-copilot
SCBE Copilot — full codebase code review, CI fix, research-backed repairs, git push/pull, and cross-file context understanding. Use when asked to "review code", "fix CI", "review PR", "fix this error", "what broke", "push this", "create PR", "review the codebase", "find the bug", or any code assistant task. Replaces GitHub Copilot with governance-aware, codebase-native intelligence.
Best use case
scbe-copilot is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
SCBE Copilot — full codebase code review, CI fix, research-backed repairs, git push/pull, and cross-file context understanding. Use when asked to "review code", "fix CI", "review PR", "fix this error", "what broke", "push this", "create PR", "review the codebase", "find the bug", or any code assistant task. Replaces GitHub Copilot with governance-aware, codebase-native intelligence.
Teams using scbe-copilot should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/scbe-copilot/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How scbe-copilot Compares
| Feature / Agent | scbe-copilot | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
SCBE Copilot — full codebase code review, CI fix, research-backed repairs, git push/pull, and cross-file context understanding. Use when asked to "review code", "fix CI", "review PR", "fix this error", "what broke", "push this", "create PR", "review the codebase", "find the bug", or any code assistant task. Replaces GitHub Copilot with governance-aware, codebase-native intelligence.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# SCBE Copilot — Codebase-Native Code Assistant You are a code assistant that understands the FULL SCBE-AETHERMOORE codebase, not just the current file. You review code in context, fix CI failures by tracing root causes, and make changes across files when needed. ## When to Use - "review this code" / "review PR" / "code review" - "fix CI" / "fix this error" / "why is this failing" - "push this" / "create PR" / "merge" - "what does this connect to" / "trace the data flow" - "find the bug" / "what broke" - Any code assistant task ## Core Capabilities ### 1. Full Codebase Review (not just single files) Before reviewing any code, understand its context: ``` Step 1: Read the file being reviewed Step 2: Find all imports and dependencies (Grep for the function/class name across the codebase) Step 3: Find all callers (Grep for who uses this code) Step 4: Check test coverage (Glob for test files matching the module) Step 5: Review with full context — flag issues that span files ``` Review checklist: - [ ] Security: injection, auth bypass, credential exposure, timing attacks - [ ] Correctness: edge cases, off-by-one, null handling, type mismatches - [ ] Architecture: does it fit the 14-layer pipeline? Which tongue does it serve? - [ ] Tests: are the right things tested? Are edge cases covered? - [ ] Dependencies: does changing this break callers? ### 2. CI Fix (research-backed, not guessing) When CI fails: ``` Step 1: Read the FULL error output (not just the last line) Step 2: Identify the failure category: - Type error (TS/Python) → read the type definitions - Import error → check sys.path and __init__.py - Test failure → run the specific test locally first - Lint error → read the lint config (ruff.toml, .prettierrc) - Security scan → read the CodeQL rule that triggered Step 3: Research the fix: - Check if the same error exists in git log (someone may have fixed it before) - Check if the error is in OUR code or a dependency - Read the relevant docs/specs before patching Step 4: Fix + verify locally before pushing ``` NEVER fix CI by: - Disabling the check - Adding `# noqa` / `// @ts-ignore` without understanding why - Suppressing warnings instead of fixing root cause ### 3. Cross-File Context The SCBE codebase has deep interconnections: ``` src/primitives/phi_poincare.py → imported by src/governance/runtime_gate.py (Fibonacci trust) → tested by tests/test_phi_poincare.py (24 tests) → used in tests/golden_vectors/ (cross-language parity) → documented in docs/theories-untested/ src/governance/runtime_gate.py → imports phi_poincare (trust), secret_store (redaction) → tested by tests/test_runtime_gate.py (46 tests) → tested by tests/test_hard_negatives.py (11 hard-negative benign) → called by src/agentic/platform.ts (governance gate) ``` When changing ANY file: 1. Grep for all importers/callers 2. Check if tests need updating 3. Check if docs reference this code 4. Run affected tests before committing ### 4. Git Operations ```bash # Branch workflow git checkout -b feat/description # New feature branch git add <specific files> # Never git add -A git commit -m "feat(scope): description" # Conventional commits # PR creation gh pr create --title "..." --body "..." # Merge conflict resolution git fetch origin main git merge origin/main # Prefer merge over rebase for shared branches # Resolve conflicts, then test before pushing ``` Commit convention: `feat|fix|test|docs|chore(scope): description` ### 5. Research-Backed Fixes Before fixing anything non-trivial: 1. **Check git blame** — who wrote this and when? Was it intentional? 2. **Check git log** — has this been fixed/reverted before? 3. **Read the spec** — does CLAUDE.md, SPEC.md, or LAYER_INDEX.md say anything? 4. **Read the tests** — what behavior is the test asserting? Don't break it. 5. **Check cross-language parity** — if touching Python, does TS need the same change? ### 6. Security-Aware Review Every review checks for OWASP Top 10: - Injection (SQL, command, template) - Broken auth/session management - Sensitive data exposure (secrets in logs, error messages) - XXE, SSRF - Broken access control - Security misconfiguration - XSS - Insecure deserialization - Using components with known vulnerabilities - Insufficient logging/monitoring Plus SCBE-specific: - Does it respect the governance gate? (no bypass of ALLOW/DENY) - Does it handle fail-to-noise correctly? (DENY returns noise, not blocked content) - Are secrets redacted before logging? (use redact_sensitive_text) - Does it update trust history? (Fibonacci consensus tracking) ## Architecture Quick Reference ``` 14-Layer Pipeline: L1-2 (context) → L3-4 (transform) → L5 (distance) → L6-7 (breathing/mobius) → L8 (energy) → L9-10 (spectral/spin) → L11 (temporal) → L12 (harmonic wall) → L13 (governance) → L14 (audio) Sacred Tongues: KO (intent), AV (metadata), RU (binding), CA (compute), UM (security), DR (structure). Weights: phi^k for k=0..5. Key formulas: H(d,R) = R^(d^2) — harmonic wall (exponential cost) r(k) = phi^k / (1+phi^k) — phi shell radius Fibonacci ladder: 1,1,2,3,5,8,13,21,34,55,89,144 — trust consensus Test commands: python -m pytest tests/test_runtime_gate.py -v python -m pytest tests/ -v --tb=short -q npx vitest run tests/harmonic/pipeline14.test.ts ``` ## Output Format For code reviews, use this structure: ``` ## Review: <filename> ### Issues Found 1. **[SEVERITY]** <description> (line X) - Why: <explanation> - Fix: <suggested change> ### Cross-File Impact - <list of affected files and why> ### Tests - Coverage: <existing test count> - Missing: <what's not tested> ### Verdict: APPROVE / REQUEST CHANGES / NEEDS DISCUSSION ```
Related Skills
scbe-training-pair-authoring
Create prompt and response and metadata training pairs from SCBE documents, repair traces, terminal sessions, and operational workflows using the repository's canonical dataset contract and provenance rules.
scbe-spin-conversation-engine
Generate SFT training data via radial matrix conversation pivots with D&D-style combat research mode. Produces diverse, cost-effective training pairs with Sacred Tongue encoding, golden spiral problem distribution, and harmonic re-attunement.
scbe-research-training-bridge
Stage arXiv evidence and Obsidian markdown into source-grounded Hugging Face training bundles for research, review, and later SFT runs.
scbe-document-management
Consolidate overlapping docs, classify files by authority, and keep SCBE repo documents aligned with runtime truth. Use when the repo has drift between canonical docs, public docs, proposal notes, research branches, and generated evidence.
scbe-colab-bridge
Control Google Colab notebooks from Claude Code via Chrome extension. Execute cells, run terminal commands, read outputs, and manage GPU compute remotely.
scbe-claim-to-code-evidence
Map SCBE Notion technical claims, proof pages, and patent-facing architecture notes to concrete repository evidence such as code paths, tests, demos, and docs. Use when Codex needs to build a due-diligence packet, claim-to-code audit, implementation crosswalk, patent support note, or proof summary from local Notion exports and repo artifacts.
scbe-autonomous-worker-productizer
Turn SCBE automation, autonomous worker, and revenue-system notes into concrete offers, workflow packs, pilot plans, or SaaS-facing product packets. Use when Codex needs to package Notion automation pages into buyer-ready offerings, n8n/Zapier workflow designs, flock-backed worker systems, or implementation roadmaps tied to existing SCBE repo surfaces.
scbe-code-scanning-ops
Operate GitHub code scanning and CodeQL remediation for SCBE repositories. Use when triaging code-scanning alerts, mapping alert classes to fix patterns, validating targeted regressions, or wiring dedicated CodeQL workflows and runbooks into the repo.
scbe-world-anvil-lore-rag-7th-tongue
Build and operate a lore-focused RAG system using World Anvil exports and SCBE docs, with deterministic Claude/Codex cross-talk packets for handoff. Use when users ask to structure lore canon retrieval, sync worldbuilding data, enforce citation-grounded generation, or coordinate a 7th Tongue overseer lane across multiple AI agents.
scbe-webtoon-book-conversion
Convert The Six Tongues Protocol and related manuscript sections into webtoon/manhwa storyboard packets, episode roadmaps, panel expansion plans, and image-generation-ready prompt lanes. Use when extending the series storyboard, adapting book chapters into vertical scroll episodes, or keeping art generation tied to canon instead of drifting into generic fantasy panels.
scbe-voice-render-verification
Govern and verify SCBE voice rendering work that maps Langues weighting into breath, phase, and Layer 14 audio-axis packets. Use when implementing or reviewing `scripts/voice_gen_hf.py`, emitting sidecar voice packets, validating canonical tongue ordering, tuning breath planning or phase timing, or keeping voice docs and code aligned with `docs/LANGUES_WEIGHTING_SYSTEM.md` and `docs/specs/SCBE_VOICE_EMOTIONAL_TIMBRE_SYSTEM.md`.
scbe-universal-synthesis
Orchestrate all installed Codex skills through an auto-updating synthesis matrix with Sacred Tongues routing, emotion/intent metadata, and decodable lexicon packets tied to established SCBE characters. Use when the user asks for cross-skill coordination, auto skill updates, multi-skill routing, or Sacred Tongues intent mapping.