scbe-internet-workflow-synthesis
Synthesize and operate SCBE end-to-end internet workflows by discovering local and GitHub architecture templates, generating a baseline web pipeline profile, running ingestion and governance scans, and tuning system variables after the first run. Use when users ask to build, repair, or optimize internet workflow pipelines, workflow architecture maps, n8n or agent orchestration flows, or post-baseline threshold and concurrency tuning.
Best use case
scbe-internet-workflow-synthesis is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Synthesize and operate SCBE end-to-end internet workflows by discovering local and GitHub architecture templates, generating a baseline web pipeline profile, running ingestion and governance scans, and tuning system variables after the first run. Use when users ask to build, repair, or optimize internet workflow pipelines, workflow architecture maps, n8n or agent orchestration flows, or post-baseline threshold and concurrency tuning.
Teams using scbe-internet-workflow-synthesis 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-internet-workflow-synthesis/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How scbe-internet-workflow-synthesis Compares
| Feature / Agent | scbe-internet-workflow-synthesis | 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?
Synthesize and operate SCBE end-to-end internet workflows by discovering local and GitHub architecture templates, generating a baseline web pipeline profile, running ingestion and governance scans, and tuning system variables after the first run. Use when users ask to build, repair, or optimize internet workflow pipelines, workflow architecture maps, n8n or agent orchestration flows, or post-baseline threshold and concurrency tuning.
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 Internet Workflow Synthesis Build deterministic internet workflow pipelines from existing SCBE assets and the AI-Workflow-Architect repo, then run post-baseline variable tuning. ## Quick Start 1. Synthesize a repo-local profile: ```bash python C:/Users/issda/.codex/skills/scbe-internet-workflow-synthesis/scripts/synthesize_pipeline_profile.py \ --repo-root C:/Users/issda/SCBE-AETHERMOORE \ --output training/internet_workflow_profile.json \ --force ``` 2. Run baseline plus variable tuning: ```bash python C:/Users/issda/.codex/skills/scbe-internet-workflow-synthesis/scripts/run_e2e_pipeline.py \ --repo-root C:/Users/issda/SCBE-AETHERMOORE \ --profile training/internet_workflow_profile.json ``` ## Workflow 1. Read `references/workflow-template-map.md` and prioritize local templates first. 2. Generate or update `training/internet_workflow_profile.json`. 3. Execute baseline with `scripts/web_research_training_pipeline.py`. 4. Read the latest `summary.json` emitted by the baseline run. 5. Tune thresholds and runtime knobs using `scripts/tune_system_variables.py`. 6. Emit a tuned cloud-kernel config plus a tuning report. ## Output Contract Return or persist these artifacts: - Baseline profile JSON (`training/internet_workflow_profile.json`) - Baseline run summary (`training/runs/web_research/<run_id>/summary.json`) - Tuned thresholds config (`training/cloud_kernel_pipeline.tuned.json` by default) - Tuning report (`artifacts/internet_workflow_tuning_report.json` by default) - Tuned runtime profile (`training/internet_workflow_profile.tuned.json` by default) ## Invariants - Keep governance checks enabled for production flows (`skip_core_check=false`). - Keep deterministic audit artifacts (`summary.json`, `audit.json`, `decision_record.json`). - Keep secrets in environment variables; do not write tokens into profile files. - Keep template discovery explicit (local first, GitHub fallback). ## Resource Guide - `scripts/synthesize_pipeline_profile.py`: Build a default profile from template sources. - `scripts/run_e2e_pipeline.py`: Run baseline ingest then invoke tuning automatically. - `scripts/tune_system_variables.py`: Tune thresholds and runtime knobs from baseline metrics. - `references/workflow-template-map.md`: Canonical local and GitHub template file map. - `references/system-variable-tuning.md`: Tuning policy and variable bounds. - `assets/internet_workflow_profile.template.json`: Copyable starter profile.
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.
skill-synthesis
Compose multiple installed skills into one coordinated execution stack with ordered packets, minimal context load, and deterministic handoff artifacts. Use when tasks span multiple domains (for example HYDRA + browser + training + deploy) and you need a single combined workflow instead of invoking skills one-by-one.
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`.