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IFTTT — Guardrails and Fix Patterns

🧭 Quick Return to Map

You are in a sub-page of Automation Platforms.
To reorient, go back here:

Think of this page as a desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.

Use this when your automation is built on IFTTT (Webhooks, Google Sheets, Gmail, Slack, Calendar). If flows “work” but answers are still wrong, citations are off, or behavior differs between applets and direct API tests, anchor your diagnosis here.

Acceptance targets

  • ΔS(question, retrieved) ≤ 0.45
  • Coverage ≥ 0.70 to the intended section or record
  • λ stays convergent across 3 paraphrases

Typical breakpoints → exact fixes

Minimal IFTTT pattern with WFGY checks

A compact pattern that keeps cite-first schema, observable retrieval, and ΔS/λ validation even when steps are split across applets.

Applet A — Trigger (Webhooks)
- Input JSON: { question, k? }

Applet B — Retrieve (Webhooks → your retriever API)
- Returns: snippets[] = { snippet_id, text, source, section_id }
- Store: a short-lived record (e.g., in Google Sheets or your API, keyed by request_id)

Applet C — Assemble + Call LLM (Webhooks → your prompt API)
SYSTEM:
  Cite lines before any explanation. Keep per-source fences.
TASK:
  Answer only from the provided context. Return citations as [snippet_id].
CONTEXT:
  <joined snippets with snippet_id + source + text>
QUESTION:
  <user question>

Applet D — WFGY Post-check (Webhooks → wfgyCheck)
- Body: { question, context, answer }
- Returns: { deltaS, lambda, coverage, notes }

Applet E — Gate and Notify
IF deltaS ≥ 0.60 OR lambda != "→"
  → send failure payload with trace table (snippet_id↔citation), ask to retry after fix
ELSE
  → deliver { answer, citations[], deltaS, lambda, coverage } to user channel

Reference specs: RAG Architecture & Recovery · Retrieval Playbook · Retrieval Traceability · Data Contracts

IFTTT-specific gotchas

  • Sheets cell truncation hides context length. Store only snippet_id and a short preview in Sheets, keep full text in your API or DB. See Data Contracts

  • Hidden tokenization when composing prompts in plain text fields. Always assemble prompts in your API layer to enforce the cite-first schema. See Retrieval Traceability

  • Environment mismatch between mobile and server triggers. Bootstrap checks must verify secrets, version, and index availability before the first LLM call. See Pre-Deploy Collapse

  • Ordering hazards when multiple applets race. Add an explicit rerank step after per-source ΔS ≤ 0.50, then lock order. See Rerankers

  • Attachment loss in cross-channel handoffs. Store attachment metadata and link by request_id, never inline large blobs into prompts.

When to escalate

  • ΔS stays ≥ 0.60 after chunking and retrieval adjustments → rebuild index with explicit metric flags and unit normalization. Retrieval Playbook

  • Answers flip between device-triggered and server-triggered runs → verify version skew, secret scope, and boot ordering. Bootstrap Ordering · Deployment Deadlock


---

### 🔗 Quick-Start Downloads (60 sec)

| Tool | Link | 3-Step Setup |
|------|------|--------------|
| **WFGY 1.0 PDF** | [Engine Paper](https://github.com/onestardao/WFGY/blob/main/I_am_not_lizardman/WFGY_All_Principles_Return_to_One_v1.0_PSBigBig_Public.pdf) | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + \<your question>” |
| **TXT OS (plain-text OS)** | [TXTOS.txt](https://github.com/onestardao/WFGY/blob/main/OS/TXTOS.txt) | 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly |

---

<!-- WFGY_FOOTER_START -->

### Explore More

| Layer | Page | What it’s for |
| --- | --- | --- |
| ⭐ Proof | [WFGY Recognition Map](/recognition/README.md) | External citations, integrations, and ecosystem proof |
| ⚙️ Engine | [WFGY 1.0](/legacy/README.md) | Original PDF tension engine and early logic sketch (legacy reference) |
| ⚙️ Engine | [WFGY 2.0](/core/README.md) | Production tension kernel for RAG and agent systems |
| ⚙️ Engine | [WFGY 3.0](/TensionUniverse/EventHorizon/README.md) | TXT based Singularity tension engine (131 S class set) |
| 🗺️ Map | [Problem Map 1.0](/ProblemMap/README.md) | Flagship 16 problem RAG failure taxonomy and fix map |
| 🗺️ Map | [Problem Map 2.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card for RAG and agent pipeline diagnosis |
| 🗺️ Map | [Problem Map 3.0](/ProblemMap/wfgy-ai-problem-map-troubleshooting-atlas.md) | Global AI troubleshooting atlas and failure pattern map |
| 🧰 App | [TXT OS](/OS/README.md) | .txt semantic OS with fast bootstrap |
| 🧰 App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q&A built on TXT OS |
| 🧰 App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image generation with semantic control |
| 🏡 Onboarding | [Starter Village](/StarterVillage/README.md) | Guided entry point for new users |

If this repository helped, starring it improves discovery so more builders can find the docs and tools.  
[![GitHub Repo stars](https://img.shields.io/github/stars/onestardao/WFGY?style=social)](https://github.com/onestardao/WFGY)

<!-- WFGY_FOOTER_END -->