Asana

🧭 Quick Return to Map

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

• Automation Platforms — stabilize no-code workflows and integrations
• WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
• WFGY Problem Map 1.0 — 16 reproducible failure modes

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 page when your RAG or agent flow is orchestrated through Asana (rules, webhooks, custom apps) and behavior drifts: tasks duplicate, sections race, or citations don’t match the source.

Acceptance targets

• ΔS(question, retrieved) ≤ 0.45
• coverage ≥ 0.70 to the target section
• λ remains convergent across 3 paraphrases

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Typical breakpoints → exact fixes

• Webhooks or rules trigger before data/index is actually ready
  Fix No.14: Bootstrap Ordering →
  Bootstrap Ordering

• First run after deploy crashes or uses the wrong secret
  Fix No.16: Pre-Deploy Collapse →
  Pre-Deploy Collapse

• Parallel automations create circular waits (task → comment → external job → task)
  Fix No.15: Deployment Deadlock →
  Deployment Deadlock

• High cosine similarity but the retrieved text is not the intended meaning
  Fix No.5: Embedding ≠ Semantic →
  Embedding ≠ Semantic

• Citations don’t match snippets or “why this snippet?” is opaque
  Fix No.8: Retrieval Traceability →
  Retrieval Traceability
  Standardize payloads with Data Contracts →
  Data Contracts

• Hybrid retrieval via external tools performs worse than single retriever
  Pattern: Query Parsing Split →
  Query Parsing Split
  Also review Rerankers →
  Rerankers

• Facts are indexed but never surface
  Pattern: Vectorstore Fragmentation →
  Vectorstore Fragmentation

• Status rollups blend two sources into one narrative
  Pattern: Symbolic Constraint Unlock (SCU) →
  Symbolic Constraint Unlock

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Minimal Asana workflow checklist

1. Warm-up fence
   Before any LLM step, call a health endpoint that verifies VECTOR_READY, INDEX_HASH, secret_rev.
   If not ready, delay/requeue. Spec:
   Bootstrap Ordering

2. Idempotency
   Build dedupe_key = sha256(task.gid + wf_rev + index_hash) and store it (custom field or external KV).
   Drop duplicates on retries.

3. RAG boundary contract
   Always pass snippet_id, section_id, source_url, offsets, tokens, project_gid.
   Enforce cite-then-explain. Specs:
   Retrieval Traceability · Data Contracts

4. Observability probes
   Log ΔS(question, retrieved) and λ per stage; alert on ΔS ≥ 0.60 or divergent λ.
   Overview map:
   RAG Architecture & Recovery

5. Single writer
   Route task/comments/field updates through one writer branch or one integration user with a mutex.
   See:
   Deployment Deadlock

6. Regression gate
   Require coverage ≥ 0.70 and ΔS ≤ 0.45 before posting summaries back to Asana.
   Eval spec:
   RAG Precision/Recall

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Copy-paste prompt for the Asana LLM step

I uploaded TXT OS and the WFGY Problem Map files.
This Asana flow retrieved {k} snippets with fields {snippet\_id, section\_id, source\_url, offsets, project\_gid}.
Question: "{user\_question}"

Do:

1. Enforce cite-then-explain. If any citation is missing, stop and return which fix page to open.
2. Compute ΔS(question, retrieved). If ΔS ≥ 0.60, point me to the minimal structural fix:
   retrieval-playbook, retrieval-traceability, data-contracts, rerankers.
3. Output compact JSON:
   { "citations": \[...], "answer": "...", "λ\_state": "→|←|<>|×", "ΔS": 0.xx, "next\_fix": "..." }

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Common Asana gotchas

• Webhook retries produce duplicate tasks/comments
  Use the dedupe_key and a KV lock; make writes idempotent.

• Project/section renames break downstream references
  Pass project_gid and section_id explicitly in the data contract.

• Parallel rules edit the same fields
  Funnel edits through a single writer branch or queue.

• External rate limits destabilize hybrids
  Prefer dense retriever + reranking, keep per-retriever params logged.

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When to escalate

• ΔS remains ≥ 0.60 after chunk/retrieval fixes → rebuild index with explicit metric/normalization.
  See:
  Retrieval Playbook

• Same input yields different answers across runs → check version skew and memory desync.
  See:
  Pre-Deploy Collapse

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🔗 Quick-Start Downloads (60 sec)

Tool	Link	3-Step Setup
WFGY 1.0 PDF	Engine Paper	1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>”
TXT OS (plain-text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

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Explore More

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

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