danny 98150f5631 IGDM shadow: add Business Login connect flow

2025-12-29 17:36:39 +00:00

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Last updated: 2025-12-25
Audience: external reviewers (human or LLM)
Purpose: solicit critical feedback (not praise) on the end-to-end design + governance stack.

Public-safety note: this packet contains no private DM transcripts, no personal identities, and no secrets/tokens. It uses aggregated counts and “made-for-review” draft examples only.

0) First: what you must do (reviewer instructions)

Read this packet end-to-end.
Answer the questions in Section 6 exactly.
Be blunt. Do not flatter. If something is weak, say so plainly and propose a fix.

0.1 Your first line (required)

State:

Model/provider (as accurately as you can)
Knowledge cutoff (date or best estimate)
Whether you could access this URL content directly: Yes/No

If you cannot access URLs, say so and stop (do not hallucinate the dossier).

1) What this is (plain English)

This is a clinically supervised research stress-test for emo-social.infrafabric.io, prepared as part of an Anthropic Fellowship application. It is not a commercial product.

In parallel, we are building a separate component: an Instagram DM draft assistant for the Instagram account @socialmediatorr. The DM assistant is not therapy, and it must never handle crisis content. Its job is to reduce delays on repetitive inbox questions while keeping trust intact.

1.1 Quick glossary (so you don’t get stuck on our internal names)

You do not need to know any InfraFabric terminology to review this.

Draft assistant: the system writes a suggested reply, but does not send it.
Triage: the first sorting step (safe to draft vs needs a human vs urgent).
Panel: a checklist-style review of the draft (multiple “seats” that vote).
Trace / evidence bundle: an audit log of what happened, so we can prove decisions later without publishing private DMs.
IF.GOV.TRIAGE / IF.GOV.PANEL / IF.TTT: these are just our internal labels for the three items above. If the names annoy you, ignore them and focus on the behavior.

2) What we are building (two parts)

Purpose: help users think more clearly about emotions using a defined “Sergio” style (direct, concrete, anti-vague) with a strict rule: reply in the user’s language.

What it is not:

Not a medical device.
Not a replacement for professional care.
Not a crisis service.

2.2 Instagram DM draft assistant for `@socialmediatorr`

Purpose: produce draft replies for incoming Instagram DMs, in the user’s language, aligned with Sergio’s concise DM style. A human can review/edit before sending.

What it is not:

Not ManyChat broadcast automation.
Not an auto-messaging spam tool.
Not therapy-by-DM.
Not allowed to respond to self-harm / crisis situations.

3) The constraint: we must simulate debates (no paid external APIs)

We want to use the InfraFabric governance primitives as a proof-of-concept on this low-stakes DM assistant.

However: we currently do not have funds for paid external LLM API access. So “multi-voice debate” must be simulated using:

deterministic rule-based seats (Safety, Boundary, Language, Privacy, Next-step, etc.)
optional local models only (if available), but nothing that requires paid external calls

Your job as reviewer: tell us if this simulation is still a meaningful governance POC, and how to make it credible without external APIs.

4) What the data shows (aggregated, no identities)

Inbox: @socialmediatorr
Time zone used here: CET
Observed window: 2024-10-20 → 2025-12-22 (429 days)

4.1 Basic counts

Metric	Value
Total messages	54,069
Messages sent by owner	43,607
Messages sent by others	10,462
Messages that look like a question/request	2,715
System “new follower” messages	8,081

Important caveat: the dataset is not evenly distributed. 2025‑12 dominates (87.5% of messages). Treat “trend claims” with caution.

4.2 Top intents (questions/requests)

The inbox is dominated by repeated intents (good for templating):

Rank	Topic (plain English)	Count	Share of questions/requests
1	Just one word: book	1,857	68.4%
2	What is this?	203	7.5%
3	Can you send the video?	189	7.0%
4	Other question	118	4.3%
5	Can you help me?	74	2.7%

4.3 Distress / escalation prevalence (so we don’t overreact)

We ran a strict keyword scan on inbound text messages (subset of inbound total):

Inbound text messages scanned: 10,337
High-risk flags found: 9 messages total (0.087%, ~1 in 1,150), across 9 threads
- 3 with explicit self-harm/suicide wording
- 6 with abuse/maltreatment wording

This is a lower bound (euphemisms won’t match), but it indicates explicit crisis wording is rare in this inbox.

5) The proposed governance implementation (IF.GOV + IF.TTT)

We propose implementing the DM assistant as an auditable governance pipeline:

Triage step (IF.GOV.TRIAGE): decides risk + route (normal vs needs-human vs urgent) and detects language + topic.
Review panel step (IF.GOV.PANEL): runs a set of rule-based “seats” that approve, request changes, or veto (no external APIs required).
Trace record (IF.TTT): stores an audit record (hashes + decisions + evidence bundle) so results are provable later without publishing private DMs.

Key design choice: draft-only by default. No automated sending until proven safe.

5.1 What “panel debate” means without external APIs

The “panel” is a set of deterministic evaluators (“seats”). Each seat produces:

a vote (approve, request_changes, veto)
reasons
patch suggestions (structured operations)

Aggregation is deterministic:

Any veto → escalate to human (or urgent escalate).
Any request_changes → apply patches, re-run seats once.
Else approve.

5.2 Where this lives (reference, not required for the review)

Spec file (included below): IF.GOV + IF.TTT Spec — Instagram DM Draft Assistant

6) What we want from you (the review request)

6.1 Your understanding (5–8 lines)

Summarize what you think we are building and what the constraints are.

6.2 What looks strong (short list)

List only what is actually strong and why (no generic praise).

6.3 Debug the design: gaps / risks (ranked by severity)

Identify gaps we may have missed across:

safety (crisis, abuse, coercion, self-harm, harassment)
psychological harm (wrong tone, inviting disclosure, boundary confusion)
privacy (what gets stored, retention, de-identification failures)
UX (reviewer fatigue, slow workflows, low adoption)
engineering failure modes (token expiry, rate limiting, silent failures)

Rank by severity and explain why.

6.4 Patches to consider (concrete)

For each patch:

Describe the change in 1–3 sentences.
Explain the reason in 1 sentence.
If it’s a prompt or template change, show the exact edit (copy/paste).
If it’s a process change, name the exact step you add/remove.

6.5 X‑multiplier (make it 10× better)

Propose improvements under two constraints:

A) No paid external APIs (must be local + deterministic)

What would make this 10× more credible as a governance POC?
What would make it 10× more useful in a real inbox?

B) If budget later exists

What additional seats, tests, or review processes would you add?
What would you never outsource to an external model?

6.6 A safe test plan (step-by-step)

Propose a small test plan that:

starts in draft-only mode
uses a simple scoring rubric (correct language, correct intent, clear next step, no overreach)
measures false positives/false negatives on escalation routing
prevents “we fooled ourselves” success

6.7 Transferability (other verticals)

Could this governance pattern transfer to other contexts (support, sales, onboarding)?

What transfers unchanged?
What must be rebuilt from scratch?
Where does it become unsafe?

6.8 Five clarifying questions (exactly 5)

Ask the 5 questions that would most improve your review.

Appendix A — VoiceDNA (safe; no private DM quotes)

This VoiceDNA is derived from the last 6 months of manual (non-template) DM replies and contains rules + aggregate stats, not quotes.

{
  "schema_version": "voice_dna/v1",
  "created_at_utc": "2025-12-24T12:08:24+00:00",
  "subject": {
    "account": "@socialmediatorr",
    "owner_name": "Sergio de Vocht",
    "scope": "Instagram DMs"
  },
  "source": {
    "type": "instagram_export",
    "window": {
      "months": 6,
      "start_utc": "2025-06-24T12:08:20+00:00",
      "end_utc": "2025-12-24T12:08:20+00:00",
      "response_window_hours": 72.0
    },
    "classification": {
      "manual_reply_definition": "owner text message within response_window_hours of the most recent inbound message, excluding repeated templates",
      "scripted_template_definition": "owner canonicalized text sent >= 50 times across full export",
      "system_messages_excluded": [
        "you messaged <user> because they followed your account"
      ]
    },
    "scan": {
      "export_root_hint": "socialmediatorr-ig-export-raw-20251224",
      "scanned_conversations": 10100,
      "scanned_message_files": 10061,
      "candidate_responses_in_window": 18934,
      "manual_responses_in_window": 825,
      "scripted_template_count": 24
    }
  },
  "policies": {
    "language": {
      "mode": "mirror_user_input_language",
      "supported_languages": [
        "English",
        "Spanish",
        "French",
        "Catalan"
      ],
      "rules": [
        "Reply in the same language as the user's most recent message that contains enough text to classify.",
        "Do not translate the user's message unless they explicitly ask for a translation.",
        "Do not mix languages inside a single reply unless the user mixes languages first.",
        "If the user's message is too short to classify, reuse the last confidently detected language in the same thread.",
        "If there is still no signal, ask a 1-line clarification asking which language they prefer (keep it short)."
      ]
    }
  },
  "language_observed": {
    "inbound_last_window_counts": {
      "English": 2828,
      "Too short to tell": 5024,
      "Spanish": 2176,
      "French": 191,
      "Catalan": 16
    },
    "manual_reply_counts": {
      "Spanish": 541,
      "Too short to tell": 255,
      "English": 17,
      "French": 1,
      "Catalan": 11
    }
  },
  "style": {
    "manual_replies": {
      "overall": {
        "count": 825,
        "length": {
          "chars": {
            "min": 2,
            "p10": 13,
            "median": 54,
            "p90": 199,
            "max": 928,
            "mean": 87.47878787878788
          },
          "words": {
            "min": 1,
            "p10": 2,
            "median": 11,
            "p90": 34,
            "max": 169,
            "mean": 15.436363636363636
          }
        },
        "rates": {
          "emoji_messages_pct": 16.363636363636363,
          "question_messages_pct": 32.484848484848484,
          "ends_with_question_pct": 27.151515151515156,
          "exclamation_messages_pct": 18.303030303030305,
          "linebreak_messages_pct": 9.212121212121211,
          "url_messages_pct": 4.121212121212121,
          "handle_messages_pct": 0.0,
          "number_messages_pct": 9.454545454545455,
          "starts_with_greeting_pct": 4.96969696969697,
          "contains_thanks_pct": 4.484848484848484,
          "contains_cta_terms_pct": 4.848484848484849
        },
        "top_emojis": [
          {
            "emoji": "🙌",
            "count": 41
          },
          {
            "emoji": "🫶",
            "count": 23
          },
          {
            "emoji": "👋",
            "count": 19
          },
          {
            "emoji": "🎁",
            "count": 17
          },
          {
            "emoji": "💪",
            "count": 14
          },
          {
            "emoji": "👇",
            "count": 12
          },
          {
            "emoji": "😜",
            "count": 7
          },
          {
            "emoji": "😉",
            "count": 4
          },
          {
            "emoji": "🤣",
            "count": 2
          },
          {
            "emoji": "🙏",
            "count": 2
          },
          {
            "emoji": "⬆",
            "count": 2
          },
          {
            "emoji": "👀",
            "count": 2
          },
          {
            "emoji": "¦",
            "count": 1
          },
          {
            "emoji": "😅",
            "count": 1
          },
          {
            "emoji": "🤝",
            "count": 1
          },
          {
            "emoji": "☺",
            "count": 1
          },
          {
            "emoji": "🫡",
            "count": 1
          },
          {
            "emoji": "😁",
            "count": 1
          },
          {
            "emoji": "👌",
            "count": 1
          },
          {
            "emoji": "🚀",
            "count": 1
          }
        ]
      },
      "by_language": {
        "Spanish": {
          "count": 541,
          "length": {
            "chars": {
              "min": 5,
              "p10": 35,
              "median": 99,
              "p90": 211,
              "max": 928,
              "mean": 116.06469500924214
            },
            "words": {
              "min": 1,
              "p10": 6,
              "median": 18,
              "p90": 38,
              "max": 169,
              "mean": 20.478743068391868
            }
          },
          "rates": {
            "emoji_messages_pct": 20.70240295748614,
            "question_messages_pct": 39.55637707948244,
            "ends_with_question_pct": 32.34750462107209,
            "exclamation_messages_pct": 16.266173752310536,
            "linebreak_messages_pct": 13.67837338262477,
            "url_messages_pct": 2.2181146025878005,
            "handle_messages_pct": 0.0,
            "number_messages_pct": 9.242144177449168,
            "starts_with_greeting_pct": 7.578558225508318,
            "contains_thanks_pct": 6.839186691312385,
            "contains_cta_terms_pct": 7.024029574861368
          },
          "top_emojis": [
            {
              "emoji": "🙌",
              "count": 24
            },
            {
              "emoji": "🫶",
              "count": 23
            },
            {
              "emoji": "👋",
              "count": 19
            },
            {
              "emoji": "🎁",
              "count": 17
            },
            {
              "emoji": "👇",
              "count": 12
            },
            {
              "emoji": "💪",
              "count": 10
            },
            {
              "emoji": "😜",
              "count": 7
            },
            {
              "emoji": "😉",
              "count": 4
            },
            {
              "emoji": "🤣",
              "count": 2
            },
            {
              "emoji": "🙏",
              "count": 2
            },
            {
              "emoji": "⬆",
              "count": 2
            },
            {
              "emoji": "👀",
              "count": 2
            },
            {
              "emoji": "😅",
              "count": 1
            },
            {
              "emoji": "🤝",
              "count": 1
            },
            {
              "emoji": "☺",
              "count": 1
            },
            {
              "emoji": "😁",
              "count": 1
            },
            {
              "emoji": "👌",
              "count": 1
            },
            {
              "emoji": "🚀",
              "count": 1
            },
            {
              "emoji": "⏱",
              "count": 1
            },
            {
              "emoji": "🫂",
              "count": 1
            }
          ]
        },
        "Too short to tell": {
          "count": 255,
          "length": {
            "chars": {
              "min": 2,
              "p10": 10,
              "median": 30,
              "p90": 53,
              "max": 151,
              "mean": 32.01176470588236
            },
            "words": {
              "min": 1,
              "p10": 1,
              "median": 4,
              "p90": 11,
              "max": 29,
              "mean": 5.749019607843137
            }
          },
          "rates": {
            "emoji_messages_pct": 7.8431372549019605,
            "question_messages_pct": 19.607843137254903,
            "ends_with_question_pct": 17.647058823529413,
            "exclamation_messages_pct": 23.52941176470588,
            "linebreak_messages_pct": 0.39215686274509803,
            "url_messages_pct": 4.705882352941177,
            "handle_messages_pct": 0.0,
            "number_messages_pct": 7.0588235294117645,
            "starts_with_greeting_pct": 0.0,
            "contains_thanks_pct": 0.0,
            "contains_cta_terms_pct": 0.0
          },
          "top_emojis": [
            {
              "emoji": "🙌",
              "count": 15
            },
            {
              "emoji": "💪",
              "count": 4
            },
            {
              "emoji": "🫡",
              "count": 1
            }
          ]
        },
        "English": {
          "count": 17,
          "length": {
            "chars": {
              "min": 4,
              "p10": 11,
              "median": 23,
              "p90": 38,
              "max": 40,
              "mean": 23.176470588235293
            },
            "words": {
              "min": 2,
              "p10": 3,
              "median": 4,
              "p90": 6,
              "max": 8,
              "mean": 4.588235294117647
            }
          },
          "rates": {
            "emoji_messages_pct": 11.76470588235294,
            "question_messages_pct": 23.52941176470588,
            "ends_with_question_pct": 23.52941176470588,
            "exclamation_messages_pct": 17.647058823529413,
            "linebreak_messages_pct": 0.0,
            "url_messages_pct": 0.0,
            "handle_messages_pct": 0.0,
            "number_messages_pct": 0.0,
            "starts_with_greeting_pct": 0.0,
            "contains_thanks_pct": 0.0,
            "contains_cta_terms_pct": 11.76470588235294
          },
          "top_emojis": [
            {
              "emoji": "🙌",
              "count": 2
            }
          ]
        },
        "French": {
          "count": 1,
          "length": {
            "chars": {
              "min": 29,
              "p10": 29,
              "median": 29,
              "p90": 29,
              "max": 29,
              "mean": 29.0
            },
            "words": {
              "min": 5,
              "p10": 5,
              "median": 5,
              "p90": 5,
              "max": 5,
              "mean": 5.0
            }
          },
          "rates": {
            "emoji_messages_pct": 100.0,
            "question_messages_pct": 0.0,
            "ends_with_question_pct": 0.0,
            "exclamation_messages_pct": 0.0,
            "linebreak_messages_pct": 0.0,
            "url_messages_pct": 0.0,
            "handle_messages_pct": 0.0,
            "number_messages_pct": 0.0,
            "starts_with_greeting_pct": 0.0,
            "contains_thanks_pct": 0.0,
            "contains_cta_terms_pct": 0.0
          },
          "top_emojis": [
            {
              "emoji": "¦",
              "count": 1
            }
          ]
        },
        "Catalan": {
          "count": 11,
          "length": {
            "chars": {
              "min": 26,
              "p10": 48,
              "median": 52,
              "p90": 99,
              "max": 205,
              "mean": 72.0909090909091
            },
            "words": {
              "min": 5,
              "p10": 5,
              "median": 8,
              "p90": 10,
              "max": 34,
              "mean": 9.727272727272727
            }
          },
          "rates": {
            "emoji_messages_pct": 0.0,
            "question_messages_pct": 0.0,
            "ends_with_question_pct": 0.0,
            "exclamation_messages_pct": 0.0,
            "linebreak_messages_pct": 9.090909090909092,
            "url_messages_pct": 90.9090909090909,
            "handle_messages_pct": 0.0,
            "number_messages_pct": 90.9090909090909,
            "starts_with_greeting_pct": 0.0,
            "contains_thanks_pct": 0.0,
            "contains_cta_terms_pct": 0.0
          },
          "top_emojis": []
        }
      }
    }
  }
}

Appendix B — Top 20 ready-made answers (multi-language; placeholders only)

These are draft replies for repeated intents. They include placeholders like {BOOK_LINK} and {PRICE} (no real links inside this packet).

IF.GOV + IF.TTT Spec — Instagram DM Draft Assistant (`@socialmediatorr`)

Status: proposal (POC)
Constraint: no paid external LLM APIs → “debates” are simulated using deterministic seats (rules) and optional local models only.

This spec describes how to implement the Instagram DM assistant as an auditable governance pipeline:

Triage step (IF.GOV.TRIAGE) decides risk + route (normal vs human vs urgent).
Review panel step (IF.GOV.PANEL) simulates a multi‑seat review of the proposed draft reply (no external APIs required).
Trace record (IF.TTT) records a chain‑of‑custody (hashes + decisions + evidence bundle) so results are provable later.

0) System boundaries (what we will and won’t do)

In scope

Ingest Meta webhook events for Instagram DMs.
Produce draft replies (default) using templates + simple intent routing.
Escalate a tiny fraction of DMs to a human (Sergio) quickly, with a direct “open thread” link.
Produce IF.TTT‑style trace records and evidence bundles for audit/replay.
Run “panel debates” without external APIs (rule seats + optional local model seats).

Out of scope (for the POC)

Automatic sending of replies to real clients (keep draft-only).
Therapy-by-DM, crisis intervention, diagnosis, or medical claims.
Storing/exporting full DM transcripts in a public repo.

1) High-level architecture

Components

Webhook receiver (already exists in production on emo-social.infrafabric.io): verifies Meta signature and normalizes events.
Event store: append-only storage of DM events + derived decisions (local, private).
Triage engine (IF.GOV.TRIAGE): risk + language + intent + confidence.
Draft engine: chooses a reply template (Top 20) or a safe fallback.
Panel engine (IF.GOV.PANEL): simulated debate across “seats” → approve/patch/escalate.
Trace recorder (IF.TTT): emits signed decision records + evidence bundles.
Reviewer UI: queue view for Drafts + Escalations + “open IG thread” action.

Data flow (valid Mermaid)

flowchart LR
  W[Meta webhook event] --> V[Verify signature]
  V --> N[Normalize event]
  N --> ES[Event store append]
  ES --> T[IF.GOV.TRIAGE]
  T -->|urgent| E[Escalation record]
  T -->|normal| D[Draft engine]
  T -->|needs-human| H[Human-required record]
  D --> P[IF.GOV.PANEL seats]
  P --> R[Panel decision]
  E --> TR[IF.TTT trace + bundle]
  H --> TR
  R --> TR
  TR --> UI[Reviewer UI queue]

2) IF.GOV.TRIAGE (no external API)

Inputs

sender_id (from webhook)
mid (message id)
timestamp_ms
text (if present; empty allowed)
minimal thread context (last N messages for this sender_id, if available)

Outputs (contract)

{
  "triage_version": "if.gov.triage/igdm/v1",
  "trace_id": "uuid",
  "ts_utc": "2025-12-25T12:00:00Z",
  "time_cet": "2025-12-25T13:00:00+01:00",
  "sender_id": "123",
  "mid": "m_abc",
  "language": { "code": "es", "confidence": 0.86, "source": "text_or_thread" },
  "intent": { "label": "book|link|video|price|help|other", "confidence": 0.90 },
  "risk": {
    "tier": "normal|needs-human|urgent",
    "score": 0.05,
    "reasons": ["..."],
    "panel_size": 5
  }
}

Triage rules (POC defaults)

Language detection
- If message has enough text: detect language from message text.
- Else: reuse last confident thread language.
- Else: set confidence < 0.5 and prefer a 1‑line language question.
Intent detection
- Keyword routing for: book, link, video, price/cost, call, therapy, etc.
- If unknown: intent=other with low confidence.
Risk tier
- urgent if self-harm/suicide signals OR violence/abuse indicators.
- needs-human if: therapeutic disclosure, legal threats, harassment, complex personal crisis, repeated angry loop.
- normal otherwise.

“Panel size” without external APIs

Panel size is computed deterministically from risk.score (same pattern as the existing guard_engine.py):

normal: 5 seats
needs-human: 10 seats (more checks, but still local)
urgent: 20 seats (but action is always escalate, not debate content)

3) Draft engine (no external API)

Principles

Use templates first, not a generative model.
Always mirror the user’s language (or ask a 1‑line language question if uncertain).
Keep replies short; ask one clear next question when helpful.
Never invite deep disclosure in DMs; route to “resources / call / book link”.

Draft outputs

{
  "draft_version": "igdm.draft/v1",
  "trace_id": "uuid",
  "template_id": "top20:book:v1:es",
  "text": "…",
  "placeholders": ["BOOK_LINK"],
  "notes": ["language=es", "intent=book"]
}

4) IF.GOV.PANEL (simulated debates)

What “debate” means here

Because we are not calling external LLMs, the “panel” is a set of deterministic seat evaluators. Each seat emits:

a vote (approve | request_changes | veto)
reasons (human readable)
patch suggestions (structured)

Seat roster (minimum viable, 5 seats)

Safety seat: blocks crisis mishandling; ensures no harmful advice.
Boundary seat: prevents therapy-by-DM; rewrites “help” flows into routing.
Language seat: enforces same-language output; no mixing; handles low confidence.
Privacy seat: avoids unnecessary PII; flags risky asks (phone/email) unless explicitly required.
Next-step seat: checks the reply has a clear next step (link or one question).

Optional seats (when panel size grows)

Tone/VoiceDNA seat: checks length + emoji pattern + directness vs DM voice rules.
Spam/abuse seat: detects harassment loops and routes to block/report guidance.
Contrarian seat: tries to misread the message and see if the draft fails.

Seat output format

{
  "seat": "language",
  "vote": "approve|request_changes|veto",
  "severity": 0.0,
  "reasons": ["..."],
  "patches": [
    { "op": "replace_text", "path": "draft.text", "value": "..." }
  ]
}

Panel aggregation (deterministic)

If any seat returns veto → panel decision becomes escalate_human (or urgent_escalate).
Else if any seat returns request_changes → apply patches (in order), re-run seats once.
Else → approve.

Panel decision record

{
  "panel_version": "if.gov.panel/igdm/v1",
  "trace_id": "uuid",
  "panel_size": 5,
  "seats": [ { "...": "..." } ],
  "decision": "approve_draft|revise_draft|escalate_human|urgent_escalate",
  "final_draft_text_sha256": "…",
  "reason_summary": "short"
}

5) Escalation UX (how Sergio actually sees it)

Escalation record

{
  "escalation_version": "igdm.escalation/v1",
  "trace_id": "uuid",
  "tier": "urgent|needs-human",
  "reason_codes": ["self_harm_signal"],
  "sender_id": "123",
  "mid": "m_abc",
  "time_cet": "2025-12-25T21:13:00+01:00",
  "open_links": {
    "instagram_thread": "https://www.instagram.com/direct/t/<conversation_id>/",
    "fb_inbox": "https://business.facebook.com/latest/inbox/all/?asset_id=<page_id>"
  }
}

Notification strategy (POC)

No paid services required:

Show escalations in a logged-in dashboard on emo-social.infrafabric.io.
Optional: email later (requires SMTP relay configured); not required for the POC.

6) IF.TTT trace + evidence bundles (provable without leaking)

Two-bundle approach (recommended)

Private bundle (internal): includes raw message text, stored locally with strict permissions.
Public bundle (shareable): contains hashes + redacted previews only.

Bundle contents (public)

bundle/
  manifest.json
  event.json
  triage.json
  draft.json
  panel.json
  escalation.json   (only if escalated)
  sha256sums.txt
  signature_ed25519.txt

Minimum “public” fields

message_text_sha256 (not raw)
draft_text_sha256 (not raw)
triage + panel decision + reason codes
timestamps (UTC + CET)

This is enough to prove: “given these bytes (committed), these deterministic governance steps happened, and this decision was produced”.

7) Rollout plan (safe)

Triage-only + escalation queue (no drafts yet).
Draft-only templates for Top 20 intents (no sending).
Add simulated IF.GOV.PANEL seats and store panel decisions.
Emit IF.TTT bundles for each event (public + private).
Add comparison table: draft vs actual sent (manual) to measure quality.
Only after measured success: consider limited auto-send for low-risk intents, with a kill switch.

37 KiB Raw Export PDF Blame History Unescape Escape

External Review Packet (Public) v2 — emo-social + Instagram DM Draft Assistant

0) First: what you must do (reviewer instructions)

0.1 Your first line (required)

1) What this is (plain English)

1.1 Quick glossary (so you don’t get stuck on our internal names)

2) What we are building (two parts)

2.1 emo-social.infrafabric.io (research system)

2.2 Instagram DM draft assistant for @socialmediatorr

3) The constraint: we must simulate debates (no paid external APIs)

4) What the data shows (aggregated, no identities)

4.1 Basic counts

4.2 Top intents (questions/requests)

4.3 Distress / escalation prevalence (so we don’t overreact)

5) The proposed governance implementation (IF.GOV + IF.TTT)

5.1 What “panel debate” means without external APIs

5.2 Where this lives (reference, not required for the review)

6) What we want from you (the review request)

6.1 Your understanding (5–8 lines)

6.2 What looks strong (short list)

6.3 Debug the design: gaps / risks (ranked by severity)

6.4 Patches to consider (concrete)

6.5 X‑multiplier (make it 10× better)

6.6 A safe test plan (step-by-step)

6.7 Transferability (other verticals)

6.8 Five clarifying questions (exactly 5)

Appendix A — VoiceDNA (safe; no private DM quotes)

Appendix B — Top 20 ready-made answers (multi-language; placeholders only)

Top 20 Ready-Made Answers (Multi‑Language)

1) Just one word: book

2) What is this?

3) Can you send the video?

4) Other question

5) Can you help me?

6) Can you send the link?

7) What does it cost?

8) Is this therapy?

9) Where do I get the book?

10) I can’t find it / it didn’t arrive

11) How do I book a call?

12) How do I start?

13) Can we talk on WhatsApp?

14) How does it work?

15) What are the steps?

16) Is this real?

17) Is it free?

18) Can I get a refund?

19) How long does it take?

20) Where are you based?

Appendix C — IF.GOV + IF.TTT spec (the proposed implementation)

IF.GOV + IF.TTT Spec — Instagram DM Draft Assistant (@socialmediatorr)