IF.emotion Evidence Trace — 09aad3e1-f420-451e-a189-e86f68073dc0

Independent verification

# Download the evidence bundle
curl -fsSL -o emo.tar.gz 'https://infrafabric.io/static/hosted/emo_trace_payload_09aad3e1-f420-451e-a189-e86f68073dc0.tar.gz'

# Verify transport integrity
sha256sum emo.tar.gz
# Expected: 1e729429e29e47b2efd1c78d07c16df653af1363786468d436f27e81cc870b3d

# Download verifier (single-file)
curl -fsSL -o iftrace.py 'https://infrafabric.io/static/hosted/iftrace.py'
python3 iftrace.py verify emo.tar.gz --expected-sha256 1e729429e29e47b2efd1c78d07c16df653af1363786468d436f27e81cc870b3d

User request (captured)

In English: Give me a concrete, testable breakdown of what to do when I feel anxious in meetings. Keep it practical. If you cite clinical context, include [Source: ...] tags.

Model output (captured)

Look, feeling anxious in meetings isn’t “wrong” or weak. It’s your nervous system doing its job: scanning for threat and trying to keep you safe.  

Now I’m going to challenge the story your brain probably adds on top: “This anxiety means I’m not competent” or “I have to get rid of it to perform.” That’s unfalsifiable, and it keeps you stuck. Anxiety is just data. The real question is whether it helps you focus or hijacks you, and you can test that in real time. The frame I use is simple: we’re not here to debate your thoughts, we’re here to map inputs and outputs—what reliably triggers you, what your body does, what you do next, and what result you get. Triggers in this context are not mystical; they’re specific meeting situations that reliably cue the spike, and the point is to identify them with examples, not theory. “You don’t all of a sudden get [emotion] for no reason… there are certain things that trigger the feeling… identify as much as we can… when you think of the last time… what do you recall?” [1]  

Here’s the concrete, testable breakdown for meetings. First, define your “anxiety” in measurable terms before the meeting starts: pick two body signals you can rate 0–10 (tight chest, sweating, throat tension, shaky hands) and one behavior you can count (how many times you interrupt, how often you over-explain, how many times you stay silent when you wanted to speak). That’s your baseline. Second, do a 30-second trigger scan the moment you notice the spike: what exactly happened in the environment and demand? Someone senior joined, you were put on the spot, you saw your name on the agenda, you heard silence after you spoke—make it one sentence and write it down. This turns “I’m anxious” into “This cue triggers this response,” which is testable. Third, run a one-minute control move that targets process, not content: shift attention away from feeding worry and toward the next useful action, because a lot of worry work is moving from content-fighting to process-shifting (attention and behavior) [2]. Concretely, you do one slow exhale, drop your shoulders, plant both feet, and silently label the task in five words: “Clarify goal, then ask one question.” Then you do exactly one “approach behavior” instead of an avoidance behavior: ask a clarifying question, or summarize what you understood, or propose a next step—something observable that moves the meeting forward (Inference).  

If you spiral into rumination afterward—replaying what you “should’ve said”—treat it as a behavior pattern, not a truth-seeking mission. Rumination is often a form of avoidance, and the practical move is to spot warning signs, swap in an alternative, and change the contingencies that keep it going (for example, stop reopening the chat log, stop re-reading your notes) [2]. That’s how you reduce frequency and intensity: not by winning an argument with your mind, but by changing what you do when the loop starts (Inference).  

And when you’re drowning in nuance, use the aspiradora rule: a vacuum doesn’t need 50 labels for dirt. It needs one question—“Is there dirt: yes or no?” Your meeting version is: “Am I avoiding right now: yes or no?” If yes, do one approach behavior within 60 seconds.  

What specific behavior will you do in the next 30 minutes—the smallest, observable action you can take to practice this (for example, write your two body ratings and your one approach sentence, or send one clarifying question to a colleague as rehearsal)?

Sources:
[1] Francine_Shapiro_EMDR_Basic_Principles__428f60c0.pdf — Full text of Francine_Shapiro_EMDR_Basic_Principles__428f60c0.pdf (if://citation/3862ce4a-bca5-4090-b5e9-5652fee391ae/v1) — TTT verified (hybrid-fips204)
[2] Hayes_Acceptance_and_Commitment_Therapy_ACT_core_text__19132c8c.pdf — Full text of Hayes_Acceptance_and_Commitment_Therapy_ACT_core_text__19132c8c.pdf (if://citation/df2b76c0-1c16-484d-97d4-d399d87e0e33/v1) — TTT verified (hybrid-fips204)

Trace: 09aad3e1-f420-451e-a189-e86f68073dc0

TTT child artifacts

IF.STORY (human-readable narrative)

# IF.story — contextual narrative log

Trace: `09aad3e1-f420-451e-a189-e86f68073dc0`

This file is a deterministic narrative projection of the hash-chained event stream (`trace_events.jsonl`). Each line includes the `event_hash` so it can be cross-checked against the ground truth.

- 2025-12-21T07:38:45Z | `req_seen` | REQ_SEEN witness appended; hour=20251221T07 count=1 merkle_root=b1011e1fc3635c70cea2bfbe06cff58a3a1f230191fba4d9d95ff4aaf1e46ee1 | event_hash=5a0d337879efab5e91d41b3aa0a8112f51ab3a88fc9878afd4271bfaefe5b2e6
- 2025-12-21T07:38:45Z | `request_received` | Auth+quota succeeded; provider=codex model=gpt-5.2 stream=False user_len=174 auth_ms=4 | event_hash=e6e1b9052b277375685f3e5a67ea94bdb0bf325c5a0fb3af117a86f21378bd5a
- 2025-12-21T07:38:46Z | `retrieval_done` | Retrieval completed; retrieved_count=2 rag_ms=1377 retrieval_event_id=dc6a1b88-6a72-4d49-bb70-5244d5479018 | event_hash=30eeb43a4f34e0fbb32f5ddbf9da5874416a72eb21f070941a379a56f4b7bf10
- 2025-12-21T07:38:46Z | `prompt_built` | Prompt built; prompt_sha256=e33d998a367220950ab15238ed00125a2e6d9c77e477dcfee38383c81db4a297 | event_hash=ecb0c05eb302c145e62ed2ef80ec88f9a5e2107d6ac5660909dc3f3f5c48a4f2
- 2025-12-21T07:39:04Z | `model_done` | Model finished; provider=codex model=gpt-5.2 llm_ms=17879 | event_hash=d086c656e2e80abe4ed53c745c5d58fee4b8625149e7bb694aed8d62cef55962
- 2025-12-21T07:39:04Z | `replacement_applied` | Normalized citation tags; [Source: if://citation/3862ce4a-bca5-4090-b5e9-5652fee391ae/v1] -> [1] | event_hash=da86a144803037149b222f64a8aeac68988f1fa4e088b3d159cc4d13137e3da3
- 2025-12-21T07:39:04Z | `replacement_applied` | Normalized citation tags; [Source: if://citation/df2b76c0-1c16-484d-97d4-d399d87e0e33/v1] -> [2] | event_hash=4e9dd7f0b3fbad6769b41c446a1036709968d1985336576a5ff341eced5cef9d
- 2025-12-21T07:39:04Z | `trace_finalizing` | Trace finalized; ok=True provider=codex | event_hash=ca78015610b622ff97d227143a670785f4c240a481e87f08a8f2da18f1c11ae1

Notes:
- Ground truth remains `trace_events.jsonl` + `ttt_signed_record.json`.
- REQ_SEEN hour ledger + Merkle head are included as `req_seen_<hour>.jsonl` and `req_seen_head_<hour>.json`.