Analyzed ATIF trajectory with context-lens and
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Analyzed ATIF trajectory with context-lens and
Agent
Codex
Model
GPT-5.5
Shared
May 28, 11:01 PM
Messages181
Reviewed user request and input files.
UnderstandConverted raw ATIF trajectory to context-lens format.
EditAdapted taxonomy and built context-lens spec.
BuildRan context-lens analysis with specified model.
VerifyNormalized export labels to match taxonomy.
EditUse context-lens CLI to analyze this ATIF trajectory: 663d455e-102f-46d4-b78d-c792c71fdb32.raw-atif.trajectory.json
Do it as per the taxonomy in taxonomy.md
Make some modifications to the taxonomy to exactly match whatever modifications exist in context-lens-export-a5a7b3be3e5c.json
Context lens classification happens on a small segment at a time, but some of these analyses require knowledge of what happened in the whole run. Therefore, look and understand what happened in the whole trajectory first, and use that information to better prompt the classification for this task.
Use context-lens CLI to analyze this ATIF trajectory: 663d455e-102f-46d4-b78d-c792c71fdb32.raw-atif.trajectory.json
Do it as per the taxonomy in taxonomy.md
Make some modifications to the taxonomy to exactly match whatever modifications exist in context-lens-export-a5a7b3be3e5c.json
Context lens classification happens on a small segment at a time, but some of these analyses require knowledge of what happened in the whole run. Therefore, look and understand what happened in the whole trajectory first, and use that information to better prompt the classification for this task.
use gpt 5.4 mini instead of 4o-mini.
I’ll first read the taxonomy/export shape and skim the full trajectory so the segment-level classification has the larger story in view. Then I’ll adapt the taxonomy and run context-lens with the requested model.
The CLI is available and accepts a --model flag, so the requested model can be passed directly. I’m reading the export now to see the exact taxonomy deltas before I build the spec.
The prior export narrows AFT into four Context Lens dimensions and adds “non-failure” buckets: AX, B0, C0, and D0, plus a reduced C-code set. I’m going to mirror those names/colors exactly in the new spec rather than using the full taxonomy verbatim.
The trajectory story is clear enough now: the agent explored a Yelp fastText task, installed the Debian CLI, built train/valid files, trained several models, never produced /app/model.bin, and the final step is still in a long-running tuning loop with no delivery. The dominant failure shape is non-convergence plus premature non-delivery, with some repeated tool polling/long experiments along the way.
I’m writing a Context Lens spec that preserves the export’s exact component names and colors, while adding the full-run summary into the segmentation/classification prompt so each segment can be judged with the whole run in mind.
Spec is in place at /Users/atharva/cvstuff/context-lens-spec-663d455e.json. Now I’m running Context Lens on the raw ATIF trajectory with gpt-5.4-mini.
The raw ATIF file itself isn’t a format context-lens parses directly. I’m going to convert the ATIF steps into the same conversation-style JSON structure Context Lens expects, preserving step ids, tool outputs, and the original trajectory content.
Check if 663d455e-102f-46d4-b78d-c792c71fdb32.trajectory.json is in the required format?
I’ll verify the structure of the converted .trajectory.json itself, not the raw ATIF file. The key question is whether it has Context Lens’s transcript wrapper shape: top-level transcripts[], each with messages[] containing role and content.
Yes. 663d455e-102f-46d4-b78d-c792c71fdb32.trajectory.json is in the required Context Lens-compatible format.
It has the expected top-level wrapper:
description, id, metadata, name, transcript_groups, transcripts
And inside transcripts[0] it has messages[] with valid string role and content fields. It contains 157 messages total:
assistant: 77
tool: 77
user: 2
system: 1
So the issue was specifically with the raw ATIF file, not this converted .trajectory.json file.
Okay, continue with that
Got it, I’ll use the converted 663d455e-102f-46d4-b78d-c792c71fdb32.trajectory.json as the Context Lens input and keep the same modified AFT spec/model settings.
Context Lens accepted the converted file and is segmenting now. I’ll poll it until the analysis/export finishes, then I’ll inspect the generated analytics before summarizing.
The run completed and wrote /tmp/context-lens-export-e4412a3553a0.json. One thing I spotted: the classifier introduced some bare-code labels like B0 and C6.1 alongside the exact export-style labels, so I’m going to normalize those back into the taxonomy’s exact component names and save a clean export in the workspace.
I normalized 300 bare-code labels into the exact export-style component names and wrote the clean export plus a recalculated analytics sidecar. I’m doing one validation pass now to confirm there are no stray labels outside the modified taxonomy.
Done. I ran context-lens on the converted trajectory with --model gpt-5.4-mini, then cleaned the export so the labels exactly match the modified taxonomy from the prior export.
Files produced:
The run output was written originally to /tmp/context-lens-export-e4412a3553a0.json, but I normalized 300 bare labels like B0, C6.1, and D0 into the exact component names from the modified taxonomy.
Top analytics after cleanup:
| Dimension | Largest components |
|---|---|
A_stage | A4 Verifying & testing 48.4%, A3 Executing & generating 29.4%, A2 Locating & exploring 12.7% |
B_root_cause | B0 No agent failure 72.5%, B4 Tool/environment interaction 27.5% |
C_behavior | C0 No agent failure behavior 54.3%, C8.4 Tool-output misread 17.0%, C6.1 Step repetition/infinite loop 14.6% |
D_impact | D0 No negative impact 68.9%, D1 Recoverable mild 19.0%, D5 Silent 6.0%, D2 Recoverable moderate 6.0% |
Validation passed: the clean export has bad_count 0, meaning every classified dimension value now belongs to the modified taxonomy exactly.
Publish to context viewer
I’ll publish the clean export by creating a secret gist, then plug its raw URL into the Context Viewer import link.
GitHub auth has gist scope, and the export looks sane. I’m creating the secret gist now and will use the raw file URL for the viewer import.
This gh version makes secret gists the default and doesn’t accept --secret. I’ll rerun without that flag.
The secret gist is created. I’m fetching its raw URL now so the Context Viewer link imports directly.
this just failed to parse on the viewer?
Let me check the published raw file and compare it against the known-good export shape. If the JSON itself is valid but the viewer still rejects it, I’ll republish a minimally edited export instead of the normalized one.
The raw gist is valid JSON and has the same top-level shape as the earlier export, so the parse failure is probably from a schema expectation deeper in the viewer rather than malformed JSON. I’m going to open the viewer and check the client-side error.
The in-app browser is loading a blank DOM with no console error captured, which matches a client-side import failure before visible UI. I’m going to test two likely causes: unencoded import URL, and the cleaned export mutation.
Encoding got the viewer to actually load the file, but the viewer is rejecting the saved-state export with “No suitable parser found.” I’m going to publish the untouched CLI export next; if that works, my cleanup changed a field the viewer parser depends on.
I found the actual breakage: my cleanup script accidentally dropped turnCount from analytics.componentComparison[0]. The viewer’s import parser is stricter than jq and rejects that as “No suitable parser.” I’ll repair the clean export, update the gist, and test the viewer link before giving it back.
Yep, you were right. The cleaned export was valid JSON, but I accidentally dropped turnCount from the export analytics object, and the viewer parser rejected it.
I fixed the export, updated the gist, and verified it now loads in Context Viewer with 157/157 messages.