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skills/benchflow-ai/skillsbench/tasks-tictoc-unnecessary-abort-detection-environment-skills-transaction-trace-analysis

tasks-tictoc-unnecessary-abort-detection-environment-skills-transaction-trace-analysis

1
benchflow-ai/skillsbench·Databases·Audit pending·Snapshot 5c797a0be7ea

Summary

This source did not publish a separate summary. Review SKILL.md before using the skill.

SKILL.md

Transaction Trace Analysis

Use this skill when the task provides concrete logs, tables, counters, timestamps, or partial records and asks you to infer transaction behavior.

This skill is about reconstruction under partial evidence:

  • normalize columns into a stable event vocabulary
  • reconstruct per-transaction intent and per-object version timelines
  • join them through explicit fields (not through row order guesses)
  • produce a minimal, auditable classification for the required output

Quick Reference (pick the next file to read)

If you need…Read
Mapping weird columns into tx/key/op/timestamp/counter/status/reasontrace-schema-normalization.md
Grouping events per transaction and avoiding “attempt vs request” mixupstransaction-history-reconstruction.md
Reconstructing a per-key version timeline from writes/validationobject-version-history.md
Interpreting timestamp/counter semantics and scope correctlytimestamp-and-ordering-fields.md
Understanding validation failures, abort reasons, and conservative checksvalidation-and-abort-records.md
Avoiding overclaims when the trace is incompleteevidence-quality-and-uncertainty.md
Emitting deduped/sorted ids and minimal justificationsoutput-classification-patterns.md

Trace Ledger (what you build first)

Build two tiny ledgers before doing any deep inference:

Transaction ledger

txn_id -> {
  seen_keys: [...],
  read-evidence: (key, observed_version_fields...),
  write-evidence: (key, installed_version_fields...),
  validation/abort-evidence: [...],
  candidate_commit_fields: ...,
}

Object ledger

key -> timeline [
  { event: write/abort/validate, txn_id, version_fields, counter_fields },
  ...
]

Only after both exist should you attempt joins like “did T’s read become stale?” or “could an aborted T have committed?”

Workflow (operational)

  1. Schema normalization: name every column; identify scope for each counter/timestamp.
  2. Per-txn reconstruction: summarize each txn’s evidence and candidate serialization point fields.
  3. Per-key reconstruction: build a timeline per key; validate what orders it (wts? counter?).
  4. Decision modeling: re-express the protocol’s decision rule in terms of trace fields.
  5. Classification: prove “must abort” vs “could commit” vs “unknown from evidence.”
  6. Output: dedupe ids, sort if required, write exact format.

Common Pitfalls

  • Treating per-key counters as global order.
  • Using file row order as time order without justification.
  • Calling something ‘unnecessary’ without exhibiting a safe order consistent with evidence.

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