seq

Overview

Mine ~/.codex/sessions/ JSONL and ~/.codex/memories/ files quickly and consistently with a single script. Focus on skill usage, format compliance, token counts, and memory-file mining.

Trigger Cues

• Questions that ask to verify prior session output using artifacts ("use $seq to find it" / "what did that session actually say").
• Orchestration ledger forensics from session traces (Orchestration Ledger, spawn_agents_on_csv, wave CSVs, concurrency counts).
• Concurrency math validation (max_concurrency, effective fanout, occurrences of peak parallelism, planned rows vs actual parallelism).

Zig CLI Iteration Repos

When iterating on the Zig-backed seq helper CLI path, use these two repos:

• skills-zig (/Users/tk/workspace/tk/skills-zig): source for the seq Zig binary, build/test wiring, and release tags.
• homebrew-tap (/Users/tk/workspace/tk/homebrew-tap): Homebrew formula updates/checksum bumps for released seq binaries.

Quick Start

run_seq() {
  if command -v seq >/dev/null 2>&1 && seq --help 2>&1 | grep -q "skills-rank"; then
    seq "$@"
    return
  fi
  if [ "$(uname -s)" = "Darwin" ] && command -v brew >/dev/null 2>&1; then
    if ! brew install tkersey/tap/seq; then
      echo "brew install tkersey/tap/seq failed; hard-failing." >&2
      return 1
    fi
    if command -v seq >/dev/null 2>&1 && seq --help 2>&1 | grep -q "skills-rank"; then
      seq "$@"
      return
    fi
    echo "brew install tkersey/tap/seq did not produce a compatible seq binary." >&2
    return 1
  fi
  echo "no compatible seq binary found; install with brew install tkersey/tap/seq" >&2
  return 1
}

run_seq datasets --root ~/.codex/sessions

Commands below use seq directly for brevity. Use run_seq when you want brew-aware bootstrap with binary-only execution.

Query (JSON Spec)

Run flexible mining via query with a small JSON spec (inline or @spec.json).

List datasets:

seq datasets --root ~/.codex/sessions

Show dataset fields/params:

seq dataset-schema --dataset token_deltas --root ~/.codex/sessions

Examples:

Rank skill usage:

seq query --root ~/.codex/sessions --spec \
  '{"dataset":"skill_mentions","group_by":["skill"],"metrics":[{"op":"count","as":"count"}],"sort":["-count"],"limit":20,"format":"table"}'

Daily token totals (from token_count events):

seq query --root ~/.codex/sessions --spec \
  '{"dataset":"token_deltas","group_by":["day"],"metrics":[{"op":"sum","field":"delta_total_tokens","as":"tokens"}],"sort":["day"],"format":"table"}'

Top sessions by total tokens:

seq query --root ~/.codex/sessions --spec \
  '{"dataset":"token_sessions","select":["path","total_total_tokens"],"sort":["-total_total_tokens"],"limit":10,"format":"table"}'

Rank tool calls:

seq query --root ~/.codex/sessions --spec \
  '{"dataset":"tool_calls","group_by":["tool"],"metrics":[{"op":"count","as":"count"}],"sort":["-count"],"limit":20,"format":"table"}'

Memory files by category:

seq query --spec \
  '{"dataset":"memory_files","group_by":["category"],"metrics":[{"op":"count","as":"count"}],"sort":["-count"],"format":"table"}'

Ready-made specs

Prebuilt specs live in specs/.

seq query --root ~/.codex/sessions --spec @specs/skills-rank.json
seq query --root ~/.codex/sessions --spec @specs/tools-rank.json
seq query --root ~/.codex/sessions --spec @specs/tokens-top-days.json
seq query --root ~/.codex/sessions --spec @specs/tokens-top-sessions.json
seq query --root ~/.codex/sessions --spec @specs/tk-trend-week.json

Spec reference

Top-level keys:

• dataset (string, required)
• params (object, optional; dataset-specific)
• where (list of predicates, optional)
• group_by (list of field names, optional)
• metrics (list of aggregations, optional; default count when grouping)
• select (list of field names, optional; for non-grouped queries)
• sort (list of field names; prefix with - for descending)
• limit (int, optional)
• format (table | json | csv | jsonl; default: table when grouped, else jsonl)

Where predicate shape:

{"field":"day","op":"eq","value":"2026-02-05"}

Supported where.op:

• eq, neq
• gt, gte, lt, lte (numeric-ish compare)
• in, nin (value is a JSON list)
• contains (substring)
• contains_any (value is a JSON list of substrings; optional case_insensitive: true)
• regex (regex-like matching with ^, $, and alternation |; optional case_insensitive: true)
• regex_any (value is a JSON list of regex-like strings; OR semantics)
• exists, not_exists

Metrics shape (grouped queries):

{"op":"sum","field":"delta_total_tokens","as":"tokens"}

Supported metrics.op:

• count
• sum, min, max, avg
• count_distinct

Tasks

1) Rank skill usage

seq skills-rank --root ~/.codex/sessions

Common options:

• --format json|csv
• --max 20
• --roles user,assistant
• --since 2026-01-01T00:00:00Z

2) Trend a skill over time

seq skill-trend --root ~/.codex/sessions --skill tk --bucket week

3) Report on a specific skill

seq skill-report --root ~/.codex/sessions --skill tk \
  --sections "Contract,Invariants,Creative Frame,Why This Solution,Incision,Proof" \
  --sample-missing 3

Another example:

seq skill-report --root ~/.codex/sessions --skill fix \
  --sections "Contract,Findings,Changes applied,Validation,Residual risks / open questions" \
  --sample-missing 3

4) Role breakdown by skill

seq role-breakdown --root ~/.codex/sessions --format table

5) Audit section compliance

seq section-audit --root ~/.codex/sessions \
  --sections "Contract,Invariants,Creative Frame" \
  --contains "Using $tk" \
  --sample-missing 5

6) Export occurrences

seq occurrence-export --root ~/.codex/sessions --format jsonl --output occurrences.jsonl

7) Bundle a report

seq report-bundle --root ~/.codex/sessions \
  --top 20 --skills tk,fix \
  --sections "Contract,Invariants,Creative Frame,Why This Solution,Incision,Proof"

8) Token usage summary

seq token-usage --root ~/.codex/sessions --top 10

9) Reproducible perf harness

Run stable workloads with fixed warmup/sample counts and optional baseline comparison.

zig build bench -Doptimize=ReleaseFast -- --config perf/frozen/workload_config.json

Head-to-head Zig vs Python gate (runs parity first):

scripts/perf/head_to_head.sh --root testdata/golden/sessions --gate 20 --samples 9 --warmup 1

Differential parity against the Python oracle:

scripts/parity/run_diff.sh --root ~/.codex/sessions/2026/02/19

10) Find sessions by prompt text

seq find-session --root ~/.codex/sessions --prompt "adapter=auto" --limit 20

11) List prompts/messages for one session

seq session-prompts --root ~/.codex/sessions --session-id <session_id> \
  --roles user,assistant --strip-skill-blocks --limit 100

12) Cue vs invoked discovery-skill rate

seq routing-gap --root ~/.codex/sessions \
  --cue-spec @cue-spec.json \
  --discovery-skills grill-me,prove-it,complexity-mitigator,invariant-ace,tk \
  --format table

13) Orchestration concurrency summary

seq orchestration-concurrency --root ~/.codex/sessions --session-id <session_id> --format table

Or target one JSONL directly:

seq orchestration-concurrency --path /absolute/path/to/rollout.jsonl --format json

Notes

• Default root: ~/.codex/sessions.
• memory_files defaults to ~/.codex/memories and accepts params.memory_root to override.
• Skill names are inferred from ${CODEX_HOME:-$HOME/.codex}/skills by default, and from ${CLAUDE_HOME:-$HOME/.claude}/skills when needed.
• Runtime bootstrap policy: require the Zig seq binary. On macOS with brew, treat brew install tkersey/tap/seq failure (or incompatible binary) as a hard error.
• Add --output <path> to write results to a file.
• query auto-projects only referenced dataset fields (where, group_by, metrics.field, select, and non-grouped sort) to reduce scan overhead.
• find-session returns session_id and path; use these to target follow-on query or resume workflows.
• session-prompts defaults to --roles user; set --roles user,assistant to include both sides of a conversation.
• session-prompts deduplicates mirrored duplicate rows by default; pass --no-dedupe-exact to keep all duplicates.
• Typical flow: run find-session, then pass the returned session_id into session-prompts --session-id <id>.
• orchestration-concurrency reports both configured (max_concurrency) and effective fanout (min(max_concurrency, csv_rows)), plus how many times each maximum occurred.

Resources

• seq binary: CLI for ranking skills, auditing sections, querying datasets, and summarizing token usage.
• zig build bench -Doptimize=ReleaseFast -- --config perf/frozen/workload_config.json: frozen-workload performance runner.
• scripts/parity/run_diff.sh: Zig/Python differential parity harness.
• scripts/perf/head_to_head.sh: parity-gated head-to-head p50 speed benchmark.