Per-Segment efficiency analyzer. Compares the Segment's actual wall-clock and token spend to a reasonable human-or-tighter-agent counterfactual, and flags two patterns: (a) wasteful branches — detours that, in hindsight, weren't on the critical path; (b) model-size mismatch — Segments where a smaller/faster model would have served, or where the chosen model was under-powered and the Segment thrashed. Outputs feed the Skills / MCP recommendation buckets when the inefficiency points at a tooling fix.
Per-Segment efficiency analyzer. Compares the Segment's actual wall-clock and token spend to a reasonable human-or-tighter-agent counterfactual, and flags two patterns: (a) wasteful branches — detours that, in hindsight, weren't on the critical path; (b) model-size mismatch — Segments where a smaller/faster model would have served, or where the chosen model was under-powered and the Segment thrashed. Outputs feed the Skills / MCP recommendation buckets when the inefficiency points at a tooling fix.
user-invocable
false
Analyze segment efficiency
Per-Segment efficiency check. Runs on every Segment regardless of Outcome — Successes can still be wasteful.
Inputs
segment: a Segment from segments.json with its meta block populated (event_range, wall_clock_s, tokens_in, tokens_out, model). The orchestrator hands you the Segment directly — you do not walk raw JSONL.
parent_segment: the parent in the Segment tree — needed to judge whether a sub-Segment's spend was proportionate to its parent Goal.
transcript.json: the OpenTranscripts Transcript document, available to dereference event ids from segment.meta.event_range when you need turn-level evidence for a detour.
external_context (optional): external-context.json if present — grounds the human counterfactual in why the session happened.
philosophy_skills, philosophy_mcp: tie-breakers when proposing a tooling fix.
Output
This is the item body. The orchestrator wraps it with id / segment_id / analyzer (see the orchestrator's "Findings-item shape" section) — emit only the fields below.
{"spend":{"wall_clock_s":0,"tokens_in":0,"tokens_out":0,"model":"..."},"human_counterfactual_s":0,"efficiency_ratio":0.0,"findings":[{"kind":"wasteful_branch" | "model_too_large" | "model_too_small" | "well_proportioned" | "meta_incomplete","evidence":"<which event ids or sub-Segments are the detour, or which events retried due to model thrash>","hypothesis":"<what would have made this faster / cheaper — may be null for well_proportioned / meta_incomplete>","recommendation_route":"prompting" | "skills" | "mcp" | "multi" | "none"}]}
Evidence cites OpenTranscripts event ids (the id strings in transcript.json / segments.json), never integer turn numbers.
Sequencing checklist
Read the Segment's meta block. If wall_clock_s or the token counts are missing, do not estimate — emit a single meta_incomplete finding for this Segment (evidence names which meta fields were absent, hypothesis: null, recommendation_route: "none") and let the orchestrator surface it. That is this analyzer's error channel; there is no other way to signal back to phase 2
Estimate a human counterfactual in seconds. Heuristic: how long would a competent engineer take to achieve this Segment's stated Goal, given the same context? Be conservative; this is a rough number
Compute efficiency_ratio = wall_clock_s / human_counterfactual_s. Flag thresholds:
ratio ≥ 5 → likely wasteful_branch or model_too_small
ratio ≤ 0.5 with Success → likely well_proportioned (the agent's edge); record but don't recommend changes
A well_proportioned finding needs no actionable change: set hypothesis: null (or omit it) and recommendation_route: "none". Producing no actionable hypothesis is a real outcome — do not invent a filler hypothesis for a Segment that was already proportionate. (Same stance analyze-agent-transcript-prompt-ambition takes for appropriately_scoped.)
Identify wasteful branches: walk the children. For each child Segment that, in hindsight, didn't contribute to the parent's Outcome, write a wasteful_branch finding. Hindsight is fair game — that's the point of efficiency analysis
Check model-size mismatch:
model_too_large: this Segment was rote (deterministic fix, file rename, schema migration with a clear spec). Could a smaller/cheaper model have served? Reference the model id from meta
model_too_small: the Segment thrashed (multiple retries, contradictory turns, abandoned and restarted). A more capable model might have one-shotted it
For each finding, set recommendation_route to where the fix would live — one of prompting / skills / mcp / multi / none, the shared outcomes-bucket enum (identical to analyze-agent-transcript-failure-hypothesis, so synthesize-agent-transcript-analysis-report routes both analyzers' findings the same way). none is valid for well_proportioned; multi when the fix spans two buckets
Notes
Don't recommend "use a cheaper model" as a default. Model-too-large findings need a real argument; "could have used smaller" is too cheap to be useful unless it's tied to a Skill that explicitly routes the Segment.
Wasteful branches require hindsight evidence. Don't flag a branch as wasteful just because it didn't pan out — flag it only when, looking at the final state, the agent could have known to skip it given a Skill, an MCP server, or a sharper prompt.
Efficiency is judged per-Goal, not in isolation. A 5-minute Plan Segment that prevented a 60-minute Action mistake is well-proportioned even if wall-clock looks high.
Don't double-count with analyze-agent-transcript-failure-hypothesis. If a Segment is a Failure, this analyzer focuses on spend shape, not why it failed — the failure hypothesis owns that.
