Task Breakdown

Task Breakdown

Turns a session's per-request breakdown (the deterministic request_units emitted by session-metrics) into semantic tasks the user actually thinks in — "added auth", "debugged the cache miss" — and labels each with a verdict. You do the one thing deterministic code can't: decide which requests belong to the same task. The script does everything else (cost, turns, tokens, waste signals, the themed page).

Model. This skill runs on your session's current model. It no longer pins one (a hard model: pin ran the inline turn on that model, dragging the whole conversation into that model's context window — on a long session that overflowed and broke invocation). The grouping + verdict work is judgement-heavy, so it wants a capable model; for a cheaper run that's still strong enough, /model sonnet before invoking. Don't drop to Haiku — the semantic verdicts need the headroom.

Division of labour — do not blur it:

• The export owns the numbers. Every cost / turn / token / waste figure comes from request_units in the JSON export. You MUST NOT sum money or invent figures — --render-tasks recomputes all totals from the export.
• You own the grouping + labels only. You assign each request_unit_id to a task, write a short title, a verdict, and a one-line rationale.

Inputs

$ARGUMENTS[0] (optional) = path to a session-metrics JSON export, e.g. exports/session-metrics/session_<id8>_<ts>.json (session scope is the primary target; project_*.json also works — units carry a session_id). The export must contain a request_units array.

If $ARGUMENTS[0] is missing, first generate a session export by invoking the session-metrics skill (or run its script) for the session of interest with --output json html, then use the written session_*.json path.

Steps

1. Locate the export and the renderer.

   • Export: $ARGUMENTS[0], or the JSON you just generated.
   • Renderer: the sibling session-metrics skill's script. Resolve its path (it ships in the same plugin):
     • plugin install: ../session-metrics/scripts/session-metrics.py
     • dev repo: .claude/skills/session-metrics/scripts/session-metrics.py Use whichever exists (glob if unsure).

2. Prepare the worksheet + skeleton (preferred — you are an editor, not an author). Run --prepare-tasks on the export: it prints a compact one-line-per-request worksheet to stdout and writes a renderable candidate <stem>_grouping.json next to the export, with deterministic clustering, seeded titles, and suggested verdicts already filled in.

   python3 <renderer> --prepare-tasks <export.json>

   The worksheet is your single source of grouping signals — do not re-probe the JSON with jq/Read. Each row shows the unit's candidate cluster (cl), turns, cost, tokens, risk/reread/cbreak, idle gap, snippet, and top tools; [cont] marks an agent-completion continuation and [blank] a no-prompt unit (both pre-attached to the preceding cluster). On a large session (>120 request units) the worksheet prints a bounded per-cluster summary instead of per-unit rows (so stdout never overflows the prompt) — the written skeleton still covers every unit, so treat it as your authoritative surface and edit it (merge/split/rename) rather than reconstructing the full per-unit list inline. Then edit the skeleton per steps 3–5 below rather than writing it from scratch: rename each seeded title (and drop its _auto_title field once named), merge/split clusters where the worksheet warrants, write one-line rationales, and fill any blank verdict the skeleton left for your judgment. Skip to step 6 (render) when done.

   (Fallback — manual authoring.) If you are not using --prepare-tasks, load the export JSON and read request_units directly. Each unit has: unit_id ("<session_id>:<anchor_index>"), prompt_snippet, prompt_text, turn_count, combined_cost_usd, total_tokens, tool_histogram, risk_turn_count, reread_path_count, cache_break_count, wall_clock_seconds, idle_gap_before_seconds, slash_command, spawned_subagents, workflow_run_ids, multi_intent_possible. If request_units is absent, tell the user to re-run session-metrics to regenerate the export (the per-request breakdown is a newer feature) and stop.

3. Group into semantic tasks. Read the units in order and cluster consecutive requests that pursue the same goal into one task. Signals, in priority order:

   • Topical/lexical continuity of prompt_snippet/prompt_text (same feature, file, bug, or subject) — the PRIMARY signal.
   • Shared tool_histogram / file targets across adjacent requests.
   • Slash command / skill starts (slash_command, a /debug, /feature-dev, etc.) often begin a task.
   • Idle gaps (idle_gap_before_seconds) — a WEAK, confirming-only hint. A long gap supports a split you already suspect topically; never split on a gap alone (lunch breaks, overnight continuations).
   • A unit flagged multi_intent_possible may belong to two tasks — note it, but keep the unit whole (it cannot be divided). Most sessions yield a handful of tasks. Don't over-segment ("now fix the test" is usually the SAME task as the feature it follows), and don't under-segment (one giant "misc" task is useless). At large scale (many dozens of units, e.g. a project-scope export): group at session granularity — one titled task per coherent session-goal — rather than attempting per-unit segmentation. Never emit a single untitled catch-all task that swallows everything: the renderer's collapse guard flags a blank-titled task covering the bulk of requests, and it is a useless grouping anyway. If you cannot segment meaningfully, that is a signal the input is too coarse for this skill (prefer a single-session export).

4. Label each task with a verdict, using the deterministic waste signals as evidence, NOT a guess:

   • worth_it — the task reached its goal at reasonable cost; low risk_turn_count / re-read churn relative to its size.
   • likely_waste — high risk_turn_count, repeated reread_path_count, many cache_break_count, or a long turn/cost run with little to show (e.g. a debug loop that churned).
   • mixed — partly productive, partly churn, or you're unsure. Bias toward mixed/worth_it when uncertain — a wrong likely_waste damages trust more than a missed one. Keep the rationale to one honest sentence tied to the signals ("12 turns, 4 risky re-reads of the same file before the fix landed").

5. Write grouping.json next to the export (same directory), shape:

   {
     "schema_version": "1",
     "scope_label": "session <id8> · <first_ts>",
     "tasks": [
       {
         "title": "Add token-refresh to auth",
         "verdict": "worth_it",
         "rationale": "8 requests, one short debug detour, shipped.",
         "request_unit_ids": ["<sid>:2", "<sid>:3", "<sid>:5"]
       }
     ]
   }

   Cover every unit_id exactly once across all tasks. (Any you leave out are swept into a synthetic "Ungrouped requests" task automatically, and the renderer warns — aim for full coverage.)

6. Render the companion:

   python3 <renderer> --render-tasks <export.json> <grouping.json>

   The script validates the grouping (flags duplicate / unknown unit ids, schema drift), recomputes every total from the export, and writes <stem>_tasks.html + <stem>_tasks.md next to the export. It prints the output paths and any validation warnings.

7. Report back to the user with a SHORT SUMMARY ONLY — do not paste the full task list or the rendered *_tasks.md inline (on a large session that overflows the reply with "Prompt is too long" / output truncation). Include: a one-line result (N tasks, total cost, coverage %), the verdict counts (worth_it / mixed / likely_waste), any grouping warnings the script surfaced, and the *_tasks.html / *_tasks.md paths. Read every figure back from the script's stdout / the rendered *_tasks.md — never recompute. Optionally name up to 3 notable tasks; the full per-task detail lives in the written files. Offer to open the HTML page.