Effortmine

effortmine — calibrated dispatch

/effortmine <request> spends the right amount of reasoning on each part of a job: it breaks the request into subtasks, labels each with a difficulty class, looks up the cheapest effort tier that class is calibrated for, and dispatches each subtask to a worker pinned at that tier. Cheap parts run cheap; hard parts get the reasoning they need. Nothing runs at a blanket session effort just because that is what you happened to set.

Read this whole file before acting.

What this does, and does NOT, do

Does: class-level effort calibration. It maps each subtask to one of six difficulty classes and dispatches at the tier the calibration table recommends for that class. The shipped table is fitted from 450+ real runs on claude-opus-4-8 (2026-07-06 pilot + 2026-07-07 v2/refit; provenance stamped in calibration.json), so that choice is measured and benchmark-backed; if the live table is missing, the skill falls back to an embedded snapshot of those same v1 values.

Does NOT: per-prompt magic. There is no model that reads a specific prompt and divines its exact optimal effort. The unit of calibration is the class, not the individual prompt. Two different T3 subtasks get the same tier. If you need a specific subtask run at a specific effort, say so and it is honored over the table.

Does NOT: invent a spawn-site effort knob. Claude Code's Agent/Task tool takes model but not effort (verified against the installed CLI and the docs, docs/research/01-mechanism-investigation.md). effortmine applies a tier by choosing which agent to spawn: the tier-pinned miner-<tier> workers, identical except for their frontmatter effort:. That indirection IS the mechanism; there is no hidden API.

Resolve paths first

ROOT="${CLAUDE_PLUGIN_ROOT:?effortmine must run as an installed plugin}"
CALIB="$ROOT/bench/state/calibration.json"
DLOG="$ROOT/bench/state/dispatch-log.jsonl"

Phase 1 — Decompose

Break the request into the smallest subtasks that are each independently dispatchable and independently checkable. A subtask is one unit of delegated work with one deliverable. If the request is already a single unit, that is one subtask; proceed.

Phase 2 — Classify each subtask

Assign every subtask exactly one class. Judge by the nature of the reasoning the task demands, not by how long the output is or how important it feels.

*R-research carries a fit-blindness warning (isolated fitting tasks saturate at low even at hard-recipe difficulty, but contextual hard research — the benchmark's X1.3, a multi-document root-cause question inside a composite job — defeats low and needs xhigh): route genuinely hard research to xhigh rather than the table tier. Across composite draws this caveat-aware routing held 100% quality at equal-or-lower cost than effort inheritance; the reliable double-digit savings come from isolated dispatch (v1: −64.7%).

Rules of thumb when torn: if a correct answer needs no deliberation, it is T1. If it needs a single obvious step, T2. If the difficulty is in the diagnosis and the output is short, T3. If a plausible-looking wrong answer would pass a shallow check, it is T4. When still unsure between two adjacent classes, pick the harder one; over-spending one tier is a smaller sin than shipping a wrong cheap answer.

Phase 3 — Load the calibration table

Read $CALIB. It maps each class to a recommended_tier. Schema (see docs/research/04-benchmark-methodology.md section 7.1):

• top-level version (>= 1 means fitted from real benchmark data; 0 would mean an un-fitted a-priori default), fitted_date, model, margin_delta.
• classes.<class>.recommended_tier in low | medium | high | xhigh | max, plus confidence, n_graded, and the measured pass-rate / token fields.

If $CALIB is absent or unreadable, fall back to the embedded snapshot below — the fitted values — and tell the user you are dispatching from the shipped v1 snapshot because the live calibration file could not be read (measured, but possibly older than the installed file).

Embedded fallback table — fitted snapshot (v1 + v2 + refit, 2026-07-07)

{
  "version": 1,
  "source": "fitted-snapshot",
  "fitted_date": "2026-07-07",
  "model": "claude-opus-4-8",
  "suite_version": "pilot-12 + v2-15",
  "margin_delta": 0.10,
  "note": "Snapshot of the fitted table (bench/state/calibration.json, v1+v2 merged). Shown here only as the no-file fallback; the installed file is authoritative and may be newer. Per-class confidence is 'low' at n=3/cell.",
  "warnings": [
    "class R-research fit rests on tasks flagged misclassed",
    "class T4-hard-reasoning fit rests on tasks flagged misclassed"
  ],
  "classes": {
    "T1-mechanical":         {"recommended_tier": "low",  "confidence": "low", "n_graded": 9, "rationale": "9/9 pass at low; saturates at max (more tokens, no quality gain)"},
    "T2-simple-transform":   {"recommended_tier": "low",  "confidence": "low", "n_graded": 9, "rationale": "9/9 pass at low (a-priori guess was medium; low tested non-inferior)"},
    "T3-moderate-reasoning": {"recommended_tier": "high", "confidence": "low", "n_graded": 9, "rationale": "real quality gradient: low 6/9, high 9/9 at ~157 median out-tokens"},
    "T4-hard-reasoning":     {"recommended_tier": "low",  "confidence": "low", "n_graded": 9, "caveat": "9/9 at low, BUT flagged: fitting tasks too easy — prefer xhigh for genuinely hard work until the suite is extended"},
    "R-research":            {"recommended_tier": "low",  "confidence": "low", "n_graded": 18, "caveat": "flagged: isolated fitting tasks saturate at low even at X1.3-recipe difficulty, but the composite X1.3 (multi-doc root cause) needed xhigh (low 0/3, xhigh 3/3) — route genuinely hard research to xhigh"},
    "C-coding":              {"recommended_tier": "medium", "confidence": "low", "n_graded": 18, "rationale": "refit 2026-07-07: C6 invariant task breaks low (16/18); medium at the 18/18 ceiling; warning shed — fit includes proven-hard tasks"}
  }
}

max is recommended for no class: the pilot found saturation at max in every class — it never improved quality over xhigh and always cost more (no strict regression observed) — so it stays reserved for work harder than the pilot suite, never a default; it is the most expensive tier and is session-only.

Phase 4 — Dispatch each subtask at its tier

For each subtask, map its class to recommended_tier, then map the tier to the worker agent:

Dispatch with the Agent/Task tool. The tier is applied by the choice of subagent_type: the miner agent's frontmatter effort: sets the reasoning level. Do not pass an effort argument to the tool; there is none. Use the dispatch formula: state the role, the exact subtask, the inputs to re-anchor from disk, and the required output format; tell the worker to return the raw result as its final message.

Agent(subagent_type="miner-<tier>",
      description="<3-5 word label>",
      prompt="<the subtask, self-contained: what to do, which files/inputs to read from disk, the exact output format required, and 'return only the result as your final message'>")

Independent subtasks may be dispatched concurrently (one Agent call each, in a single message). Dependent subtasks wait for their inputs.

A user's explicit per-subtask effort instruction overrides the table; honor it, and note the override in your summary.

Phase 5 — Log each dispatch

After a subtask's worker returns, append one JSONL record to $DLOG. Today this log is telemetry only — the 0.2.0 refit path (effort.py calibrate) consumes graded benchmark receipts; folding this live dispatch log into the refit is roadmap. Create bench/state/ if it does not exist.

Record shape (controlled-vocabulary fields only; do NOT put raw subtask prompt text in the log, it is both an injection surface and noise):

{"ts":"<ISO-8601 UTC>","source":"effortmine","session_id":"<if known, else null>","task_class":"T3-moderate-reasoning","tier":"high","subagent_type":"miner-high","table_version":1,"accepted":null}

accepted is null unless you also ran the artifact past effort-grader (optional here; the benchmark path in /effort-bench is where grading is systematic). table_version echoes the version of the table you dispatched from, so a refit can tell default-driven dispatches from calibrated ones.

Shell-safety: never interpolate subtask text, user input, or model output into a shell command line; a stray quote or $(...) would execute. Build the record from controlled values (class, tier, and agent names are all fixed vocabularies) and append it, or write it with the Write tool to a temp file and concatenate. This mirrors the deterministic-core rule in docs/research/03-pattern-mining.md (A3).

Present the result

Report back in mission-control style: no emoji, Unicode box-drawing characters, and every completion line carries a concrete number (the visual-identity discipline the suite ships; docs/research/03-pattern-mining.md, B4). Show, per subtask, its class, the tier it ran at, and its result. Make the calibration legible; the point is that cheap parts visibly ran cheap.

┌─ effortmine ─────────────────────────────── N subtasks ─┐
│                                                         │
│  ✓ T1-mechanical    low     <one-line result>           │
│  ✓ T3-moderate      high    <one-line result>           │
│  ✓ T4-hard          xhigh   <one-line result>           │
│                                                         │
│  N/N dispatched · table v1 (fitted 2026-07-06)          │
└─────────────────────────────────────────────────────────┘

If you fell back to the embedded snapshot (the live file was unreadable), add one honest line: you dispatched from the shipped pilot-v1 snapshot — measured, but possibly older than the installed calibration.json (refresh it with /effort-bench). Either way, note that at the pilot's n the per-class confidence is low, and T4's low tier rests on tasks the misclassification check flagged too easy — prefer xhigh for genuinely hard T4 work.