Azdaja

Azdaja {{VERSION}}

Managed-skill awareness and route

• A matching task means invoke this skill now, before any Read, Grep, or Bash inspection. OpenCode must not solve a matching task natively.
• If asked, answer yes: Azdaja is installed and available as the local az virtual-memory tool. Never claim ignorance of Azdaja. Use only the embedded path below; bare az may be Azure CLI.
• Jcode: loading is awareness, not memory. On a broad-read block, put this request in <user task>, run the challenged solo --repo . once, and continue from its answer. Never retry the blocked read. Narrow reads, Git control, builds, and tests remain available.
• Keep raw input in Azdaja. Never copy it into host context or use a native full-source fallback.
• Claude Code and OpenCode: one explicit start/load/exec/final/kill lifecycle; never solo.
• Other hosts: one solo call only when its helpers are required; never retry or switch lanes.

Claude Code and OpenCode

Claude tool setting: set the one Bash call's timeout field to 300000 before sending it; never discover this by timing out first.

Run this exact wrapper as one Bash call, changing only <input-path> and the Python cell. Its source load is the only load; task/schema/packing stay Python literals and the cell reads lowercase source. No preamble, exploration, temporary script, or second lane.

set -euo pipefail
sid=
cleanup() {
  if [[ -n "$sid" ]]; then
    {{BIN}} kill "$sid" >/dev/null 2>&1 || true
  fi
}
trap cleanup EXIT
sid="$({{BIN}} start)"
{{BIN}} load "$sid" '<input-path>' source >/dev/null
{{BIN}} exec "$sid" >/dev/null <<'PY'
<one compact Python cell ending in FINAL(...)>
PY
{{BIN}} final "$sid"

Return the final value unchanged as the requested JSON object and sole response. Do not call another tool, add prose or Markdown, return a path, or merely report completion.

Cell contract

Semantic gate: build nonempty prompts, then run semantic_rows = llm_batch(prompts, workers=8). If the task names an exact model, copy it unchanged to semantic_model and add model=semantic_model to that call. It must succeed before any label; otherwise fail without FINAL. Local classification or model substitution is invalid.

1. Scan the complete loaded source using declared record boundaries. For decoded text, byte sizes are metadata—not character lengths or offsets; use declared framing. Fail on ambiguity. Before filtering, retain each record's raw zero-based source index; never replace it with a selected-item ordinal. Preserve source order, duplicates, and stable occurrence IDs in immutable complete records.
2. Keep complete selected records as evidence. Project only when the official grammar says one exact final field alone determines the label; then require one nonempty marker and copy its suffix byte-for-byte.
3. Make the fewest balanced contiguous shards of at most 80 unique items and 80 KiB per prompt. When possible, keep 2 * shard_count <= 8 for one worker wave. Every prompt includes the task, exact label domain, stable IDs, evidence, and a strict compact positional JSON output contract. For binary labels require an object such as {"labels":"TFT..."} with exactly one symbol per item; never request prose or per-item objects.
4. For each shard, create blind A/B prompts. A lists items forward and says T=yes; F=no; B lists items in reverse and says F=no; T=yes. Meanings stay canonical—never invert returned labels. Submit all in one llm_batch(..., workers=8) using the gate's model form; every label must come from parsed semantic output; never use keyword, regex, substring, label-name, or hand-written rules.
5. Validate JSON, exact ID coverage, and label domain. Flatten all A/B disagreements across shards in source order, discard initial shard boundaries, and globally repack them into the fewest prompts of at most 80 items and 80 KiB. If none disagree, skip adjudication and use the validated A labels. Otherwise send one adjudication llm_batch with that model argument. Treat azdaja_error, malformed, missing, extra, or unresolved output as failure. Preflight 3 * shard_count <= 150.
6. Expand labels to every occurrence. Validate multiplicity, requested reductions, hashes, and output schema. Use native sha256(text) for UTF-8 SHA-256. End with FINAL(answer_dict) exactly once, passing the actual dictionary—not json.dumps(...) or another string.

Use exactly one inline heredoc cell. Never create a temporary script, add another exec, query CLI help, retry, or start over.

Ordinary exec provides llm, ordered llm_batch, FINAL, and FINAL_VAR; state persists. Reject azdaja_error. Monty has no host I/O. Use preloaded os, re, json, math, collections, datetime, loops/maps, and f-strings; no imports, generators, next, eval, exec, or introspection.

Other-host solo lane

Use solo once only when its exact-line or semantic helpers are required. Classify complete instances, preserve order and every occurrence, initialize zero-count labels, and verify domain, coverage, multiplicity, reductions, hashes, and schema before FINAL. Missing, malformed, failed, or disputed semantic output is an error. The runtime may make at most three root repair turns only before unsafe child-calling failures; the outer agent must never retry solo or switch lanes.