naga-match

name	naga-match
description	Generates a target artifact whose N4 cosine fidelity to the source fingerprint clears the per-(pattern-class, target-domain) p10 threshold from the N5 posterior. Loop: emit chunk, score, rewrite below threshold (cap 2 attempts), drop on third. Use when the user runs /naga:match or asks to generate a new artifact in the same shape as an existing one. Do not use for fingerprint-only reports (see /naga:fingerprint) or for cross-repo work (see /naga:match-across).
model	sonnet
tools	["Read","Write","Edit","Grep","Glob"]

naga-match

Preconditions

• The source has been fingerprinted (plugins/naga-observe/state/patterns/<hash>.json exists). If not, the skill auto-runs /naga:observe <source> first.
• The N5 posterior at plugins/naga-learning/state/posterior.json is readable. Cold-start default: p10 = 0.6.

Inputs

• Slash command: /naga:match <source> <target>
• Arguments:
  • source — path to the source artifact (must already be fingerprinted, or auto-fingerprinted here).
  • target — path where the new artifact will be written.

Steps

1. Resolve the source fingerprint. If missing, invoke /naga:observe <source> and continue.
2. Read the N5 posterior; pick the (pattern_class, target_domain) p10 threshold. Mark cold_start: true when N=0.
3. Spawn the naga-shaper Sonnet agent with {source_fingerprint, threshold, target_path} and a structured-return clause.
4. The shaper emits chunk-by-chunk; each chunk is scored against the source vector via N4 cosine; chunks below threshold get one rewrite, then are dropped.
5. Aggregate chunk-level cosines into (score, ci_low, ci_high, N) via bootstrap_ci.
6. If source/target domains diverge (e.g., .py source -> .md target), the shaper escalates to naga-orchestrator Opus for the relaxation set; the shaper re-runs with the relaxed feature subset.
7. Write the target artifact via Write.
8. Publish naga.artifact.generated with {source_path, target_path, fidelity_score, ci_low, ci_high, N}.

Outputs

• The generated artifact at <target>.
• One naga.artifact.generated event on the bus.
• Console: (score, ci_low, ci_high, N, chunks_dropped).

Handoff

If the developer wants an independent fidelity check, they run /naga:validate <target> <source> — the validator re-scores cold against the same fingerprint.

Failure modes

• F11 reward hacking — never weaken the N5 threshold to pass a chunk. The threshold is read-only here; only naga-learning writes it.
• F12 degeneration loop — same chunk rewrite loops below threshold. Hard cap: 2 attempts per chunk; drop on the third.
• F04 task drift — extending into prompt-engineering. Naga replicates; that's Wixie's surface.