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ADR 0001: nvim-treesitter highlights.scm as capture source ADR 0002: language-agnostic method extraction via child_by_field_name ADR 0003: method-level n-gram clustering before inference ADR 0004: frequency filter with min_coverage threshold ADR 0005: import extraction per cluster ADR 0006: argument pattern extraction via AST node classification ADR 0007: JSON output for LLM prompt injection ADR 0008: BEX ensemble for grammar inference
63 lines
2.2 KiB
Markdown
63 lines
2.2 KiB
Markdown
# 7. JSON output for LLM prompt injection
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**Date:** 2026-07-03
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**Status:** Accepted
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## Context
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The text table output is human-readable but not directly usable by an LLM. To use Dervish conventions in another agent or coding session, the output must be parsed, reformatted, and injected into a prompt — an extra friction step.
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An LLM consuming conventions needs:
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- Structured data it can read directly (no parsing).
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- All metadata per convention (grammar, imports, args, files, packages).
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- Compact enough to fit in context without overflow.
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## Decision
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Add a `--json` flag that outputs a structured JSON array instead of the text table.
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JSON structure:
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```json
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[{
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"language": ".kt",
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"conventions": [{
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"label": "every → assertEquals → verify",
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"method_count": 16,
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"algorithm": "CRX",
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"grammar": "every+.assertEquals.verify+.any?",
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"mdl_score": 8.64,
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"imports": ["import io.mockk.every", "..."],
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"packages": ["eu/corentic/springrag/agent/capability"],
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"arg_patterns": {
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"assertEquals": {
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"occurrences": 42,
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"arg_count": {"min": 2, "max": 3, "common": 2},
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"patterns": [{"count": 30, "args": 2, "types": ["lit", "var"]}]
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}
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}
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}],
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"total_methods": 665
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}]
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```
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Also accepts `--format json` and `--format text` for explicit control.
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## Consequences
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**Positive:**
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- LLM consumes the JSON directly — no parsing step needed.
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- All metadata in one object per convention — imports, args, files, packages all together.
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- `--json` is a single flag — the default text output remains for human review.
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**Negative:**
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- JSON is more verbose than text (full import list instead of truncated preview).
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- No easy way to limit output size — a large codebase produces JSON that may overflow context.
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- Mitigation: `--include` flag filters files before analysis.
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## Alternatives Considered
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- **YAML output**: More readable, but less universally parseable by LLMs.
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- **CSV output**: Too flat for nested data (arg_patterns, imports list).
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- **Custom prompt template**: Would need per-framework templates. JSON is framework-agnostic.
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- **No structured output**: User must pipe through `jq` or manual reformatting. Bad UX.
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