agent-instruction-forge
Agent Instruction Forge
Core thesis: Exceptional agent instructions encode specific implicit knowledge, not generic advice. Generic rules ("write clean code") hurt performance (ETH Zurich 2024: LLM-generated context files reduce success ~3%). What works: the non-obvious knowledge every team member carries but no file says.
Modes (detected automatically in Phase 1):
- Greenfield: No instruction files exist. Read codebase, extract knowledge, synthesize rules.
- Augment: Instruction files exist. Audit quality/coverage, validate against code, fill gaps, strengthen weak rules.
- Interview-Only: No codebase access. Skip Phase 1 Steps 2-4. Lean on Failure Round and Resource Ingestion. Flag: "Can't validate rules against code — file paths need manual verification."
Seven Properties of a Great Rule
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Specific & falsifiable — agent can verify compliance. Unfailable = not a rule.
"Write clean code"→"Every external API call must return Result<T, AppError>, never throw." -
Encodes WHY — without rationale, agents optimize around rules.
"Don't use console.log"→"Use src/lib/logger.ts — console.log bypasses Datadog correlation IDs."
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