18 lines
2.3 KiB
Markdown
18 lines
2.3 KiB
Markdown
## Phase 2: Claude-Native PR Review (future)
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Replace Codex CLI dependency with native Claude Code agents:
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1. **Create `codeflash-pr-review` agent** — adapts codex adversarial review prompt for Claude, with attack surface taxonomy and structured JSON output. Focused on general PR review (not optimization-specific like existing `codeflash-review` agent).
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2. **Create `/codeflash-pr-review` command** — handles scope selection (working-tree/branch/PR number), gathers git context, launches the agent. Replaces codex-companion.mjs logic with native git commands.
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3. **Add review output schema** to `agents/references/shared/review-output.schema.json`.
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4. **Create stop-review-gate hook** — uses the stop-review-gate prompt concept, still powered by Codex CLI (OpenAI models are better reviewers, Claude is better at implementing the fixes).
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## Phase 3: Game-Theoretic Strategy Selection (future)
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Formalize the game theory patterns already implicit in the plugin — currently agents discover payoffs empirically through the experiment loop; this phase adds reasoning about expected value *before* trying strategies.
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1. **Payoff matrix from history** — parse `results.tsv` across sessions to build a strategy × target-pattern payoff matrix. E.g., "container swap on dict-heavy hot path → 85% chance of ≥10% speedup". Agents consult this before choosing their first move instead of always following a fixed rotation order.
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2. **Strategy selection with priors** — domain agents use accumulated payoff data to rank strategies by expected value for the current target's profile signature, falling back to the default rotation when no history matches.
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3. **Cross-domain coalition scoring** — deep agent scores interaction pairs (memory→CPU, structure→memory, etc.) by historical compounding rates from the interaction column in results.tsv. Prioritizes targets where coalition payoff is highest.
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4. **Adaptive exploration budget** — allocate experiment budget per strategy proportional to historical success rate, with a minimum exploration floor (e.g., 20%) for untried strategies to avoid premature convergence.
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5. **Feedback loop closure** — after each session, auto-update `learnings.md` with strategy outcomes keyed by target profile signature, so future sessions start with better priors.
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