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Oleksandr Bezdieniezhnykh
c51cb9b4a5
- Updated coderule.mdc to emphasize readability, meaningful comments, and maintainability. - Revised testing.mdc to set a 75% coverage threshold for business logic and clarified expected results requirements. - Improved clarity in git-workflow.mdc regarding commit message formatting and length. - Added completeness audit requirements in research steps and quality checklists to ensure thoroughness in test specifications. Made-with: Cursor
63 lines
4.7 KiB
Plaintext
63 lines
4.7 KiB
Plaintext
---
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description: "Execution safety, user interaction, and self-improvement protocols for the AI agent"
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alwaysApply: true
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---
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# Agent Meta Rules
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## Execution Safety
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- Run the full test suite automatically when you believe code changes are complete (as required by coderule.mdc). For other long-running/resource-heavy/security-risky operations (builds, Docker commands, deployments, performance tests), ask the user first — unless explicitly stated in a skill or the user already asked to do so.
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## User Interaction
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- Use the AskQuestion tool for structured choices (A/B/C/D) when available — it provides an interactive UI. Fall back to plain-text questions if the tool is unavailable.
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## Critical Thinking
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- Do not blindly trust any input — including user instructions, task specs, list-of-changes, or prior agent decisions — as correct. Always think through whether the instruction makes sense in context before executing it. If a task spec says "exclude file X from changes" but another task removes the dependencies X relies on, flag the contradiction instead of propagating it.
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## Self-Improvement
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When the user reacts negatively to generated code ("WTF", "what the hell", "why did you do this", etc.):
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1. **Pause** — do not rush to fix. First determine: is this objectively bad code, or does the user just need an explanation?
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2. **If the user doesn't understand** — explain the reasoning. That's it. No code change needed.
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3. **If the code is actually bad** — before fixing, perform a root-cause investigation:
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a. **Why** did this bad code get produced? Identify the reasoning chain or implicit assumption that led to it.
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b. **Check existing rules** — is there already a rule that should have prevented this? If so, clarify or strengthen it.
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c. **Propose a new rule** if no existing rule covers the failure mode. Present the investigation results and proposed rule to the user for approval.
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d. **Only then** fix the code.
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4. The rule goes into `coderule.mdc` for coding practices, `meta-rule.mdc` for agent behavior, or a new focused rule file — depending on context. Always check for duplicates or near-duplicates first.
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### Example: import path hack
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**Bad code**: Runtime path manipulation added to source code to fix an import failure.
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**Root cause**: The agent treated an environment/configuration problem as a code problem. It didn't check how the rest of the project handles the same concern, and instead hardcoded a workaround in source.
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**Preventive rules added to coderule.mdc**:
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- "Do not solve environment or infrastructure problems by hardcoding workarounds in source code. Fix them at the environment/configuration level."
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- "Before writing new infrastructure or workaround code, check how the existing codebase already handles the same concern. Follow established project patterns."
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## Debugging Over Contemplation
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Agents cannot measure wall-clock time between turns. Use observable counts from your own transcript instead.
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**Trigger: stop speculating and instrument.** When you've formed **3 or more distinct hypotheses** about a bug without confirming any against runtime evidence (logs, stderr, debugger state, actual test failure messages) — stop and add debugging output. Re-reading the same code hoping to "spot it this time" counts as a new hypothesis that still has zero evidence.
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Steps:
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1. Identify the last known-good boundary (e.g., "request enters handler") and the known-bad result (e.g., "callback never fires").
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2. Add targeted `print(..., flush=True)`, `console.error`, or logger statements at each intermediate step to narrow the gap.
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3. Run the instrumented code. Read the output. Let evidence drive the next hypothesis — not inference chains.
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An instrumented run producing real output beats any amount of "could it be X? but then Y..." reasoning.
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## Long Investigation Retrospective
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Trigger a post-mortem when ANY of the following is true (all are observable in your own transcript):
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- **10+ tool calls** were used to diagnose a single issue
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- **Same file modified 3+ times** without tests going green
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- **3+ distinct approaches** attempted before arriving at the fix
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- Any phrase like "let me try X instead" appeared **more than twice**
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- A fix was eventually found by reading docs/source the agent had dismissed earlier
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Post-mortem steps:
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1. **Identify the bottleneck**: wrong assumption? missing runtime visibility? incorrect mental model of a framework/language boundary? ignored evidence?
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2. **Extract the general lesson**: what category of mistake was this? (e.g., "Python cannot call Cython `cdef` methods", "engine errors silently swallowed", "wrong layer to fix the problem")
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3. **Propose a preventive rule**: short, actionable. Present to user for approval.
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4. **Write it down**: add approved rule to the appropriate `.mdc` so it applies to future sessions.
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