Self Eval

# Self-Eval: Honest Work Evaluation ultrathink **Tier:** STANDARD **Category:** Engineering / Quality **Dependencies:** None (prompt-only, no external tools required) ## Description Self-eval is a Claude Code skill that produces honest, calibrated work evaluations. It replaces the default AI tendency to rate everything 4/5 with a structured two-axis scoring system, mandatory devil's advocate reasoning, and cross-session anti-inflation detection. The core insight: AI self-assessment converges to "everything is a 4" because a single-axis score conflates task difficulty with execution quality. Self-eval separates these axes, then combines them via a fixed matrix that the model cannot override. ## Features - **Two-axis scoring** — Independently rates task ambition (Low/Medium/High) and execution quality (Poor/Adequate/Strong), then combines via a lookup matrix - **Mandatory devil's advocate** — Before finalizing, must argue for both higher AND lower scores, then resolve the tension - **Score persistence** — Appends scores to `.self-eval-scores.jsonl` in the working directory, building history across sessions - **Anti-inflation detection** — Reads past scores and flags clustering (4+ of last 5 identical) - **Matrix-locked scoring** — The composite score comes from the matrix, not from direct selection. Low ambition caps at 2/5 regardless of execution quality ## Usage After completing work in a Claude Code session: ``` /self-eval ``` With context about what to evaluate: ``` /self-eval the authentication refactor we just completed ``` The skill reviews the conversation history, identifies what was accomplished, scores it on both axes, runs the devil's advocate check, and appends the result to the score history file. ## Examples ### Example 1: Feature Implementation ``` /self-eval added pagination to the user list API ``` Output: ``` ## Self-Evaluation **Task:** Added cursor-based pagination to the user list API endpoint **Ambition:** Medium — New feature with established patterns, but required careful backwards compatibility **Execution:** Strong — All tests pass, handles edge cases, existing clients unaffected **Devil's Advocate:** - Lower: Pagination is well-documented. The pattern was copied from another endpoint with minor changes. Could argue Low ambition. - Higher: The backwards-compat requirement made this harder than a greenfield implementation. Had to support both offset and cursor modes. - Resolution: Medium ambition holds — the backwards-compat constraint added real complexity beyond copy-paste. **Score: 4** — Solid execution on meaningful work with real constraints. ``` ### Example 2: Bug Fix ``` /self-eval ``` Output: ``` ## Self-Evaluation **Task:** Fixed race condition in the checkout flow causing duplicate orders **Ambition:** Medium — Required debugging across 3 services to find the root cause **Execution:** Adequate — Fix works but the test only covers the exact scenario reported, not the general class of race conditions **Devil's Advocate:** - Lower: The fix was a single line (adding a mutex). Once found, it was trivial. - Higher: Finding the root cause across 3 services took real investigation. The mutex placement required understanding the full flow. - Resolution: Medium ambition is correct for the investigation, but execution drops to Adequate — a more thorough fix would address the pattern, not just the instance. **Score: 3** — Good debugging work but the fix is narrow. ``` --- ## What to Evaluate $ARGUMENTS If no arguments provided, review the full conversation history to identify what was accomplished this session. Summarize the work in one sentence before scoring. ## How to Score — Two-Axis Model Score on two independent axes, then combine using the matrix. Do NOT pick a number first and rationalize it — rate each axis separately, then read the matrix. ### Axis 1: Task Ambition (what was attempted) Rate the difficulty and risk of what was worked on. NOT how well it was done. - **Low (1)** — Safe, familiar, routine. No real risk of failure. Examples: minor config changes, simple refactors, copy-paste with small modifications, tasks you were confident you'd complete before starting. - **Medium (2)** — Meaningful work with novelty or challenge. Partial failure was possible. Examples: new feature implementation, integrating an unfamiliar API, architectural changes, debugging a tricky issue. - **High (3)** — Ambitious, unfamiliar, or high-stakes. Real risk of complete failure. Examples: building something from scratch in an unfamiliar domain, complex system redesign, performance-critical optimization, shipping to production under pressure. **Self-check:** If you were confident of success before starting, ambition is Low or Medium, not High. ### Axis 2: Execution Quality (how well it was done) Rate the quality of the actual output, independent of how ambitious the task was. - **Poor (1)** — Major failures, incomplete, wrong output, or abandoned mid-task. The deliverable doesn't meet its own stated criteria. - **Adequate (2)** — Completed but with gaps, shortcuts, or missing rigor. Did the thing but left obvious improvements on the table. - **Strong (3)** — Well-executed, thorough, quality output. No obvious improvements left undone given the scope. ### Composite Score Matrix | | Poor Exec (1) | Adequate Exec (2) | Strong Exec (3) | |------------------------|:---:|:---:|:---:| | **Low Ambition (1)** | 1 | 2 | 2 | | **Medium Ambition (2)**| 2 | 3 | 4 | | **High Ambition (3)** | 2 | 4 | 5 | **Read the matrix, don't override it.** The composite is your score. The devil's advocate below can cause you to re-rate an axis — but you cannot directly override the matrix result. Key properties: - Low ambition caps at 2. Safe work done perfectly is still safe work. - A 5 requires BOTH high ambition AND strong execution. It should be rare. - High ambition + poor execution = 2. Bold failure hurts. - The most common honest score for solid work is 3 (medium ambition, adequate execution). ## Devil's Advocate (MANDATORY) Before writing your final score, you MUST write all three of these: 1. **Case for LOWER:** Why might this work deserve a lower score? What was easy, what was avoided, what was less ambitious than it appears? Would a skeptical reviewer agree with your axis ratings? 2. **Case for HIGHER:** Why might this work deserve a higher score? What was genuinely challenging, surprising, or exceeded the original plan? 3. **Resolution:** If either case reveals you mis-rated an axis, re-rate it and recompute the matrix result. Then state your final score with a 1-2 sentence justification that addresses at least one point from each case. If your devil's advocate is less than 3 sentences total, you're not engaging with it — try harder. ## Anti-Inflation Check Check for a score history file at `.self-eval-scores.jsonl` in the current working directory. If the file exists, read it and check the last 5 scores. If 4+ of the last 5 are the same number, flag it: > **Warning: Score clustering detected.** Last 5 scores: [list]. Consider whether you're anchoring to a default. If the file doesn't exist, ask yourself: "Would an outside observer rate this the same way I am?" ## Score Persistence After presenting your evaluation, append one line to `.self-eval-scores.jsonl` in the current working directory: ```json {"date":"YYYY-MM-DD","score":N,"ambition":"Low|Medium|High","execution":"Poor|Adequate|Strong","task":"1-sentence summary"} ``` This enables the anti-inflation check to work across sessions. If the file doesn't exist, create it. ## Output Format Present your evaluation as: ## Self-Evaluation **Task:** [1-sentence summary of what was attempted] **Ambition:** [Low/Medium/High] — [1-sentence justification] **Execution:** [Poor/Adequate/Strong] — [1-sentence justification] **Devil's Advocate:** - Lower: [why it might deserve less] - Higher: [why it might deserve more] - Resolution: [final reasoning] **Score: [1-5]** — [1-sentence final justification]