# Theagenticguy Erpaval Meta Prompt Optimizer > Contents - Skill: `tomevault-io/theagenticguy-erpaval-meta-prompt-optimizer` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/theagenticguy-erpaval-meta-prompt-optimizer` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/theagenticguy-erpaval-meta-prompt-optimizer/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/theagenticguy-erpaval-meta-prompt-optimizer --- ## Contents | File | When to load | | ----------------------------------- | -------------------------------------------------------------------------- | | `references/opus-4-7-principles.md` | Core prompting principles and the 4.7-specific behavior shifts | | `references/rubric.md` | Scoring rubric — shared with the `prompt-critic` agent | | `references/rewriting-patterns.md` | Before/after transformation patterns with worked examples | | `references/target-types.md` | Type-specific guidance (CLAUDE.md, agents, commands, Jinja2, Pydantic/Zod) | The `prompt-critic` agent in this plugin shares the same rubric and is the right tool when the user wants a scored assessment without a rewrite. # Meta-prompt optimizer Audit and rewrite prompts so they work well on Claude Opus 4.7. Every run produces two deliverables: a scored audit (what's wrong and why) and a rewritten prompt that applies the fixes. The guidance is grounded in Anthropic's official prompting best-practices doc for the Claude 4.x family, with emphasis on the Opus 4.7-specific behavior shifts (literal instruction following, adaptive thinking, effort-driven calibration, dialed-back emphasis language, positive framing). ## The three modes The user's request determines which mode to run. | Mode | Trigger | Output | | --------------------- | -------------------------------------------------------------------------------- | ----------------------------------------------------------- | | **Audit only** | "review this prompt", "critique this", "is this any good?", "score this" | Structured audit report (pass/fail per rubric item + notes) | | **Audit + rewrite** | "improve this", "rewrite for 4.7", "optimize this", "fix this prompt" | Audit report **+** revised prompt **+** changelog | | **Draft from intent** | "write a system prompt that…", "draft an agent for…", "give me a CLAUDE.md for…" | New prompt written against the rubric from the start | If the mode is ambiguous, ask once. Default to **audit + rewrite** when the user hands over an existing prompt. ## Workflow ### Step 1 — Identify the target type Different prompt types need different treatment. Pick the matching section of `references/target-types.md` and load it before proceeding: - **CLAUDE.md** (project or global memory) — 200-line auto-load ceiling, must stay terse, imports via `@path` are an option. - **Claude Code agent definition** (`.md` with YAML frontmatter) — description is the triggering mechanism, body is the system prompt. - **Slash command** (`.md` with YAML frontmatter) — arguments, bash execution, may compose with other skills. - **SKILL.md** body — description is the triggering mechanism, progressive disclosure via references. - **System prompt in production code** — usually a multi-line string or Jinja2 template. Often templated with variables. - **Pydantic v2 or Zod tool / tool-input description** — terse natural-language strings the model reads when deciding whether and how to call a tool. If you can't tell what type it is from context, ask. ### Step 2 — Read the relevant references Always load: - `references/opus-4-7-principles.md` — the principles you're grading against. - `references/rubric.md` — the scoring rubric. Load selectively: - `references/rewriting-patterns.md` — when rewriting (modes 2 and 3). - `references/target-types.md` — the section matching the detected type. ### Step 3 — Run the audit For each rubric item, mark it pass / needs work / rethink and note a concrete finding. Cite line numbers or specific phrases in the source prompt when calling out issues. Audits without evidence are not useful. The rubric has four categories: 1. **Directness and clarity** — is the ask specific, imperative, verifiable? 2. **Framing and tone** — positive statements rather than negative? Emphasis used sparingly? Contrastive phrasing avoided? 3. **Structure** — appropriate use of XML tags, ordered by stability for caching, scope stated explicitly? 4. **Type-specific fitness** — passes the checks for its target type (e.g., CLAUDE.md under 200 lines, agent description describes when to trigger). A full rubric walkthrough lives in `references/rubric.md`. Use it verbatim — do not improvise new rubric items mid-audit. ### Step 4 — Rewrite (if the mode calls for it) Apply the transformations from `references/rewriting-patterns.md`. The most common ones: 1. **Flip negatives to positives.** Say what to do, not just what to avoid. Cite source: Anthropic prompt-engineering doc, "Tell Claude what to do instead of what not to do." 2. **Dial back shouting.** Reserve `**IMPORTANT:**` / `**YOU MUST:**` for one or two high-cost rules. Opus 4.x over-triggers on aggressive language that was tuned for older models. 3. **State scope explicitly.** Opus 4.7 interprets prompts literally and won't generalize. "Apply this to every section, every file, every tool call" — not "apply this." 4. **Add motivation after non-obvious rules.** One clause of "because…" turns a rule into a principle the model can generalize from. 5. **Remove hedging.** Replace "likely / probably / maybe" with the specific finding. 6. **Cut self-evident instructions.** If a rule describes behavior the model would already produce, it's filler. Remove it. 7. **Match format to stability.** For cached prompts, put stable content first, volatile content last. 8. **Use XML tags for structure when the prompt mixes content types.** ``, ``, ``, ``. ### Step 5 — Produce the deliverable Output format depends on mode: **Audit only:** ```markdown ## Audit: [prompt name or summary] ### Scores | Category | Score | Notes | | --------------------- | --------------------------- | ------------------ | | Directness & clarity | Pass / Needs work / Rethink | [specific finding] | | Framing & tone | … | … | | Structure | … | … | | Type-specific fitness | … | … | ### Findings 1. **[Finding title]** — [what's wrong, with line reference]. Impact: [why it matters]. 2. … ### Top priorities to fix [numbered list of the 2-3 highest-leverage changes] ``` **Audit + rewrite:** Same audit section, then: ```markdown ### Rewritten prompt [the full revised prompt] ### Changelog - [Change 1] — [which rubric item it addresses] - [Change 2] — … ``` **Draft from intent:** ```markdown ## Drafted prompt: [name] [the prompt] ### Rationale - [Decision 1] — [why] - [Decision 2] — … ``` ### Step 6 — Offer to run the critic After producing the deliverable, offer to run the `prompt-critic` agent on the result for an independent second opinion. The critic uses the same rubric from an unbiased starting point — useful for checking that the rewrite actually improved the scores rather than shuffling them. Invoke it with the `Agent` tool, `subagent_type: "prompt-critic"`, passing the rewritten prompt and the prompt's target type. Don't auto-run it — ask first, because users iterating quickly may not want the extra round trip. (Note: the `prompt-critic` agent is not bundled in this plugin — it lives in the upstream `personal-plugins` repo. If unavailable, the rewrite is the deliverable.) ## Special cases ### Jinja2 templates Audit the template skeleton and the rendered output separately. Silent invalidators (per-request timestamps, user IDs, unsorted dict serialization) inside the template will break prompt caching. Flag them. See `references/target-types.md` → "Jinja2 templates". ### Pydantic v2 / Zod tool descriptions These are terse by nature. The rubric applies differently — short is good here, but the description must answer three questions: what does this tool do, when should the model call it, what do the parameters mean. See `references/target-types.md` → "Tool descriptions". ### Migration tasks If the user is migrating a prompt from an older model (e.g., Sonnet 3.7 → Opus 4.7), the rewrite should remove obsolete parameter guidance (sampling parameters, `budget_tokens`), dial back the shouting that older models needed, and flip negative framings. Note the migration explicitly in the changelog. ## Anti-patterns to avoid in your own output This skill is a prompt-optimizer — its output is held to the same standard it grades against. - Don't pad audits with "Based on our analysis…" or "Overall the prompt has strengths and weaknesses." Lead with the finding. - Don't hedge scores. If a rubric item fails, say it fails. "Mostly passes" is not useful. - Don't rewrite for the sake of rewriting. If a section passes the rubric, leave it alone and say so in the changelog. - Don't invent rubric items that aren't in `references/rubric.md`. The rubric is the contract. --- > Source: [theagenticguy/erpaval](https://github.com/theagenticguy/erpaval) — distributed by [TomeVault](https://tomevault.io).