Anthropic shipped Claude Opus 4.7 on April 16, 2026 — their most capable generally available model. It's not a faster Sonnet. It's a different tool.
Opus 4.7 earns its price when the job has three shapes: deep reasoning, long context (150k+ tokens), or tool use with verification. For anything shorter than that, Sonnet is cheaper and usually ties.
I spent a week running it on real work — code review, legal analysis, post-mortems, agent harnesses. Below are 4 of the 14 prompts I ended up keeping. The full 14-template guide includes RFC drafting, migration planning, meeting transcript analysis, and a competitor teardown template.
When Opus 4.7 is worth it (and when it isn't)
| Job | Pick | Why |
|---|---|---|
| Deep reasoning, long context, tool use | Claude Opus 4.7 | Strongest on SWE-bench; holds 200k ctx well |
| Bulk writing, tight prose under 4k tokens | Claude 4.5 Sonnet | 80% of Opus quality at a fraction of cost |
| Multimodal with fresh web grounding | Gemini 3.1 Ultra/Pro | Native browsing + current SERP |
| Voice, fastest drafts, huge ecosystem | GPT-5.4 Pro | Tool and plugin density |
| Cost-sensitive batch work | DeepSeek V3.1 / Llama 3.3 70B | 5–20× cheaper per token |
Rule of thumb: if the prompt fits on one screen and the output is under 800 words, Sonnet first. Opus only when Sonnet visibly disappoints.
The universal Opus 4.7 prompt formula
Opus 4.7 rewards structured prompts more than any previous Claude model. Five slots:
[ROLE]: who the model is writing as (reviewer, counsel, architect)
[CONTEXT]: the domain, stage, constraints
[INPUTS]: every file, transcript, doc pasted — label each one
[TASK]: the deliverable shape (table, ranked list, one-pager)
[VERIFICATION]: how the model should check its own work before finalizing
The last slot is what separates Opus 4.7 prompts from Sonnet ones. Ask Opus to verify, cite, flag uncertainty, and push back. It will. Sonnet tends to comply; Opus tends to correct.
Run any messy draft through our free prompt enhancer first — it'll tighten the structure so Opus tokens aren't wasted.
1. Long-document synthesizer (100+ pages)
Use when: You have a 10-K, white paper, or RFP and you need fidelity, not highlights.
You are a senior research analyst. I am pasting a long document (100+ pages).
Synthesize it for a founder audience.
Document type: [10-K / white paper / industry report / RFP / court filing]
My role: [FOUNDER / PM / COUNSEL / INVESTOR]
What I must walk away with: [DECISION / SUMMARY / RISK MAP]
Document:
"[PASTE FULL TEXT]"
Output:
1. Thesis in ONE sentence (the author's, not mine)
2. 5 claims the author actually makes, with page/section cites
3. 3 claims the author IMPLIES but never states — and whether the evidence holds
4. The 2 places where the document contradicts itself (cite both)
5. 3 questions this document doesn't answer that I now need to answer
6. A final "if I only remember 3 sentences from this" paragraph
Rules:
- If a cite is missing, SAY "no cite found" — do not fabricate.
- Flag any claim where your confidence is <70% with [LOW CONFIDENCE].
- If the document is thin or circular, tell me so explicitly.
Why it works: The "contradicts itself" and "implies but never states" slots force Opus to read against the text, not just summarize it. Sonnet tends to agree with documents; Opus interrogates them.
2. Code review → ranked refactor plan
Use when: Multi-file diff, tests included, stakes real.
You are a principal engineer doing a pre-merge review. Be direct.
Repo: [1-SENTENCE DESCRIPTION]
Language / framework: [X]
Change intent: [WHAT THE AUTHOR TRIED TO DO]
Stakes: [PRODUCTION / INTERNAL / EXPERIMENT]
Diff + touched files:
"[PASTE]"
Existing tests (if any):
"[PASTE]"
Output in this exact order:
1. Does this ship? (YES / NO / YES-WITH-CONDITIONS — name the conditions)
2. Top 3 bugs, ranked by blast radius
3. Top 3 refactors, ranked by ratio of (clarity gained) / (risk added)
4. Tests the author forgot — give 1-line cases
5. What you'd want to see before merging, in priority order
Rules:
- If the diff shows a change you can't reason about without more context, ASK before recommending.
- Do not suggest stylistic fixes (linters handle those).
- Flag anything that changes behavior at a distance (global state, env, feature flags).
Why it works: Opus 4.7 is measurably better at SWE-bench-class reasoning than prior tiers. The "does this ship" slot forces a decision. The ranked triplet structure stops the output from becoming a wall of text.
3. Multi-source strategic SWOT
Use when: You have 5+ inputs (reports, transcripts, competitor docs) and need positioning clarity.
You are a strategy consultant. I'm pasting 5 inputs below, labeled.
My company: [1-SENTENCE DESCRIPTION]
Decision I'm making: [X]
Time horizon: [6 months / 18 months / 3 years]
Input 1 - Our last board update: "[PASTE]"
Input 2 - Competitor homepage + pricing: "[PASTE]"
Input 3 - Customer interview transcript: "[PASTE]"
Input 4 - Industry analyst report excerpt: "[PASTE]"
Input 5 - Our win/loss notes last 6 deals: "[PASTE]"
Produce a SWOT, but with these rules:
- Each cell has AT MOST 3 items.
- Each item cites which input it's grounded in (e.g. "[Input 3]").
- Mark any item not grounded in any input as [INFERRED] and explain the inference.
- After the SWOT: ONE paragraph naming the single most important 18-month bet this implies.
- Then: ONE paragraph on what would falsify that bet.
Flag any contradictions between inputs explicitly before the SWOT.
Why it works: Generic SWOTs are useless. This version grounds every cell in cited evidence and forces the model to surface contradictions between sources — which is where Opus's long-context advantage actually shows up.
4. Agent harness with tool use + verification
Use when: Multi-step task, must check its own work.
You are an autonomous agent with access to these tools:
1. [TOOL 1 — e.g. web.search(query) -> snippets]
2. [TOOL 2 — e.g. file.read(path) -> contents]
3. [TOOL 3 — e.g. code.exec(python) -> stdout/stderr]
Goal: [1-SENTENCE GOAL]
Inputs you already have: [X, Y, Z]
Budget: [MAX TOOL CALLS / MAX MINUTES]
Rules of the harness:
- Plan BEFORE acting. Emit a PLAN block first with numbered steps.
- After each tool call, emit a VERIFY block: what did you learn, what would falsify your plan, do you still think the plan holds?
- If a tool call returns unexpected data, STOP and replan — don't push through.
- Final output is a DELIVERABLE block in the exact shape I asked for.
- If you can't complete the goal within budget, say so explicitly and return partial work with a clear "what's missing" note.
Go.
Why it works: The PLAN → ACT → VERIFY loop is where Opus 4.7's self-correction kicks in. Without explicit verification slots, agents tend to confidently ship wrong answers. With them, Opus will notice its own errors and course-correct.
The other 10 prompts (quick hit list)
The full guide covers:
- Research paper → one-pager + open questions
- Production incident post-mortem (logs + Slack + PRs → a document people trust)
- 5-competitor parallel teardown
- RFC drafter from brain-dump + precedent docs
- Framework/library migration planner (React 18→19, Rails 7→8)
- Contract / legal clause analyzer
- Anomaly hunt over a CSV/JSON dump
- Architecture review with failure modes
- Repo onboarding Q&A (docs + code aware)
- Meeting transcript → actions, owners, confidence
Each one follows the same 5-slot structure: ROLE, CONTEXT, INPUTS, TASK, VERIFICATION.
Common mistakes (5 things that waste Opus tokens)
- Using Opus when the prompt fits on one screen. If Sonnet would get 80% of the way there, run Sonnet first and upgrade only if the output is visibly thin.
- Skipping the VERIFICATION slot. Without it, Opus behaves like expensive Sonnet. The whole point of the Opus tier is the model's willingness to push back and self-check — don't silence that.
- Asking "summarize this" on a 100-page document. Opus will happily give you a generic summary. Ask for thesis, cited claims, contradictions, and open questions instead.
- Running the same prompt 3-4 times hoping for a better answer. If the first output is weak, the prompt is weak. Fix the structure, not the number of attempts.
-
Pasting messy context and hoping Opus figures it out. Label every input.
[CUSTOMER INTERVIEW]+[COMPETITOR HOMEPAGE]is cheaper than letting the model guess.
Resources
- Full 14-template guide on midastools.co — same prompts, deeper context on each
- Free prompt enhancer — paste a messy draft, get a tighter version back
- Claude Code Kit ($39) — 40+ production prompts for Claude Code CLI workflows
- AI Prompt Mega Pack ($97) — 200+ prompts across code, writing, research, marketing
Originally published at midastools.co. Working on a Claude-powered tool? Reach out — we're shipping one-off DFY prompt packs for teams with specific workflows.
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