Produces publishable markdown benchmark reports from eval results, modeled on the structure and tone of frontier AI lab reports (Anthropic, Cursor, FrontierSWE, BaxBench). Use this when the user wants to write up benchmark findings, turn eval results into a blog post or technical report, publish a model/agent comparison, announce a new eval, or share benchmark data publicly. Triggers on phrases like "write a benchmark report", "turn these results into a post", "publish our evals", "draft a writeup of the benchmark", "create a model comparison report", "generate a benchmark blog post". Use this skill even if the user just says "write this up" or "make a report" in the context of benchmark/eval results. Do NOT use for internal changelog entries, release notes, PR descriptions, or academic paper drafts.
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name
benchmark-report-writer
description
Produces publishable markdown benchmark reports from eval results, modeled on the structure and tone of frontier AI lab reports (Anthropic, Cursor, FrontierSWE, BaxBench). Use this when the user wants to write up benchmark findings, turn eval results into a blog post or technical report, publish a model/agent comparison, announce a new eval, or share benchmark data publicly. Triggers on phrases like "write a benchmark report", "turn these results into a post", "publish our evals", "draft a writeup of the benchmark", "create a model comparison report", "generate a benchmark blog post". Use this skill even if the user just says "write this up" or "make a report" in the context of benchmark/eval results. Do NOT use for internal changelog entries, release notes, PR descriptions, or academic paper drafts.
license
MIT
compatibility
Requires read access to the benchmark result files (typically JSONL) and optionally a benchmark guide document. Output is pure markdown with mermaid-rendered charts — no external rendering dependencies. `jq` is recommended for reliably aggregating JSONL results.
metadata
{"author":"Liran Tal","version":"1.0.0"}
Benchmark Report Writer
Instructions
Produce a publishable markdown benchmark report from eval results and, when available, a benchmark guide. The report should match what a reader expects from frontier AI lab writeups: a short summary that sets honest expectations, a methodology section that earns the reader's trust, a results section anchored by a leaderboard and charts, a qualitative analysis section that explains , and a limitations section that doesn't flinch.
what the numbers mean
The goal is a report that a reader can skim in two minutes or read thoroughly in twenty, and that stands up to scrutiny because every claim is traceable to the data.
Inputs
Required: one or more result files (JSONL with one record per run, JSON, or CSV) with per-run scores and metrics.
Recommended: a benchmark guide document (e.g. docs/benchmark.md) describing goals, methodology, and task design. If it's missing, ask the user for the missing context rather than inventing it.
Optional: an existing draft or a prior report to build on, and a target style ("Anthropic announcement", "FrontierSWE deep-dive", "Cursor blog", etc.).
Reference files
Read these on demand — don't dump them all into context at the start.
references/report-structure.md — the full section-by-section template, including which sections to keep or drop based on data shape. Read before Step 3.
references/voice-and-style.md — tone rules, writing patterns, phrases to avoid. Read before Step 4.
references/visualizations.md — which chart type fits which metric, with mermaid snippets. Read when planning or drafting charts.
references/example-sections.md — short before/after examples of Summary, Results, and Qualitative Analysis prose. Read when stuck on phrasing.
assets/report-template.md — skeleton markdown to copy and fill in.
Workflow
Follow these steps in order. Do not skip verification — reports that invent numbers are worse than no report at all.
Step 1: Gather the inputs and the audience
Locate the result files (ask the user if not provided). Check results/ directories and the user's message for attached paths.
Locate the benchmark guide if one exists (common paths: docs/benchmark.md, README.md, BENCHMARK.md). If none is available, ask the user for the five-sentence version: what the benchmark measures, how scores are computed, what varies across runs, how many runs were aggregated, and what the known limits are.
Ask the user who the report is for — public blog post, internal writeup, paper submission — unless it's obvious from context. The target shapes both tone and depth. When in doubt, default to "public blog post for a technical audience".
Ask whether there is a preferred house style (e.g. "like the Anthropic Claude 3.7 post", "like FrontierSWE"). If not specified, use the default hybrid described in references/report-structure.md.
Done when: you have a results source, a methodology source (guide or user-provided summary), and a clear target audience and style.
Step 2: Extract findings from the data
Load the results programmatically. Prefer jq on JSONL or a small script over eyeballing the file — you need exact numbers, and exact numbers are the whole point.
Compute at minimum, for each task × config cell: the score, total tokens (summing all token fields), wall time, and turn count. Identify the best score per task, the most efficient config (best score-per-token), and any outliers.
Scan for patterns that could become qualitative observations: does quality correlate with cost, or diverge from it? Do different configs fail on the same tasks? Does one tool dominate the tool-use breakdown? Is the score spread wide or tight? Each pattern is a candidate section in the Qualitative Analysis.
Write every headline number into a scratchpad (inline in your response is fine). Every number that ends up in the report must be traceable to this scratchpad — never let a number appear in the draft without a source.
Done when: you have a structured summary of findings with exact numbers and 2–4 candidate themes for qualitative analysis.
Step 3: Draft the outline
Read references/report-structure.md for the template and variant guidance.
Map findings onto sections. Drop any section that would be padding for the data at hand — a three-config run doesn't need a dedicated "Methodology Comparison" section, for example.
Decide which charts go where using references/visualizations.md. Each chart must earn its place — a chart that repeats what the leaderboard table already shows is noise.
Write a one-line intent per section (what the reader should take away). If you can't state an intent, the section shouldn't exist.
Done when: you have a section-by-section outline with a one-line intent per section and a chart list.
Step 4: Write the report
Read references/voice-and-style.md before drafting the first sentence.
Start with the Summary and the Introduction. These set the frame and are hardest to change once the body is written.
Draft each section at the outline's intent — no more. A short section that lands beats a long one that meanders.
For charts, use mermaid where it fits (xychart-beta for bars/lines, pie, flowchart, quadrantChart). Where the data needs a chart type mermaid can't render cleanly, include a markdown table instead and mark it <!-- TODO: render as chart --> so the user knows to generate an image.
Copy diagrams from the benchmark guide verbatim if they already exist in mermaid form (the pipeline flowchart is usually the best one to lift). Don't redraw what's already clear.
Write the Limitations section honestly — every benchmark has caveats, and hiding them undermines credibility. See the examples in references/example-sections.md for calibration.
Done when: every planned section is drafted, every chart is either rendered or placeholder-marked, and the report reads as one voice rather than a patchwork.
Step 5: Verify every claim
For each specific number or comparison in the report, trace it back to the scratchpad from Step 2. Any number without a source is a bug — fix it or cut the claim.
Check that the Summary's headline claim actually follows from the Results section. A reader who only reads the Summary should leave with an accurate picture, not an oversold one.
Check that every named model, config, and task is spelled consistently throughout.
Run one final pass for voice: imperative where appropriate, no filler, no unsupported superlatives, no "revolutionary" or "groundbreaking" language.
Spot-check chart data against the scratchpad — a typo in a chart is the easiest way to mislead a reader.
Done when: every claim has a source, the Summary matches the body, names are consistent, and the voice reads cleanly.
Step 6: Deliver
Write the report to the path the user specifies, or default to reports/benchmark-<YYYY-MM-DD>.md.
Summarize for the user: what's in the report, what you deliberately omitted and why, which charts are mermaid vs. placeholders for externally-rendered images, and what the user should review before publishing (named partners, claims about competitors, unverified numbers).
Done when: the file is written and the user has a short list of pre-publication review items.
Examples
User says: "We just finished a benchmark run — can you write this up as a blog post?"
Actions:
Find the most recent results/benchmark-*.jsonl file and read it.
Locate docs/benchmark.md (if present) and skim for methodology and the pipeline diagram.
Ask the target audience (default: public, technical).
Work through Steps 2–6: extract exact numbers with jq, draft a ~1,500-word report with a leaderboard table, a score-per-config bar chart (mermaid), and 2–3 qualitative observations.
Save to reports/benchmark-<date>.md and summarize what's in it and what the user should double-check.
Result: A publishable markdown file the user can paste into their blog platform or push to a docs repo.
User says: "Here's our benchmark guide and the latest JSONL file. Draft the writeup."
Actions:
Read the guide to understand methodology; use it directly to write the Benchmark Design section rather than re-describing from scratch.
Load the JSONL and extract aggregates programmatically.
Write sections in order; lift the guide's pipeline flowchart inline (as mermaid) if one exists.
Deliver the report and flag any gaps where the guide didn't supply enough context.
Result: A report that faithfully represents the documented benchmark and the actual results, with gaps called out for the user rather than papered over.
User says: "Turn these eval results into a report in the style of the Anthropic Claude 3.7 Sonnet announcement."
Actions:
Recognize the request for Anthropic-style framing: outcome-forward summary, bar-chart-heavy results, methodology pushed to an appendix, confident-but-measured tone.
In Step 3, consult the "Announcement" variant in references/report-structure.md and follow it.
Draft with methodology in the appendix rather than the body; emphasize headline benchmark numbers and a single punchy claim in the opening.
Result: A report in the requested house style, grounded entirely in the user's real data.
User says: "Write this up but we don't have a benchmark guide, just the JSONL."
Actions:
Ask the user for a five-sentence summary of the benchmark (what's measured, how it's scored, what varies, number of runs, known limits).
Inspect the JSONL to infer what you can — task IDs, config IDs, metric fields — and confirm your inferences with the user ("I see three configs named X/Y/Z, each run against two tasks — is that correct?").
Proceed with Steps 2–6, using the user's five-sentence summary as the source for the Benchmark Design section and flagging any methodology detail you had to leave vague.
Result: A report that is appropriately hedged where methodology context was thin, rather than one that confidently makes things up.
Troubleshooting
Error: "I don't have enough context to write the Benchmark Design section."
Cause: No benchmark guide was provided and the user didn't describe the methodology.
Solution: Ask the user for the five-sentence version (what's measured, how it's scored, what varies, how many runs, known limits). Do not invent methodology — a vague design section is better than a confident wrong one.
Error: "The Summary's headline claim isn't supported by the Results."
Cause: The Summary was drafted from vibes rather than from the Step 2 scratchpad.
Solution: Rewrite the Summary using only claims that trace to the scratchpad. If no headline claim is strong enough, say so plainly ("No single config dominated across all tasks") rather than over-selling.
Error: "A chart renders as raw code in the user's markdown viewer."
Cause: Used a mermaid chart type the viewer doesn't support, or the target platform doesn't render mermaid at all.
Solution: Fall back to a markdown table for the data and include a <!-- TODO: render as chart --> comment so the user knows to generate an image before publishing.
Error: "The report reads like a pitch deck."
Cause: Over-reliance on superlatives, marketing language, or unsupported claims.
Solution: Re-read references/voice-and-style.md, strip every "revolutionary", "state-of-the-art", and "groundbreaking" unless it's supported by a cited number, and reframe claims as measurements ("scored X on Y") rather than adjectives ("best-in-class at Y").
Error: "The report is 5,000 words and the user asked for a blog post."
Cause: Every section was written at maximum depth; no editing pass.
Solution: Cut the Appendix to essentials, trim the Qualitative Analysis to the 2–3 strongest observations, and aim for 1,200–2,000 words total for a blog post. Full-depth technical reports belong on a different track.
