Borrowing it
Nothing to install: this file belongs to alfonsograziano/auto-agent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/alfonsograziano/auto-agent/master/.claude/skills/accuracy-chart/SKILL.mdgit clone --depth 1 https://github.com/alfonsograziano/auto-agentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/alfonsograziano/auto-agent/accuracy-chart)<a href="https://agentmods.dev/skills/alfonsograziano/auto-agent/accuracy-chart"><img src="https://agentmods.dev/badge/skills/alfonsograziano/auto-agent/accuracy-chart/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/alfonsograziano/auto-agent/accuracy-chart"><img src="https://agentmods.dev/badge/skills/alfonsograziano/auto-agent/accuracy-chart.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00109 | $0.01920 |
| Opus 5.5 | $0.00044 | $0.00768 |
| Sonnet 5.5 | $0.00022 | $0.00384 |
| Haiku 4.5 | $0.00011 | $0.00192 |
Grade A, and why
accuracy-chart scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Accuracy Chart Generator
Generate a standalone HTML file with a line chart showing accuracy progression across iterations, plus a summary table, for an auto-agent job.
Data Sources
All data lives inside the job folder. Read these files:
-
out.log.txt— Contains the iteration summary block near the end of the file. Look for the last occurrence of the pattern:Iteration Summary: # Hypothesis Decision Accuracy Duration ──────────────────────────────────────────────────────── 1 001-abc123 CONTINUE 33.3% 4m 25s ... Total time: 16m 18s Final branch: agent-1-hyp-004-12f9ffParse each row for: iteration number, hypothesis ID, decision, accuracy percentage, and duration. Also extract total time and final branch.
-
hypotheses/000-baseline/REPORT.md— Contains the baseline accuracy in a metrics table:| accuracy | 18.3% |Extract the accuracy value. This is the first data point on the chart (x=Baseline).
How to Build the Chart
Step 1: Collect data
- Read
out.log.txtand find the last "Iteration Summary" block (the file may contain multiple job runs appended together). - Read baseline accuracy from the REPORT.md metrics table.
- Build the data series:
[baseline, iter1, iter2, ..., iterN]with accuracy percentages.
Step 2: Compute SVG coordinates
The chart is a hand-crafted SVG (no external libraries). Use these parameters:
- Chart area: x from 72 to
72 + chartWidth, y from 40 to 340 (300px tall) - chartWidth: scale based on number of data points —
Math.max(528, (points - 1) * 63)keeps spacing comfortable - SVG width: chartArea right edge + 40px padding
- X positions: evenly spaced from left edge to right edge
- Y mapping:
y = 340 - (accuracy / 100) * 300
For Y-axis grid lines, use 0%, 20%, 40%, 60%, 80%, 100% as labels.
Step 3: Handle label placement
When consecutive data points have similar accuracy values (within ~8pp), alternate label positions above and below the dots to prevent overlap. Place the percentage label 10px above the dot by default; for alternating ones, place 22px below.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 131 lines · 109 tokens per session scan A 17f923d13cc9
accuracy-chart is a skill published in the GitHub repository alfonsograziano/auto-agent (58 stars, last pushed 7mo ago), licensed MIT. It adds 109 tokens to every session and 1,920 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
accuracy-chart compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| accuracy-chartalfonsograziano/auto-agent | Skill | 58 | 7mo ago | MIT | A | 109 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 103 | Claude Code |