Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add cxcscmu/SkillLearnBench --skill json-result-formattinggit clone --depth 1 https://github.com/cxcscmu/SkillLearnBenchWrote 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/cxcscmu/skilllearnbench/json-result-formatting)<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/json-result-formatting"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/json-result-formatting/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/cxcscmu/skilllearnbench/json-result-formatting"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/json-result-formatting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00016 | $0.01182 |
| Opus 5.5 | $0.00006 | $0.00473 |
| Sonnet 5.5 | $0.00003 | $0.00236 |
| Haiku 4.5 | $0.00002 | $0.00118 |
Grade A, and why
json-result-formatting 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 1mo 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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
JSON Result Formatting
Overview
Formatting retrieved data into standardized JSON output with answer lists and token tracking.
Use Cases
- Writing query results to JSON files
- Formatting answers as lists regardless of count
- Tracking API token consumption
- Creating consistent output files for downstream processing
Required Output Format
{
"q1": {"answer": ["xxx"], "tokens": 123},
"q2": {"answer": ["xxx", "yyy"], "tokens": 456},
"q3": {"answer": [], "tokens": 789}
}
Code Examples
Initialize Result Container
import json
result = {
"q1": {"answer": [], "tokens": 0},
"q2": {"answer": [], "tokens": 0},
"q3": {"answer": [], "tokens": 0}
}
Add Single Answer
def add_answer(result, question_key, answer_items, tokens=0):
"""Add answer as list (always list format)"""
# Ensure answer_items is a list
if isinstance(answer_items, str):
answer_items = [answer_items]
elif not isinstance(answer_items, list):
answer_items = list(answer_items)
result[question_key] = {
"answer": answer_items,
"tokens": int(tokens)
}
return result
# Usage
result = add_answer(result, "q1", ["eid_1e9356f5"], tokens=150)
result = add_answer(result, "q2", employee_ids_list, tokens=200)
Write to JSON File
import json
def write_result_file(result, filepath):
"""Write result to JSON file"""
with open(filepath, 'w') as f:
json.dump(result, f, indent=4)
print(f"Results written to {filepath}")
# Usage
write_result_file(result, '/root/answer.json')
Track Token Usage (Without API)
import json
# For local data processing, estimate tokens
def estimate_tokens_from_text(text):
"""Rough estimation: ~4 characters per token"""
return len(text) // 4
# Better: track actual API usage
def track_tokens(usage_dict):
"""Track from API response"""
if hasattr(usage_dict, 'input_tokens'):
return usage_dict.input_tokens + usage_dict.output_tokens
return 0
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.
- 1mo ago First seen · 174 lines · 16 tokens per session scan A c082bdf6d4aa
json-result-formatting is a skill published in the GitHub repository cxcscmu/SkillLearnBench (88 stars, last pushed 17d ago), licensed MIT. It adds 16 tokens to every session and 1,182 once invoked, about $0.0001 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.
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