json-report-generation

json-report-generation is a skill for Claude Code, Codex from cxcscmu/SkillLearnBench. It costs 16 tokens per session (1,134 once invoked), scanned A, original, MIT.

A method for creating, validating, and saving reports in JSON, a structured text format that programs can read reliably.

In plain words
What is it for?
It is for producing project reports with metrics such as pull requests, issues, merge times, contributors, and resolved bugs.
Why use it?
It helps prevent malformed reports and ensures required fields use the expected names and data types.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/cxcscmu/skilllearnbench/json-report-generation
Any agent
npx skills add cxcscmu/SkillLearnBench --skill json-report-generation
Clone the repo
git clone --depth 1 https://github.com/cxcscmu/SkillLearnBench

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for json-report-generation

README.md
[![agentmods](https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/json-report-generation.svg)](https://agentmods.dev/skills/cxcscmu/skilllearnbench/json-report-generation)
Your own site
<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/json-report-generation"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/json-report-generation.svg" alt="Measured on agentmods" height="20"></a>
Per session 16 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,134 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00016 $0.01134
Opus 5 $0.00008 $0.00567
Sonnet 5 $0.00003 $0.00227
Haiku 4.5 $0.00002 $0.00113

Measured 4d ago against content hash 8a969bde7143, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

json-report-generation 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 4d 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.

skills/b1-one-shot-claude-haiku-4-5/github-repo-analytics/json-report-generation/SKILL.md · 141 lines

How it starts

The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.

JSON Report Generation Skill

Overview

Create, validate, and write JSON reports with proper formatting and schema verification.

Basic Report Structure

{
  "pr": {
    "total": 0,
    "merged": 0,
    "closed": 0,
    "avg_merge_days": 0.0,
    "top_contributor": "username"
  },
  "issue": {
    "total": 0,
    "bug": 0,
    "resolved_bugs": 0
  }
}

Field Specifications

  • pr.total: Integer count of all PRs created during period
  • pr.merged: Integer count of merged PRs (as of report date)
  • pr.closed: Integer count of closed (non-merged) PRs
  • pr.avg_merge_days: Float rounded to 1 decimal place
  • pr.top_contributor: String username of author with most PRs
  • issue.total: Integer count of all issues created during period
  • issue.bug: Integer count of issues with 'bug' in any label
  • issue.resolved_bugs: Integer count of bug issues that were closed

Python Implementation

Basic Write

import json

report = {
    "pr": {
        "total": 125,
        "merged": 95,
        "closed": 20,
        "avg_merge_days": 3.2,
        "top_contributor": "alice"
    },
    "issue": {
        "total": 45,
        "bug": 18,
        "resolved_bugs": 12
    }
}

with open('/app/report.json', 'w') as f:
    json.dump(report, f, indent=2)

With Validation

import json

def validate_report(report):
    """Validate report structure and types"""
    required_keys = {'pr', 'issue'}
    pr_keys = {'total', 'merged', 'closed', 'avg_merge_days', 'top_contributor'}
    issue_keys = {'total', 'bug', 'resolved_bugs'}

    assert set(report.keys()) == required_keys, "Missing top-level keys"
    assert set(report['pr'].keys()) == pr_keys, "Missing PR keys"
    assert set(report['issue'].keys()) == issue_keys, "Missing issue keys"

    assert isinstance(report['pr']['total'], int), "PR total must be int"
    assert isinstance(report['pr']['merged'], int), "PR merged must be int"
    assert isinstance(report['pr']['closed'], int), "PR closed must be int"
    assert isinstance(report['pr']['avg_merge_days'], (int, float)), "avg_merge_days must be numeric"
    assert isinstance(report['pr']['top_contributor'], str), "top_contributor must be string"

    assert isinstance(report['issue']['total'], int), "Issue total must be int"
    assert isinstance(report['issue']['bug'], int), "Issue bug count must be int"
    assert isinstance(report['issue']['resolved_bugs'], int), "resolved_bugs must be int"

    return True

def write_report(report, filepath):
    """Write and validate report"""
    validate_report(report)
    with open(filepath, 'w') as f:
        json.dump(report, f, indent=2)
    print(f"Report written to {filepath}")

Read the full file on GitHub · 141 lines

Changes

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.

  1. 4d ago First seen · 141 lines · 16 tokens per session scan A 8a969bde7143

Subscribe to this mod's changes

json-report-generation is a skill published in the GitHub repository cxcscmu/SkillLearnBench (82 stars, last pushed 1mo ago), licensed MIT. It adds 16 tokens to every session and 1,134 once invoked, about $0.0001 per session on Opus 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.

Related

Other skills, from other repositories

ceo-setup

One-time onboarding for the executive/manager commitment workflow — delegation-heavy, meeting prep, decision capture, morning and evening digests. Creates a commitments project and installs two dashboard widgets. After successful setup this skill is excluded from selection until the marker file is deleted.

suyoumo/ClawProBench · 60 tokens

content-creator-setup

One-time onboarding for the content creator workflow — content pipeline stages, trend expiration, cross-platform cascades, heavy idea parking. After successful setup this skill is excluded from selection until the marker file is deleted.

suyoumo/ClawProBench · 47 tokens

idea-parking

Park interesting ideas for later consideration, resurface them periodically, and promote to commitments when ready.

suyoumo/ClawProBench · 23 tokens

routing-subtour-elimination

Subtour-elimination methods for TSP, VRP, pickup/dropoff routing, and routing MIPs with binary arc variables. Use when route-continuity constraints may permit disconnected cycles and the model needs MTZ constraints, flow-based connectivity constraints, DFJ subset cuts, or lazy/iterative subtour cuts.

benchflow-ai/skillsbench · 70 tokens

scip-opt

SCIP optimization with PySCIPOpt. Use when facing an optimization problem with an objective, hard constraints, soft penalties, integer decisions, routing, assignment, scheduling, allocation, packing, capacity, inventory, or service-level rules. Prefer modeling and solving the problem with PySCIPOpt when it is…

benchflow-ai/skillsbench · 67 tokens

ebcdic-overpunch-decoding

Reference for the EBCDIC "overpunch" / zoned-decimal sign convention where the units position of a numeric field is replaced with a letter that encodes both a digit and a sign. Useful when reading mainframe-style fixed-length tapes whose amount fields appear as digits followed by a letter (e.g. "0000000000123D" or…

benchflow-ai/skillsbench · 90 tokens