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 h4vzz/awesome-ai-agent-skills --skill report-generationgit clone --depth 1 https://github.com/h4vzz/awesome-ai-agent-skillsWrote 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/h4vzz/awesome-ai-agent-skills/report-generation)<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/report-generation"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/report-generation/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/h4vzz/awesome-ai-agent-skills/report-generation"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/report-generation.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 | $0.00036 | $0.03181 |
| Opus 5.5 | $0.00014 | $0.01272 |
| Sonnet 5.5 | $0.00007 | $0.00636 |
| Haiku 4.5 | $0.00004 | $0.00318 |
Grade A, and why
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 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.
This is a copy
95% identical to report-generation — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 261 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Report Generation
This skill enables an AI agent to produce polished, data-driven reports from structured input. The agent accepts data in JSON, CSV, or API response format, applies a report template, generates narrative insights alongside tables and chart specifications, and outputs the final report in Markdown, HTML, or PDF. It supports common report types including sprint retrospectives, financial summaries, analytics dashboards, and incident postmortems.
Workflow
-
Ingest and validate the source data. Accept the input data file or payload in JSON, CSV, YAML, or raw API response format. Validate the schema — check for required fields, correct data types, missing values, and outliers. If the data is incomplete (e.g., a sprint retro JSON missing the velocity field), flag the gap and either infer a default, request the missing value, or note the omission in the report. Normalize date formats, currency symbols, and units for consistency.
-
Select or customize the report template. Match the data to a report template based on the report type specified by the user. Built-in templates include:
sprint_retrospective,financial_summary,analytics_dashboard,incident_postmortem, andweekly_status. Each template defines the section order, required data mappings, chart types, and tone (analytical for financial reports, constructive for retros, urgent for postmortems). Users can customize templates by overriding sections, adding fields, or changing the visual theme. -
Compute metrics and generate insights. Derive computed metrics from the raw data — percentage changes, averages, rankings, trend directions, and anomaly flags. For a sprint retro, calculate velocity variance and commitment accuracy. For an analytics report, compute conversion rates and segment-level breakdowns. Then generate narrative insights: not just "conversion rate was 3.2%" but "conversion rate dropped 0.8pp from last period, driven primarily by a 15% decline in mobile traffic."
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 · 261 lines · 36 tokens per session scan A bc9543d37710
report-generation is a skill published in the GitHub repository h4vzz/awesome-ai-agent-skills (36 stars, last pushed 22d ago), licensed MIT. It adds 36 tokens to every session and 3,181 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 95% identical to report-generation, differing in 2 lines, and is treated as a copy.
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