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.
git clone --depth 1 https://github.com/fatihkan/badiWrote 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/commands/fatihkan/badi/report)<a href="https://agentmods.dev/commands/fatihkan/badi/report"><img src="https://agentmods.dev/badge/commands/fatihkan/badi/report.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00992 |
| Opus 5 | $0.00000 | $0.00496 |
| Sonnet 5 | $0.00000 | $0.00198 |
| Haiku 4.5 | $0.00000 | $0.00099 |
Grade A, and why
report 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 today.
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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Professional report command. Turns raw data and findings into professional, audience-appropriate reports.
Required Tools
- Read (data sources)
- Write (report file)
- Grep (data scan)
- Glob (source discovery)
- Bash (data processing)
Procedure (5 Steps)
Step 1: Clarify the Inputs
Get from the user:
- Topic: What is the report about?
- Data Sources: Which data will be used? (files, metrics, analyses)
- Audience: Who will read it? (Executive / Technical / Client)
- Purpose: What will the report be used for? (decision support, information, persuasion)
- Format Preference: Default: professional, clear, jargon-free
- Length: Short (1-2 pages) / Standard (3-5 pages) / Detailed (5+ pages)
- Urgency: Normal / Urgent (fast draft)
Step 2: Source Collection
Compile all relevant data:
- Quantitative Data: Metrics, statistics, measurements
- Qualitative Data: Observations, feedback, assessments
- Trends: Time-series data, change rates
- Comparisons: Targets vs. actuals, previous period vs. current
- Anomalies: Out-of-norm situations and their explanations
- External Factors: Outside influences on the results
If data is missing, tell the user and either collect it or state the assumption.
Step 3: Structure by Audience
Type A: Executive Report
- Conclusion first, detail later (pyramid structure)
- Bullets and short paragraphs
- Decision metrics and KPIs up front
- A 2-page main body maximum
- Visual summaries (table and chart descriptions)
- Clear recommendations and next steps
- No jargon, business language
Type B: Technical Report
- Methodology and data sources in detail
- Technical terminology allowed
- Data tables and detailed analyses
- A caveats-and-limitations section
- Source references and citations
- Reproducibility information
- Appendix sections
Type C: Client Report
- Results and ROI emphasis
- Contextualized numbers (percentages, comparatives)
- Visual formats (tables, lists, highlighted metrics)
- Wins and value demonstration
- Plain language, minimal technical detail
- Next steps and expectations
- A professional, confidence-inspiring tone
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.
- today First seen · 154 lines · 0 tokens per session scan A 39f7edfc0a7e
report is a command published in the GitHub repository fatihkan/badi (7 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 992 tokens. 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 2026-09-06.
Other commands, from other repositories
audit-agents-skills
Audit quality of agents, skills, and commands in a Claude Code project.
land-and-deploy
Merge PR, wait for CI, verify deploy, run canary — the complete landing pipeline.
qa
Systematic QA testing of a web application — diff-aware, tiered, with fix-and-verify loop.
review-pr
Perform a comprehensive code review of a pull request.
security-audit
Comprehensive security audit with scored posture assessment.
git-worktree-clean
Clean up stale git worktrees with merged branch detection and disk usage report.