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 agentmods add commands/fatihkan/badi/content-perfgit 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/content-perf)<a href="https://agentmods.dev/commands/fatihkan/badi/content-perf"><img src="https://agentmods.dev/badge/commands/fatihkan/badi/content-perf.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 | $0.00000 | $0.00465 |
| Opus 5 | $0.00000 | $0.00233 |
| Sonnet 5 | $0.00000 | $0.00093 |
| Haiku 4.5 | $0.00000 | $0.00047 |
Grade A, and why
content-perf 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.
What it actually says
Content performance tracking command. Tracks likes, comments, reach, and ROI for published content.
Required Tools
- Bash (badi content perf)
Procedure
Step 1: Add Data
Record the metrics after every publish:
badi content perf add --file 2026-04-19-topic.md \
--platform instagram \
--likes 150 --comments 12 --shares 5 --saves 20 \
--reach 2500 \
--effort 1.5
Parameters:
--file— Content file name--platform— instagram/twitter/linkedin/tiktok/facebook--likes/--comments/--shares/--saves/--reach— Metrics--effort— Production time (hours)
Step 2: Reports
badi content perf # Weekly summary (default)
badi content perf --week
badi content perf --month
badi content perf list # All records
Step 3: Trend Analysis
badi content perf --trend
Previous vs. current period comparison:
- Total engagement change (%)
- Platform-level trends
Step 4: ROI Analysis
badi content perf --roi
Engagement/effort ratio per platform. Which platform pays the most for your hour?
Step 5: Platform Filter
badi content perf --platform instagram --month
Step 6: Interpretation + Action
Based on the report, tell the user:
- Best performer: "Shall we repeat this format?"
- Low ROI: "Rethink the time investment on this platform?"
- Negative trend: "Should the content mix be revised?"
Step 7: Weekly Routine
Thursday/Friday evening weekly evaluation:
badi content perf --trend # Evaluate the week
badi content plan # Plan next week
Example
/content-perf # Weekly summary
/content-perf --trend # Trend comparison
/content-perf --roi # ROI ranking
/content-perf add --file ... --platform linkedin --likes 85 ...
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.
- 4d ago First seen · 82 lines · 0 tokens per session scan A cf07d6f2e5fe
content-perf is a command published in the GitHub repository fatihkan/badi (7 stars, last pushed 17d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 465 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 2026-08-31.
Other commands, from other repositories
audit-agents-skills
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sonarqube
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land-and-deploy
Merge PR, wait for CI, verify deploy, run canary — the complete landing pipeline.
methodology-advisor
Analyzes your codebase and asks 3 targeted questions to recommend the right AI-assisted development methodology stack.
scaffold
Interactive coach that asks 4-5 questions to determine whether you need an agent, command, skill, hook, or rule — then generates a ready-to-use template. Usage: /scaffold (no arguments — starts the coaching session).
investigate
Systematic root-cause debugging — find the cause before writing any fix.