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 skills/techwolf-ai/ai-first-toolkit/analyze-performancenpx skills add techwolf-ai/ai-first-toolkit --skill analyze-performancegit clone --depth 1 https://github.com/techwolf-ai/ai-first-toolkitWrote 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/techwolf-ai/ai-first-toolkit/analyze-performance)<a href="https://agentmods.dev/skills/techwolf-ai/ai-first-toolkit/analyze-performance"><img src="https://agentmods.dev/badge/skills/techwolf-ai/ai-first-toolkit/analyze-performance.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 | $0.00031 | $0.00582 |
| Opus 5 | $0.00015 | $0.00291 |
| Sonnet 5 | $0.00006 | $0.00116 |
| Haiku 4.5 | $0.00003 | $0.00058 |
Grade A, and why
analyze-performance 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.
How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze Content Performance
Identify patterns in high-performing posts to inform future content strategy.
Process
- Run
./scripts/print-published.sh linkedin-postto read all published LinkedIn posts - Extract posts that have engagement data (engagement.reactions, engagement.views, etc.)
- Analyze patterns across high-performing vs low-performing posts
Analysis Dimensions
Hook Analysis
- What hook styles correlate with higher engagement?
- Personal anecdote vs company experience vs surprising data vs news hook?
- First 210 characters (LinkedIn cutoff) - what patterns work?
Content Characteristics
- Word count vs engagement correlation
- Use of concrete examples vs abstract concepts
- Presence of frameworks or mental models
- Use of lists/structure vs flowing narrative
Topic Analysis
- Which tags correlate with higher engagement?
- Which themes resonate most?
- Timing patterns (if publishedDate available)
Structural Patterns
- Opening style (question, statement, story)
- Closing style (call-to-action, reflection, question)
- Paragraph length and density
Performance Tiers
Categorize posts by reaction count:
- High performers: 100+ reactions
- Medium performers: 30-99 reactions
- Lower performers: <30 reactions
Output Format
Provide:
- Summary statistics - Total posts analyzed, average engagement by tier
- Top performers - List highest-engagement posts with their key characteristics
- Pattern insights - What distinguishes high vs lower performers?
- Recommendations - Actionable suggestions for future content
Example Analysis Output
## Performance Summary
- Posts analyzed: 12 (with engagement data)
- High performers (100+): 3 posts
- Medium performers (30-99): 5 posts
- Lower performers (<30): 4 posts
## Top Performers
1. "Title" - 245 reactions
- Hook: Personal anecdote
- Topic: AI productivity
- Word count: 180
## Key Patterns
- Personal anecdotes in the first sentence correlate with 2x higher engagement
- Posts with concrete examples outperform abstract posts by 40%
- Optimal word count appears to be 150-200 words
## Recommendations
1. Lead with personal or company-specific openings
2. Include at least one specific example or data point
3. Keep total length under 220 words
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 · 85 lines · 31 tokens per session scan A 80946a9e0b2d
analyze-performance is a skill published in the GitHub repository techwolf-ai/ai-first-toolkit (98 stars, last pushed 1mo ago), licensed MIT. It adds 31 tokens to every session and 582 once invoked, about $0.0002 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.
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