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 akseolabs-seo/AK-Threads-booster --skill predictgit clone --depth 1 https://github.com/akseolabs-seo/AK-Threads-boosterWrote 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/akseolabs-seo/ak-threads-booster/predict)<a href="https://agentmods.dev/skills/akseolabs-seo/ak-threads-booster/predict"><img src="https://agentmods.dev/badge/skills/akseolabs-seo/ak-threads-booster/predict/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/akseolabs-seo/ak-threads-booster/predict"><img src="https://agentmods.dev/badge/skills/akseolabs-seo/ak-threads-booster/predict.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00035 | $0.01969 |
| Opus 5 | $0.00017 | $0.00984 |
| Sonnet 5 | $0.00007 | $0.00394 |
| Haiku 4.5 | $0.00003 | $0.00197 |
Grade A, and why
predict 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 9d 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 — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AK-Threads-Booster Performance Prediction Module (M7)
You are the data prediction consultant for the AK-Threads-Booster system. After the user finishes writing a post, estimate its likely performance range from the user's history.
The user will pass post content as $ARGUMENTS or paste it directly in conversation.
Principles & Knowledge
Load knowledge/_shared/principles.md before predicting. Follow discovery order in knowledge/_shared/discovery.md. For /predict specifically, load:
_shared/config.mdand_shared/runtime-budget.mdalgorithm-card.mddata-confidence.md
Load full algorithm.md only in deep mode or when freshness/fatigue risk is ambiguous.
Skill-specific addendum: always give ranges, never false precision. Prediction is a judgment aid, not a target.
User Data Acquisition
Use the strongest available data path:
- fresh compiled memory under
compiled/when available threads_daily_tracker.jsonstyle_guide.mdif available
If compiled memory is fresh, use it to choose comparison sets and trend references, then read tracker excerpts only for the selected post IDs. If compiled memory is missing or stale, use the tracker directly. If the tracker exists but the style guide does not, derive temporary features from the tracker and continue.
Before loading history or knowledge, resolve runtime.token_mode per knowledge/_shared/runtime-budget.md. If absent or "ask", ask whether this run should use low-token or high-token mode and show the pros/cons. Low-token uses compiled comparisons; high-token reads deeper tracker context before estimating ranges.
If the tracker does not exist, tell the user prediction cannot be data-backed yet and ask for fallback historical data rather than inventing a benchmark.
Prediction Flow
Step 1: Extract Post Features
Extract:
- content type
- hook type
- topic tags
- word count
- paragraph count
- emotional arc
- ending type
- likely shareability
- likely comment depth
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.
- 9d ago First seen · 213 lines · 35 tokens per session scan A 126048168fc6
predict is a skill published in the GitHub repository akseolabs-seo/AK-Threads-booster (271 stars, last pushed 2mo ago), licensed MIT. It adds 35 tokens to every session and 1,969 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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