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/danstrem2/clawdbot-skill-master-pack/agent-developmentnpx skills add danstrem2/clawdbot-skill-master-pack --skill agent-developmentgit clone --depth 1 https://github.com/danstrem2/clawdbot-skill-master-packWrote 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/danstrem2/clawdbot-skill-master-pack/agent-development)<a href="https://agentmods.dev/skills/danstrem2/clawdbot-skill-master-pack/agent-development"><img src="https://agentmods.dev/badge/skills/danstrem2/clawdbot-skill-master-pack/agent-development.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 | $0.00069 | $0.02330 |
| Opus 5 | $0.00034 | $0.01165 |
| Sonnet 5 | $0.00014 | $0.00466 |
| Haiku 4.5 | $0.00007 | $0.00233 |
Grade A, and why
agent-development 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 5d 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 332 lines · 69 tokens per session scan A 19e71afe7184
agent-development is a skill published in the GitHub repository danstrem2/clawdbot-skill-master-pack (2 stars, last pushed 7mo ago), with no licence file. It adds 69 tokens to every session and 2,330 once invoked, about $0.0003 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 2026-08-31.
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package-private-ml-artifacts
Inventories, hashes, scans, and verifies datasets, adapters, checkpoints, logs, and evaluation evidence before an explicitly approved private upload. Use when ephemeral jobs or handoffs require durable ML artifacts without exposing credentials or unapproved data.
prepare-training-corpus
Builds and freezes deterministic train, validation, and test corpora with provenance, group-safe splits, manifests, hashes, and prompt-leakage checks. Use before teacher labeling, fine-tuning, or comparing models on generated or retrieved examples.
review-training-data-quality
Audits a candidate or labeled training corpus for distribution collapse, ambiguity, context sufficiency, hard-negative quality, abstention behavior, leakage, and stable label defensibility. Use before scaling teacher calls or starting fine-tuning.
select-and-verify-best-checkpoint
Proves that evaluation ran at the intended cadence, the tracked metric selected the true best checkpoint, and held-out inference loaded that checkpoint. Use with Hugging Face Trainer or compatible training-state artifacts before trusting test metrics.