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/zpower426/datapowersWrote 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/zpower426/datapowers/write-plan)<a href="https://agentmods.dev/commands/zpower426/datapowers/write-plan"><img src="https://agentmods.dev/badge/commands/zpower426/datapowers/write-plan/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/commands/zpower426/datapowers/write-plan"><img src="https://agentmods.dev/badge/commands/zpower426/datapowers/write-plan.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00010 | $0.00040 |
| Opus 5.5 | $0.00004 | $0.00016 |
| Sonnet 5.5 | $0.00002 | $0.00008 |
| Haiku 4.5 | $0.00001 | $0.00004 |
Grade A, and why
write-plan 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 1mo 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
Use the datapowers:writing-analysis-plans skill to decompose the approved design into a task plan.
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.
- 1mo ago First seen · 6 lines · 10 tokens per session scan A bcf5a986a591
write-plan is a command published in the GitHub repository zpower426/datapowers (1 stars, last pushed 6mo ago), licensed MIT. It adds 10 tokens to every session and 40 once invoked, about $0.0000 per session on Opus 5.5. 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.
Other commands, from other repositories
research
Research a technical or product question.
firebase-publish
Create or reuse a Firebase project, configure Hosting, and deploy the current static site. Never deletes.
ccc-rules-distill
Scan installed skills for principles that recur across them and propose them as rules, writing only what you approve. Use when skills repeat the same guidance or the rules directory has drifted from them.
p2-poc
Builds a minimal technical Proof of Concept for the riskiest technical area of the project. The goal is not a finished feature but proof that the planned approach fundamentally works – before investing in architecture and implementation.
p3-ux-wireframes
Creates wireframes (ASCII art) for the most important screens with interaction descriptions.
resume
Auto-detect and resume any interrupted Plan Cascade task. Detects mega-plan, hybrid-worktree, or hybrid-auto context and routes to the appropriate resume command.