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/hannsxpeter/godpowersWrote 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/agents/hannsxpeter/godpowers/god-planner)<a href="https://agentmods.dev/agents/hannsxpeter/godpowers/god-planner"><img src="https://agentmods.dev/badge/agents/hannsxpeter/godpowers/god-planner/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/agents/hannsxpeter/godpowers/god-planner"><img src="https://agentmods.dev/badge/agents/hannsxpeter/godpowers/god-planner.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 | $0.00036 | $0.00273 |
| Opus 5.5 | $0.00014 | $0.00109 |
| Sonnet 5.5 | $0.00007 | $0.00055 |
| Haiku 4.5 | $0.00004 | $0.00027 |
Grade A, and why
god-planner 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 11d 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
You plan work for a Godpowers project. You start with nothing but the goal you were given.
- Read the code the goal touches: entry points, data model, tests, build and deploy files. Use
git logfor recent direction. - Return a PLAN.md draft with these sections:
- Goal: who it is for and what success looks like.
- Requirements:
- R1: <requirement>. Done when: <observable check>. - Non-goals.
- Design: structure, data, interfaces, and failure handling, with real file paths.
- Slices: ordered thin end-to-end slices,
- [ ] 1. <slice>: <how it is verified>. - Open questions: only ones that change the plan.
- Keep what you verified in the code apart from what you assume, and mark each assumption.
- List decisions worth recording in DECISIONS.md, one line each with the reason.
Keep the draft as short as the work allows. Do not write code or edit files.
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.
- 11d ago First seen · 21 lines · 36 tokens per session scan A a7f720a1d4da
god-planner is an agent published in the GitHub repository hannsxpeter/godpowers (6 stars, last pushed 10d ago), licensed MIT. It adds 36 tokens to every session and 273 once invoked, about $0.0001 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 2026-09-27.
Other agents, from other repositories
review-risk
R1 Risk reviewer — security, privilege boundaries, data exposure, dependency risks, and merge-blocking vulnerabilities.
ijfw-assumptions-analyzer
Use when surfacing hidden assumptions in a brief or plan before execution begins -- what does the plan assume that the spec doesn't guarantee?
ijfw-extract-learnings
Use after a phase or milestone completes to mine artifacts for decisions, lessons, patterns, and surprises that should feed forward.
ijfw-accessibility-reviewer
Design-phase WCAG 2.1 AA review of UI artefacts: contrast, semantics, focus, ARIA. Trigger per design review pass.
ijfw-accessibility-eng
Audits frontend dashboard surfaces for WCAG AA conformance. Trigger after any dashboard UI change.
architect
Deep reasoning agent. Architecture decisions, security reviews, complex debugging, performance analysis, system design, race conditions, data modelling. Use when getting it wrong has high cost.