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 clay-good/OpenLore --skill openlore-plan-refactorgit clone --depth 1 https://github.com/clay-good/OpenLoreWrote 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/clay-good/openlore/openlore-plan-refactor)<a href="https://agentmods.dev/skills/clay-good/openlore/openlore-plan-refactor"><img src="https://agentmods.dev/badge/skills/clay-good/openlore/openlore-plan-refactor/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/clay-good/openlore/openlore-plan-refactor"><img src="https://agentmods.dev/badge/skills/clay-good/openlore/openlore-plan-refactor.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 | $0.00046 | $0.02795 |
| Opus 5.5 | $0.00018 | $0.01118 |
| Sonnet 5.5 | $0.00009 | $0.00559 |
| Haiku 4.5 | $0.00005 | $0.00280 |
Grade A, and why
openlore-plan-refactor 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 2mo 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 — 300 lines — stays where its author put it; the contents beside it link to each section on GitHub.
openlore: Plan Refactor
When to use this skill
Trigger this skill whenever the user asks to plan a refactoring on a codebase, with phrasings like:
- "plan a refactoring of X"
- "analyze my code and prepare a refactor plan"
- "generate a refactoring plan"
- "I want to refactor this function / this file"
- explicit command
/openlore-plan-refactor
This skill modifies no code files. It only produces .openlore/refactor-plan.md.
To apply the plan, use the openlore-execute-refactor skill.
⚠️ Fundamental principle — each change is a complete mini-development
The plan must be written so that every entry in the change sequence is independently executable and testable.
Each change = one atomic edit → one diff verification → one full test run → ✅ or rollback.
This is not a final gate at the end. Testing is a mandatory sub-step after every single change. The plan must make this cycle explicit and impossible to skip.
For small models (< 13B parameters): each change must touch at most 50 contiguous lines in the source file. If a logical extraction exceeds this limit, split it into smaller sub-changes, each with its own test gate.
User-specified target — shortcut path
If the user has already named a specific file or function to refactor:
- Skip Steps 2, 3, and 3b (discovery is not needed).
- Do not skip Steps 3c, 4, 5, 6, and 6b — coverage check and impact analysis are mandatory regardless of how the target was chosen.
- Jump directly to Step 3c using the user-provided target.
Step 1 — Confirm the project directory
Ask the user which project to analyze, or confirm the current workspace root.
Step 2 — Run static analysis
Call the openlore MCP tool analyze_codebase with {"directory": "$DIRECTORY"}.
If a recent analysis already exists, skip unless the user explicitly requests a fresh run.
Step 3 — Get the refactoring report
Call the openlore MCP tool get_refactor_report with {"directory": "$DIRECTORY"}.
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.
- 2mo ago First seen · 300 lines · 46 tokens per session scan A 14620f7ceebf
openlore-plan-refactor is a skill published in the GitHub repository clay-good/OpenLore (358 stars, last pushed today), licensed MIT. It adds 46 tokens to every session and 2,795 once invoked, about $0.0002 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 2026-08-30.
openlore-plan-refactor compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| openlore-plan-refactorclay-good/OpenLore | Skill | 358 | today | MIT | A | 46 | Claude Code, Codex |
| software-architecteligapris/software-architect | Skill | 4 | yesterday | none | A | 171 | Claude Code, Codex |
| architecture-decision-record-creatorbookforge-ai/bookforge-skills | Skill | 42 | 6mo ago | CC-BY-SA-4.0 | A | 91 | Claude Code |
| agenticx-query-data-sourceDemonDamon/AgenticX | Skill | 1.0k | today | Apache-2.0 | A | 49 | Claude Code, Codex |
| nw-ddd-eventsourcingnWave-ai/nWave | Skill | 633 | today | MIT | A | 38 | Claude Code |
| nw-fp-clojurenWave-ai/nWave | Skill | 633 | today | MIT | A | 25 | Claude Code |
| nw-leanux-methodologynWave-ai/nWave | Skill | 633 | today | MIT | A | 37 | Claude Code |