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 flonat/flonat-research --skill reorggit clone --depth 1 https://github.com/flonat/flonat-researchWrote 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/flonat/flonat-research/reorg)<a href="https://agentmods.dev/skills/flonat/flonat-research/reorg"><img src="https://agentmods.dev/badge/skills/flonat/flonat-research/reorg/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/flonat/flonat-research/reorg"><img src="https://agentmods.dev/badge/skills/flonat/flonat-research/reorg.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 | $0.00053 | $0.01072 |
| Opus 5 | $0.00026 | $0.00536 |
| Sonnet 5 | $0.00011 | $0.00214 |
| Haiku 4.5 | $0.00005 | $0.00107 |
Grade A, and why
reorg 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 8d 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
reorg — local-model file reorganiser
Conversational front-end for the local-reorg CLI (scripts/local-reorg/). The CLI runs a
local Ollama model on the Mac Mini that reads each file's content and proposes where it
should go, or finds duplicates. Nothing is ever moved without your approval, and every run
is reversible.
When to use
- "Reorganise / tidy / sort this folder" where you want the model to read files, not just pattern-match filenames.
- "Find duplicates" / "dedup this folder" (exact + near-duplicate via embeddings).
- General clutter (Downloads, Desktop, a messy project subfolder).
For research-project structural changes use an installed project-renaming or
project-initialization workflow; if none is available, use a normal reviewed git mv
plan with explicit backlink and configuration checks. For meeting to-sort/ inboxes,
use the project's inbox-processing workflow. This skill is general-purpose file tidying.
How to invoke the CLI
First hostname:
- On the Mac Mini (
[server]): thereorgshim is on PATH — callreorg …directly. - Anywhere else (MacBook, etc.): run the installed Mini wrapper over SSH:
ssh mini '~/.local/bin/reorg <args>'
The model + files both live on the Mini, so resolve the target using the Mini's Task Management path registries before invoking the wrapper. Do not embed either machine's physical Dropbox root. If you cannot resolve the path unambiguously, ask.
Workflow
- Confirm the target folder and pick a mode with the user if unclear:
- reorg by scheme:
auto(default) ·by-type·by-topic·by-date - dedup:
--dedup(report) →--dedup --quarantine(move redundant copies aside)
- reorg by scheme:
- Dry-run — never skip this:
- reorg:
reorg <folder> [--scheme X] [--recursive] - dedup:
reorg <folder> --dedupThe CLI writes<folder>/.reorg/plan.md(reorg) or.reorg/duplicates.md(dedup).
- reorg:
- Read the plan file and summarise it for the user — group counts, notable moves, and
any
⚠low-confitems. Do not dump the whole file; give a scannable summary and the path. - Get approval. The user may edit
.reorg/plan.jsonfirst (trim/retarget moves). - Execute only after a clear yes:
- reorg:
reorg <folder> --apply(add--yesif ≥20 moves — tell the user it's the break-the-glass threshold) - dedup:
reorg <folder> --dedup --quarantine
- reorg:
- Offer undo:
reorg <folder> --undoreverses the most recent applied/quarantined run (LIFO). Mention it after any execution.
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.
- 8d ago First seen · 79 lines · 53 tokens per session scan A 66cc1cb945a0
reorg is a skill published in the GitHub repository flonat/flonat-research (133 stars, last pushed 16d ago), licensed MIT. It adds 53 tokens to every session and 1,072 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 2026-09-03.
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