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 init-project-lightgit 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/init-project-light)<a href="https://agentmods.dev/skills/flonat/flonat-research/init-project-light"><img src="https://agentmods.dev/badge/skills/flonat/flonat-research/init-project-light.svg" alt="Measured on agentmods" 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 | $0.00051 | $0.01028 |
| Opus 5 | $0.00026 | $0.00514 |
| Sonnet 5 | $0.00010 | $0.00206 |
| Haiku 4.5 | $0.00005 | $0.00103 |
Grade A, and why
init-project-light 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 4d 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Init Project Light
Lightweight project bootstrapper for small projects that do not need a full research-project scaffold.
When to Use
- Small document collections (proposals, applications, meeting notes)
- One-off or short-lived projects
- Projects without a code pipeline or Overleaf link
- When the user says "set up something light", "quick init", "organise this folder"
- Any project that does not warrant the installation's full research-project initializer
When NOT to Use — Escalate to a full research-project initializer
- Research papers targeting a journal or conference
- Projects with code, data, or computational pipelines
- Anything that needs Overleaf, git, or a vault atlas entry
Phase 1: Scan
Read everything already in the directory before asking questions.
- List all files and folders (excluding
.claude/,.DS_Store) - Read text files (
.md,.tex,.bib,.txt) to understand content — respect file size (skip files > 500 lines, note them) - Build a mental model: what is this project, what's the main output, who's involved?
Goal: Minimise interview questions by inferring answers from existing files.
Phase 2: Interview (2-3 questions max)
Use the available structured-question mechanism. Only ask what you couldn't infer from Phase 1.
Pick from these (skip any you can already answer):
- What is this project? — one sentence (e.g., "PhD research proposal for [University]")
- What's the main output? — document, application, collection of notes, etc.
- Anyone else involved? — names and roles if relevant
If Phase 1 gave you enough, confirm your understanding instead of asking:
"From the files, this looks like [X]. The main output is [Y]. Correct?"
Phase 3: Create CLAUDE.md
Follow the lean-guidance-files rule. Include only:
- Project overview — 2-3 sentences from interview/scan
- People — if collaborators/supervisors exist
- Directory structure — compact tree of what exists
- Conventions — only if detectable (e.g., LaTeX compilation, bibliography style)
- Key context — anything a future session needs to know immediately
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.
- 4d ago First seen · 140 lines · 51 tokens per session scan A f519e976e9de
init-project-light is a skill published in the GitHub repository flonat/flonat-research (132 stars, last pushed 13d ago), licensed MIT. It adds 51 tokens to every session and 1,028 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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