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 Light0305/Light-skills --skill light-project-structuregit clone --depth 1 https://github.com/Light0305/Light-skillsWrote 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/light0305/light-skills/light-project-structure)<a href="https://agentmods.dev/skills/light0305/light-skills/light-project-structure"><img src="https://agentmods.dev/badge/skills/light0305/light-skills/light-project-structure.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 | $0.00147 | $0.02616 |
| Opus 5 | $0.00073 | $0.01308 |
| Sonnet 5 | $0.00029 | $0.00523 |
| Haiku 4.5 | $0.00015 | $0.00262 |
Grade A, and why
light-project-structure 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 — 272 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Project structure lifecycle
Own the visible project tree and its migration evidence. Do not mistake a tidy directory for reproducible research.
Read
references/project-lifecycle-resource-map.md
before an existing-repository migration. It defines artifacts, policy, access
levels, provenance, and cross-skill ownership. Use
references/structure-profiles.json for
small profile minima and
templates/project-policy.template.json
for explicit project/file policy.
Use scripts/structure_governance_gate.py before delivery to validate profile
choice, existing-project read-only safety, template residuals, secret scan,
environment doctor, authorization binding, applied-manifest binding, and
rollback evidence.
Non-negotiable boundary
- Treat inventory as read-only access, not authorization to move.
- Never overwrite, delete, run
git rm --cached, initialize DVC, rewrite configuration, or move a symlink automatically. - Never use
--forceas consent. The lifecycle has no force bypass. - Preserve all
.light/content.memory-pmalone creates or edits passport, project card, decision log, version history, terminology, and handoff files. - Keep absent facts
UNKNOWN. A path such asdata/rawdoes not prove size, sensitivity, immutability, recomputability, or Git policy. - Do not turn this overlay into a DAG node. Emit no
light.findings.v1; add noSTAGE_GATES,ROUTES, stage number, or back-edge. - State that structural conformance does not prove data quality, experiment reproducibility, statistical validity, or paper quality.
- Never ship a generated or migrated tree with unresolved template placeholders, unhandled secret-scan hits, or missing required Python/R/environment checks.
Choose the mode
| Situation | Mode |
|---|---|
| Empty target and the user wants a starting tree | scaffold with one explicit profile |
| Existing repository, monorepo package, or non-Git directory | intake, then stop at the decision |
| User approved exact moves after seeing the plan | create authorization, then apply |
| Applied moves need reversal | rollback from the applied manifest |
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 272 lines · 147 tokens per session scan A 085a9af9101c
light-project-structure is a skill published in the GitHub repository Light0305/Light-skills (604 stars, last pushed 2mo ago), licensed MIT. It adds 147 tokens to every session and 2,616 once invoked, about $0.0007 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 2026-08-30.
Other skills, from other repositories
anti-defensive-writing-en
Stops defensive writing across the entire paper lifecycle — writing, revising, cutting, and organizing experiments. Treats the paper as a press conference, not a project summary, lab log, or self-audit: identify the single most publishable strength of the work and build the most favorable, complete, and persuasive…
anti-defensive-writing
A Chinese-language writing guide for presenting a research paper around its strongest supported contribution. It treats the paper as a focused academic presentation rather than a project diary or complete lab record.
research-writing
A collection of 30 prompt templates for writing and reviewing scientific papers. It covers tasks such as translating, editing, summarizing research, writing sections, creating figure captions, and preparing reviewer replies.
ml-paper-writing
Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.
ml-paper-writing
Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, conducting literature reviews, finding related work, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, citation verification workflows, and paper…
academic-citation
Search, verify, and map citations for CS/AI/ML papers. Produces VERIFIED/UNVERIFIED reference lists with Citation-to-Claim maps and Exemplar Sets. Use when: finding references for a paper section, verifying citation accuracy, building exemplar sets for introduction/related work learning, checking if existing citations…