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-orchestratorgit 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-orchestrator)<a href="https://agentmods.dev/skills/light0305/light-skills/light-orchestrator"><img src="https://agentmods.dev/badge/skills/light0305/light-skills/light-orchestrator/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/light0305/light-skills/light-orchestrator"><img src="https://agentmods.dev/badge/skills/light0305/light-skills/light-orchestrator.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 | $0.00141 | $0.03824 |
| Opus 5 | $0.00071 | $0.01912 |
| Sonnet 5 | $0.00028 | $0.00765 |
| Haiku 4.5 | $0.00014 | $0.00382 |
Grade A, and why
light-orchestrator 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 10d 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 — 355 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Light orchestrator
Coordinate, route and recover the research lifecycle. Do not impersonate the stage skills and do not turn a deterministic check into a research judgment.
Read
references/orchestrator-resource-map.md
before a real lifecycle run. It defines intake, state authority, migration,
evidence states, access tiers, resident budget and handoff. Read
references/integration-contract.json
when changing any role, gate or route. The detailed rationale is
../../docs/design/orchestrator-spec.md.
Non-negotiable boundaries
- Never choose the research direction, final idea, plan, venue, back-edge, revision-budget exception, known-limitation conversion or final delivery for the user. Present a recommendation, evidence, alternatives and consequences, then stop.
reroute.pyis advisory. Onlypassport.py add-back-edge --authorization-id <user-record>may write a real back-edge, and only after the user authorizes that exact route.- A back-edge must go to an earlier stage (
to < from). The 2⊣3 data feasibility result is anadmission_hold, not a back-edge. - Never call a gate passed from prose. Use a producer's
light.findings.v1,run_checkpoint.py, its exit code, a fresh timestamp and a content hash. - Never collapse evidence states. Use only
VERIFIED,PLANNED,UNKNOWN,UNAVAILABLEorFAILED. - Never overwrite dirty/untracked user work, silently migrate a passport, silently rerun a stale downstream chain, or silently mark a limitation.
- Never put an overlay or engineering skill in the scientific DAG.
system-design,frontend-design,patent-disclosureandsoftware-copyrighthave no stage, findings,STAGE_GATES,ROUTESor scientific back-edge. - Never claim 23-skill delivery because files exist. Verify the live inventory, hashes, checkpoints, limitations, handoff and user delivery decision.
- Never silently install or reconfigure local runtimes. If a stage emits an
environment advisory such as
r_advisory.requires_user_choice=true, present the choices and consequences; only continue with install/config after an explicit user authorization. Non-interactive runs may choose the documented honest fallback only when the downstream contract does not require that runtime.
What ships with it
20 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.
- references/integration-contract.json 6.3 KB
- references/orchestrator-resource-map.md 6.7 KB
- references/passport.schema.json 2.4 KB
- resident/AGENTS.snippet.md 3.5 KB
- resident/CLAUDE.snippet.md 3.5 KB
- resident/INSTALL.md 7.9 KB
- resident/session_start_resident.py 14 KB runs code
- resident/settings.snippet.unix.json 325 B
- resident/settings.snippet.windows.json 338 B
- scripts/decision_checkpoint.py 3.7 KB runs code
- scripts/execution_mode.py 9.9 KB runs code
- scripts/integration_audit.py 15 KB runs code
- scripts/lifecycle.py 16 KB runs code
- scripts/passport.py 78 KB runs code
- scripts/reroute.py 25 KB runs code
- scripts/run_checkpoint.py 25 KB runs code
- scripts/workflow_ledger.py 22 KB runs code
- templates/passport.v3.yaml 452 B
- templates/task-profile.example.json 428 B
- templates/workflow-ledger.example.json 2.0 KB
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.
- 10d ago First seen · 355 lines · 141 tokens per session scan A 09b56864b413
light-orchestrator is a skill published in the GitHub repository Light0305/Light-skills (617 stars, last pushed 2mo ago), licensed MIT. It adds 141 tokens to every session and 3,824 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
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.
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.
paper-writing
Research paper writing assistant that enforces Arpit Gupta's editorial principles, voice profile, and writing workflow. MANDATORY TRIGGERS: Use this skill whenever the user mentions writing a paper, drafting a section, revising a section, editing a paper, reviewing a draft, rewriting an introduction, writing an…
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.
academic-paper-writing-skill
Evidence-first academic research workflow for topic ideation, scholarly search, paper reading, literature and systematic reviews, study and experiment design, statistics and data analysis, scientific figures, manuscript drafting and polishing, citation checks, peer review, rebuttals, submission packages, theses, and…