AutoMCM-Pro for opencode
RealSeaberry/AutoMCM-Pro
The opencode binding of the AutoMCM-Pro math modeling pipeline for CUMCM and MCM/ICM contests, with tool mappings, install prompts and checkpointed runs.
Coordinates and recovers multi-stage Light research projects from a single passport file, with checkpoints, stale-work tracking and rerouting only when you approve.
$ npx skills add Light0305/Light-skills --skill light-orchestrator -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Light0305/Light-skills light-orchestrator --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/Light0305/Light-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/light-orchestrator .claude/skills/light-orchestrator && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "light-orchestrator" agent skill from https://github.com/Light0305/Light-skills/tree/master/skills/light-orchestrator into .claude/skills/light-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "light-orchestrator", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/Light0305/Light-skills/tree/master/skills/light-orchestratorType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add Light0305/Light-skills --skill light-orchestrator -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Light0305/Light-skills light-orchestrator --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Light0305/Light-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/light-orchestrator .agents/skills/light-orchestrator && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "light-orchestrator" agent skill from https://github.com/Light0305/Light-skills/tree/master/skills/light-orchestrator into .agents/skills/light-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "light-orchestrator", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Light0305/Light-skills --skill light-orchestrator -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Light0305/Light-skills light-orchestrator --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Light0305/Light-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/light-orchestrator .cursor/skills/light-orchestrator && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "light-orchestrator" agent skill from https://github.com/Light0305/Light-skills/tree/master/skills/light-orchestrator into .cursor/skills/light-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "light-orchestrator", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/Light0305/Light-skills.git --path skills/light-orchestrator--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add Light0305/Light-skills --skill light-orchestrator -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Light0305/Light-skills light-orchestrator --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Light0305/Light-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/light-orchestrator .gemini/skills/light-orchestrator && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "light-orchestrator" agent skill from https://github.com/Light0305/Light-skills/tree/master/skills/light-orchestrator into .gemini/skills/light-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "light-orchestrator", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install Light0305/Light-skills light-orchestratorInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add Light0305/Light-skills --skill light-orchestrator -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Light0305/Light-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/light-orchestrator .github/skills/light-orchestrator && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "light-orchestrator" agent skill from https://github.com/Light0305/Light-skills/tree/master/skills/light-orchestrator into .github/skills/light-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "light-orchestrator", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Light0305/Light-skills --skill light-orchestrator -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Light0305/Light-skills light-orchestrator --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Light0305/Light-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/light-orchestrator .opencode/skills/light-orchestrator && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "light-orchestrator" agent skill from https://github.com/Light0305/Light-skills/tree/master/skills/light-orchestrator into .opencode/skills/light-orchestrator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "light-orchestrator", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
light-orchestratorCoordinates and recovers multi-stage Light research projects from a single passport file, with checkpoints, stale-work tracking and rerouting only when you approve.
This skill sits above the Light research stage skills, routing and recovering a project across stages 1 to 13 without doing the stage work itself. State lives in a canonical .light/passport.yaml, with checkpoints, findings, parallel joins, stale propagation and handoffs tracked around it. It is meant for new, resumed, partial, dirty, failed, stale or delivered projects, and for requests such as continue, resume, take over, checkpoint, reroute, recover or deliver.
Its boundaries are strict. The agent never picks the research direction, idea, plan, venue or final delivery for you: it presents a recommendation, evidence, alternatives and consequences, then stops. Reroute suggestions are advisory, and a real back-edge to an earlier stage is written only after you authorize that exact route. A gate counts as passed only from checkpoint output with an exit code, a fresh timestamp and a content hash, evidence is labeled VERIFIED, PLANNED, UNKNOWN, UNAVAILABLE or FAILED, and delivery is never declared just because files exist.
Overlay skills such as system-design, frontend-design, patent-disclosure and software-copyright stay out of the scientific stage graph. The agent also avoids overwriting dirty user work, silently migrating a passport, or installing local runtimes without your authorization.
8 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 6b44f57. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships 8 files in scripts/ (Python, from the files we listed), which the agent can run.
Shell commands in SKILL.md call:
pythonFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Light Research Orchestrator loads about 3.8k tokens when it runs, and up to ~7.7k if it reads all its reference files. Until then it costs about 177 tokens; SKILL.md has 1,479 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
The full file from Light0305/Light-skills at commit 6b44f57, republished under its MIT licence (© Light0305). 1,479 words, ~3,774 tokens.
.claude/skills/light-orchestrator/SKILL.md (or your agent's skills folder). This skill also uses 21 other files; get the full folder from GitHub.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.
reroute.py is advisory. Only
passport.py add-back-edge --authorization-id <user-record> may write a
real back-edge, and only after the user authorizes that exact route.to < from). The 2⊣3 data
feasibility result is an admission_hold, not a back-edge.light.findings.v1, run_checkpoint.py, its exit code, a fresh timestamp
and a content hash.VERIFIED, PLANNED, UNKNOWN,
UNAVAILABLE or FAILED.system-design, frontend-design, patent-disclosure and
software-copyright have no stage, findings, STAGE_GATES, ROUTES or
scientific back-edge.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.Start with:
python scripts/lifecycle.py intake --root <project-root>Act on the primary state:
| State | Required behavior |
|---|---|
new | inspect scope; propose only needed stages; ask at strategic choices |
resume | trust passport/hash/handoff over chat memory; continue next_action |
partial | preserve delivered stages; choose the next dependency-ready node |
dirty | inventory user changes; do not mutate until they are protected |
failed | inspect blocking evidence; run checkpoint/reroute; stop for user |
stale | show the propagated reverify set; rerun only after scope is clear |
delivered | verify the full delivery package; ask the user to accept/deliver |
If multiple flags coexist, treat dirty work as a mutation blocker and failed evidence as a progression blocker. Do not hide either behind the primary label.
.light/passport.yaml is the canonical pipeline state. memory-pm owns project
facts, decisions, versions and handoff history around it; a handoff is only a
hash-bound pointer.
light.passport.v3.references/passport.schema.json.templates/passport.v3.yaml.state_hash: SHA-256 of canonical state excluding the hash field.inputs_fingerprint: path + file bytes, not mtime.state_revision: increments on every v3 save.For an older passport:
python scripts/passport.py migrate --file .light/passport.yaml
# inspect the dry-run and UNKNOWN legacy authorization
python scripts/passport.py migrate --file .light/passport.yaml --writeDo not run --write until the user authorizes ledger migration. Migration may
mark a legacy back-edge authorization UNKNOWN; it must not invent proof.
1 literature-search → 2 data-engineering and 3 idea-generation → 4 idea-critique → 5 research-plan → 6 experiment-coding → 7 result-analysis.
Stage 7 forks to 8 paper-writing and 9 figure. Stage 8 also feeds 9 figure and 10 citation. Stages 9 and 10 join at 11 typesetting → 12 venue-matching → 13 review-rebuttal.
Dependencies are forward DAG edges. Parallel branches must declare
depends_on; a join waits for every required branch. Do not treat list order
as dependency order.
Invoke frontend-design or system-design only when the project needs a UI or software architecture. Their outputs may be referenced by the project, but they remain off-DAG and do not produce scientific findings.
Invoke patent-disclosure or software-copyright only when the user explicitly needs IP/material handoff from a real project. They prepare review materials, not legal advice, filings, registration guarantees or scientific findings.
先选择最小充分执行模式,不要把每个任务都升级成多 agent 编排:
python scripts/execution_mode.py --input task-profile.json输出 light.execution_mode.v1,只做决策、不执行任务:
{
"complexity": "complex",
"path_predictable": false,
"subtasks_independent": true,
"clear_evaluator": true,
"requirements_complete": true,
"user_decision_needed": false,
"distinct_categories": 3,
"budget_allows_parallel": true,
"iterative_improvement": false,
"human_checkpoints": ["高成本执行前", "最终交付前"]
}布尔字段必须是真正的 JSON true/false;若启用 iterative_improvement,还必须给
max_iterations >= 1,避免无界 evaluator loop。
direct:单步、低风险、无依赖;fixed_workflow:依赖顺序稳定;routed:需先分类再分流;parallel:独立分支且预算允许;orchestrated:动态依赖、需共享状态协调;evaluator_loop:有明确验收器,且预算允许修订。缺少会改变路线的必填信息时返回 UNRESOLVED,并只问一个最有信息量的问题;用户已给足信息时不得为了“互动感”重复询问。
高风险、付费、远程、发布、投稿、删除或不可逆动作先生成 light.decision.v1,再过授权门:
python scripts/decision_checkpoint.py --input decision.jsonexecution_mode.py enforces this rule by data, not by caller goodwill: even
when user_decision_needed=false, a task profile that declares remote
execution, paid resources, external writes, publish/submit, delete/overwrite
of user work, irreversible action, high risk, private/legal/ethics-sensitive
scope, or positive cost must return UNRESOLVED unless it carries a passing
light.decision_checkpoint.v1 with an authorization ID. A single boolean must
not bypass the authorization gate. Use
templates/task-profile.example.json as the fail-closed example.
PROPOSED 返回 UNRESOLVED 并给出唯一关键问题;只有 AUTHORIZED 且授权主体、scope
与风险规则一致才返回 allowed=true。REJECTED/REVOKED/EXPIRED 一律拒绝执行。
本门只核授权,不替用户执行动作。
多任务、并行分支、人工暂停或断点恢复还必须过 workflow ledger:
python scripts/workflow_ledger.py --input templates/workflow-ledger.example.jsonlight.workflow.ledger.v1 核对 owner/context scope、依赖闭包、join 是否提前、任务证据、
真实 sha256:<64hex>、独立验证包、HITL decision id/scope/question/options/expiry、max_attempts、terminal retry 以及
resume_snapshot.workflow_digest/task_id/visit_count/state_hash。它只给 runnable/waiting/failed
集合,不执行任务。快照与当前 workflow digest 不同、重试预算耗尽仍 RUNNING、依赖未完成却
启动 join、等待用户却没有具体问题与至少两个带后果说明的选项,均为 FAIL;WAITING_USER
只有在问题/选项完整时才保持 UNRESOLVED,不得自动代答。
SUCCEEDED 不能只靠 owner 自填 completion_status=PASS。它还必须带
verification.status=PASS、与 owner 不同的 verifier_id、方法、带时区且不在未来的
checked_at、验证报告路径/哈希,以及与当前 evidence_artifacts 完全相同的
subject_sha256s。方法只允许 machine_gate、independent_review 或 human_review;
后者还必须绑定 authorization_id。这只证明“当前哈希版本被一个可定位的验证步骤检查过”,
不证明内容必然正确;机器门优先,agent 自评不得冒充独立验证。
Run the stage's producer first. Then preview:
python scripts/run_checkpoint.py \
--file .light/passport.yaml --stage <1-13> \
--findings <producer-findings.json> --ts <ISO-8601>Inspect report, exit code, producer, target, inner gate findings and expected stage contract. After the user authorizes the ledger write:
python scripts/run_checkpoint.py \
--file .light/passport.yaml --stage <stage> \
--findings <producer-findings.json> --ts <ISO-8601> --write--write without --ts is invalid. A critical finding returns exit 1,
records FAILED, and blocks progression. PASS/WARN records VERIFIED; WARN
still remains visible.
STAGE_GATES has entries for 2–11 except 12, plus 13. Stage 1 produces search
signals for downstream consumption. Stage 12 is a user decision packet, not a
confirmation gate.
Stage 3 must aggregate idea-generation's idea_genealogy and
innovation_engine critical findings before the warn-only collision/diversity
signals; anti-collage failures cannot be bypassed by sending the candidate
straight to idea-critique.
On a failed checkpoint:
python scripts/reroute.py \
--findings <failed-findings.json> --stage <source-stage> \
--passport .light/passport.yamlInterpret actions:
rework: a legal earlier-stage back-edge;admission_hold: stop entry to stage 3; do not write an edge;known_limitation: revision budget is exhausted; ask whether to record it;manual: the signal cannot be mapped without human judgment.Present:
Only after the user's exact authorization:
python scripts/passport.py add-back-edge \
--file .light/passport.yaml --from <source> --to <earlier-target> \
--root-cause "<evidence-backed cause>" \
--evidence-ptr "<producer:gate@locator>" \
--authorization-id "<user-message-or-decision-id>"The command increments the target's durable revision_rounds. The limit is
two. A third attempt fails; do not reset the count across sessions or replace
it with a fresh passport.
Canonical suggestions are 4→3, 7→6, 7→5, 8→7, 9→7, and 13→3/5/8. An 8→6 route is a user root-cause override, not an automatic route.
python scripts/passport.py stale-check --file .light/passport.yaml --root <project>
python scripts/lifecycle.py handoff --root <project> --out <handoff.json>
python scripts/lifecycle.py verify-handoff --root <project> --handoff <handoff.json>An upstream byte change stales dependent stages. It does not automatically
invalidate independent parallel siblings. A changed passport hash invalidates
the old handoff. verify-handoff also checks the recorded project root,
timezone-bearing generated_at and the live intake snapshot
(intake_state, next_action, blockers, need_reverify,
known limitations and evidence state). A file-only stale change or new dirty
work therefore invalidates an old handoff even when the passport hash did not
change. Resume from the reported next action only after failed/dirty/stale
blockers are visible.
Claude Code's SessionStart hook injects red lines plus the resume report. Codex/OpenCode consume the same memory-pm resume implementation through their instruction files, but their trigger is model-read rather than harness-forced. Do not claim identical automatic behavior.
The hook budgets 4,200 characters for discipline and 5,400 for state. On
overflow it truncates to a pointer to this skill or the canonical passport. If
memory-pm cannot load, it emits UNAVAILABLE and the manual resume command.
Before asking the user to accept delivery:
integration_audit.py and confirm 23 roles/stages/gates/routes;workflow_ledger.py,确认 retry budget、snapshot compatibility、
parallel join、完成态独立验证绑定和 WAITING_USER 状态;UNKNOWN, UNAVAILABLE, FAILED, stale stage and known
limitation;Do not convert PLANNED to VERIFIED, waive a failed gate, exceed the revision
budget, or finalize delivery without the user's decision.
After explicit acceptance, record it mechanically:
python scripts/passport.py authorize-delivery \
--file .light/passport.yaml --root <project> \
--authorization-id <user-record> \
--known-limitation "<accepted limitation>"The command refuses non-delivered/non-VERIFIED stages and stale/incomplete
artifacts. It is the only supported transition to delivery_status=DELIVERED.
© Light0305, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 21 other files (scripts, references) in skills/light-orchestrator of Light0305/Light-skills.
Open the folder on GitHubat commit 6b44f57
Light Research Orchestrator next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Light Research Orchestrator this skillLight0305/Light-skills | 641 | — | ~3.8k | Automated safety check: Pass | MIT | |
| AutoMCM-Pro for opencodeRealSeaberry/AutoMCM-Pro | 257 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Show Me Your Work Decision Logcursor/plugins | 10k | 9 repos | ~1.6k | Automated safety check: Pass | None | |
| PUA High-Agency Governancetanweai/pua | 20k | — | ~502 | Automated safety check: Pass | MIT | |
| Cline Pilotsickn33/agentic-awesome-skills | 47k | 1 repos | ~4.6k | Automated safety check: Pass | MIT | |
| PUA High-Agency Governance for Traetanweai/pua | 20k | — | ~878 | Automated safety check: Pass | MIT |
RealSeaberry/AutoMCM-Pro
The opencode binding of the AutoMCM-Pro math modeling pipeline for CUMCM and MCM/ICM contests, with tool mappings, install prompts and checkpointed runs.
cursor/plugins
Keeps a TSV decision log for long or unattended agent runs, one row per decision with what, why, evidence and result, so a reviewer can check the work later.
tanweai/pua
Pushes an agent to keep verifying and changing approach after repeated failures, using a diagnosis line, evidence-based completion and confirmation before risky edits.
sickn33/agentic-awesome-skills
Acts as a proxy for the Cline CLI, dispatching coding tasks one at a time, monitoring runs by hard evidence, relaying decisions to you and learning per-project preferences.
tanweai/pua
An instruction-only discipline for Trae that forces evidence-based work when an agent keeps failing, gives up or declares a task finished without proof.
adand-91/gpt-6-astra-skill
Takes over one selected project on request, reports progress in a fixed Chinese-language format, and runs bounded reviews, handoffs and verifications from explicit sources.
Light0305/Light-skills
Verifies that every reference in a manuscript is real, correctly identified and actually supports its claim, and produces a citation registry for typesetting.
Light0305/Light-skills
Builds an evidence-backed invention disclosure packet from a project or research result for attorney or patent-agent review, without giving legal advice.
Light0305/Light-skills
Audits, scaffolds and safely migrates research project folder structures, keeping existing repositories read-only until you approve exact moves from a plan.
Light0305/Light-skills
Prepares draft materials for a China software copyright registration from a real project: application worksheet, source deposit plan, operation manual and consistency checks.
Light0305/Light-skills
Evidence-based workflow for designing or modernizing a software system: current-state inventory, options, API and schema contracts, migration plans, ADRs and verification.
Light0305/Light-skills
Build and preflight submission-ready LaTeX/PDF artifacts for Light stage 11.
Categories
Coordinates and recovers multi-stage Light research projects from a single passport file, with checkpoints, stale-work tracking and rerouting only when you approve. This skill sits above the Light research stage skills, routing and recovering a project across stages 1 to 13 without doing the stage work itself.yaml, with checkpoints, findings, parallel joins, stale propagation and handoffs tracked around it.
Light Research Orchestrator fits situations like: resuming or taking over a partially finished Light research project; recovering a failed or stale project and checking what must be rerun; work that crosses two or more Light research stages; verifying a research project before declaring delivery.
Run `npx skills add Light0305/Light-skills --skill light-orchestrator -a claude-code`. Or copy the skill folder (skills/light-orchestrator in Light0305/Light-skills) into .claude/skills/light-orchestrator in your project. Claude Code loads it when a task matches its description.
Run `npx skills add Light0305/Light-skills --skill light-orchestrator -a codex`. Or copy the skill folder (skills/light-orchestrator in Light0305/Light-skills) into .agents/skills/light-orchestrator in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Light0305/Light-skills --skill light-orchestrator -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/light-orchestrator, .gemini/skills/light-orchestrator, .github/skills/light-orchestrator and .opencode/skills/light-orchestrator in your project.
Going by SKILL.md and its folder, Light Research Orchestrator needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: The Light research stage skills it coordinates.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Light Research Orchestrator is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.8k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.9k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Light Research Orchestrator: AutoMCM-Pro for opencode (RealSeaberry/AutoMCM-Pro, 257 stars), Show Me Your Work Decision Log (cursor/plugins, 10k stars), PUA High-Agency Governance (tanweai/pua, 20k stars) and Cline Pilot (sickn33/agentic-awesome-skills, 47k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Light0305 (a GitHub user) maintains it in Light0305/Light-skills, which has 641 GitHub stars. The repository holds 23 skills in this directory. The repository was last updated on July 6, 2026.
Source: Light0305/Light-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.