Skill
loop-converge
Author and optionally direct-launch a two-node loop-graph run whose North Star is code convergence — delete unused code, merge duplicates, reuse, slim. Use…
@levi-qiaoView SkillDevelopment
Skill
/loop-research
Author and optionally direct-launch a two-node loop-graph run that compares feasible technical approaches with open-source evidence, primary research, and controlled experiments before selecting one. Use when a decision needs several research and evaluation rounds with durable
Install
$ npx -y skills add levi-qiao/octopus-skill --skill loop-research --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/loop-research
Context preview
The summary Claude sees to decide when to auto-load this skill.
Author and optionally direct-launch a two-node loop-graph run that compares feasible technical approaches with open-source evidence, primary research, and controlled experiments before selecting one. Use when a decision needs several research and evaluation rounds with durable
SKILL.md
loop-research.SKILL.mdname: loop-research
description: Use only when the user explicitly invokes /loop-research or names this skill. Authors a loop-research run. Do not use unless named. Do not execute or resume generated runtime node files.
disable-model-invocation: true
loop-research — a loop-graph preset for evidence-led choices
A thin authoring entry. It binds a research-and-selection pack, starts the owner interview, then follows [`loop-graph`](../loop-graph/SKILL.md) to compile the normal executor, ledger, directives, ops, and supervisor artifacts. It is not a research node, a second runtime, or a second template set.
Fit check
- Use this when the approach is undecided and a decision needs comparative evidence from
open-source projects, primary research, and a controlled benchmark or A/B experiment.
- Use [`../loop-deliver/SKILL.md`](../loop-deliver/SKILL.md) once the approach is chosen,
or [`../loop-converge/SKILL.md`](../loop-converge/SKILL.md) for code consolidation.
- For a short answer, one source lookup, or non-comparative literature summary, use the
host's ordinary research task instead of a graph.
On invoke
1. Inspect the workspace, existing evidence, experiment harnesses, data policy, and current host the same way loop-graph does. Never ask which client this is when context already identifies it. 2. Read and bind [`preset.md`](preset.md). That pack is the North Star, supervisor requirement, interview, evidence shape, method guards, knob overrides, and artifact emphasis. Do not redesign them. 3. Start the owner interview immediately. Ask only the pack's unresolved choices — decision/scope, evidence budget and data authority, and launch — as recommended A/B (or A/B/C) choices. Do not ask the owner to invent evaluation criteria. 4. Read and follow [`../loop-graph/SKILL.md`](../loop-graph/SKILL.md) from **When called from a preset skill** through generate and deliver. Compile only from loop-graph's [`templates/`](../loop-graph/templates/). This skill never executes the generated nodes.
Read more
name: loop-research description: Use only when the user explicitly invokes /loop-research or names this skill. Authors a loop-research run. Do not use unless named. Do not execute or resume generated runtime node files. disable-model-invocation: true
loop-research — a loop-graph preset for evidence-led choices
A thin authoring entry. It binds a research-and-selection pack, starts the owner interview, then follows [`loop-graph`](../loop-graph/SKILL.md) to compile the normal executor, ledger, directives, ops, and supervisor artifacts. It is not a research node, a second runtime, or a second template set.
Fit check
- Use this when the approach is undecided and a decision needs comparative evidence from
open-source projects, primary research, and a controlled benchmark or A/B experiment.
- Use [`../loop-deliver/SKILL.md`](../loop-deliver/SKILL.md) once the approach is chosen,
or [`../loop-converge/SKILL.md`](../loop-converge/SKILL.md) for code consolidation.
- For a short answer, one source lookup, or non-comparative literature summary, use the
host's ordinary research task instead of a graph.
On invoke
1. Inspect the workspace, existing evidence, experiment harnesses, data policy, and current host the same way loop-graph does. Never ask which client this is when context already identifies it. 2. Read and bind [`preset.md`](preset.md). That pack is the North Star, supervisor requirement, interview, evidence shape, method guards, knob overrides, and artifact emphasis. Do not redesign them. 3. Start the owner interview immediately. Ask only the pack's unresolved choices — decision/scope, evidence budget and data authority, and launch — as recommended A/B (or A/B/C) choices. Do not ask the owner to invent evaluation criteria. 4. Read and follow [`../loop-graph/SKILL.md`](../loop-graph/SKILL.md) from **When called from a preset skill** through generate and deliver. Compile only from loop-graph's [`templates/`](../loop-graph/templates/). This skill never executes the generated nodes.
Ships withlevi-qiao-octopus-skill
Long-horizon agent skill for Claude Code, Cursor, Codex & Grok Build. Stop agent drift with a durable ledger, a clean-context supervisor, and verified gates.
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