What does this skill do, and when should you use it?
Infinite Gratitude is a Claude Code skill from Washin Village's AI Dojo series for multi-agent parallel research. It splits a topic into up to 5 research directions (GitHub projects, HuggingFace models, papers, competitors, best practices), dispatches up to 10 background agents to investigate them, compiles the findings into a structured report, and loops on follow-up questions until you stop it. The README documents one real case: researching an AI system to recognize the village's 28 cats and dogs, where 10 parallel agents covered 12 topics and produced 9 reports in 30 minutes versus an estimated 20+ hours manually, with the resulting system reaching 77.6% accuracy. It suits solo developers and small teams who need broad multi-angle research quickly and accept haiku-model economics.
Takes a topic plus optional flags (--depth quick/normal/deep, --agents 1-10, and --waves mentioned in the README); splits the topic into up to 5 research directions; dispatches agents in parallel via the Task API as research-scout subagents on the haiku model with run_in_background; collects and compiles their findings into a structured report; asks whether to continue if new questions emerged and loops back to dispatch (deep mode loops until satisfied); caps 5 agents per wave. Install by placing infinite-gratitude.skill.md into ~/.claude/skills/, then invoke with /infinite-gratitude "topic" in Claude Code.
- A solo developer comparing many tools or libraries (vector databases, embedding models) before committing to a stack, without researching each by hand.
- A founder or PM running competitor analysis: each agent digs into a different competitor, results rolled into a comparison report.
- An engineer doing a fast literature scan: parallel paper reading and summarization, then targeted follow-ups on key findings.
- Market research: scan one industry topic from project, model, paper, competitor and best-practice angles simultaneously.
- Due diligence: multi-angle background checks on a project or company.
How do you install this skill?
- Dispatching up to 10 background agents incurs API cost and rate usage; confirm budget beforehand and manually bound deep-mode loop counts.
- All research findings and performance figures (e.g., 77.6% accuracy) are author self-reports, independently unverified; validate before relying on them.
- SKILL.md and README disagree on defaults (5 vs 10 agents) and the --waves parameter; actual behavior may vary.
- Core function depends on GitHub/HuggingFace reachability, which is not guaranteed from mainland-China networks.
- The referenced references/agent-config.md was not present in the reviewed evidence and could not be verified.
- Network access
Claude Code Task/subagent APIhaiku model access
The README gives a one-command install: curl -sSL https://raw.githubusercontent.com/sstklen/infinite-gratitude/main/infinite-gratitude.skill.md -o ~/.claude/skills/infinite-gratitude.skill.md, i.e. download the skill file into ~/.claude/skills/. The repo also ships SKILL.md and references/agent-config.md (agent configuration). No further dependency setup is documented.
git clone --depth 1 https://github.com/sstklen/infinite-gratitude.git ~/.claude/skills/infinite-gratitudeGenerated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- your research topic
- pet AI recognition
- RAG best practices
- React state management
In Claude Code, run /infinite-gratitude "your research topic". Options: --depth quick|normal|deep (default normal; deep loops until satisfied), --agents 1-10 (default 5, max 10), and --waves 3 per the README (iterations, default 3). Agents research in parallel, return a report, and ask whether to continue. SKILL.md examples: /infinite-gratitude "pet AI recognition", /infinite-gratitude "RAG best practices" --depth deep, /infinite-gratitude "React state management" --agents 3.
What are this skill's strengths and limitations?
- Parallel multi-agent research: the README's real case finished an estimated 20+ hour manual research effort in 30 minutes with 9 traceable reports.
- Loop mechanism: each report surfaces new questions and asks the user before the next wave; deep mode digs until satisfied.
- Cost-conscious by design — defaults to haiku, max 5 agents per wave; depth, agent count and waves are configurable.
- One-command install, trilingual docs (EN/中文/日本語), and an agent-config reference file.
- Hard dependency on Claude Code's Task subagent API, the research-scout subagent type and slash-command behavior — not portable to other runtimes without rework.
- Internal inconsistency: SKILL.md says default 5 directions and max 5 agents per wave while the README advertises 10 parallel agents; actual parallelism depends on the Claude Code environment.
- Output quality depends on the underlying model's research ability; no independent benchmarks or test suite are provided beyond the single pet-recognition case study.
- The --waves flag appears only in the README, not the SKILL.md parameter table — some behavior details are under-documented.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Infinite Gratitude this page | 49 · Use with care | ★ 31 | 7mo ago | MIT |
| do — Feature Development Orchestrator | 47 · Use with care | ★ 2.8k | 5mo ago | AGPL-3.0 |
| OmO Multi-Agent Orchestrator | 46 · Use with care | ★ 2.8k | 5mo ago | AGPL-3.0 |
| Dev End-to-End Development Workflow | 43 · Not recommended | ★ 2.8k | 5mo ago | AGPL-3.0 |
| Loki Checkpoint Mode | 68 · Recommended | ★ 1.1k | 16d ago | NOASSERTION |
The repo explicitly positions this skill against its sibling research-scout (single-agent research): use Infinite Gratitude for broad parallel deep-dives, research-scout for lightweight single-agent lookups. It also recommends pairing with the author's yes.md (safety gates and evidence rules) and 5x-cto (post-research development pipeline).
How did FollowSkills review this skill?
The skill handles no sensitive data, no credentials, no external writes; it asks 'Continue? (y/n)' before each loop and its data flow (web research and reporting only) is transparent. Deductions: dispatching up to 10 background agents on haiku has cost/rate implications without explicit caps, rollback or recovery guidance; publisher identity is unverified and attribution points only to washinmura.jp.
The happy path (split directions, dispatch, compile, loop) is self-consistent with a clear parameter table. Deductions: static review cannot execute reproduction; no error handling or failure feedback for agent failures, missing research-scout subagent type, or out-of-range parameters; loop termination ('until satisfied') is vague.
Target scenarios (technical research, competitor analysis, literature review) are clear, with trilingual docs and explicit triggers. Deductions: no declared non-fit boundaries; inconsistency between SKILL.md (5 directions, 5 default agents) and README (10 agents, --waves flag); core function depends on GitHub/HuggingFace reachability, uncertain from mainland China; referenced references/agent-config.md is not present in evidence.
Docs are well structured with parameter table, examples, notes, MIT license and trilingual README. Deductions: no versioning or changelog; the referenced agent-config.md cannot be verified; maintenance ownership and update path rest on personal branding only.
The multi-agent parallel research concept plausibly completes the task with directly usable structured reports. Deductions: static review cannot verify actual output quality; claimed benefits (77.6% accuracy, 30 min vs 20+ hours) are author self-reports without independent evidence; the data-volume-to-accuracy formula has no cited source, so marginal-value evidence is limited (static cap is 7 in any case).
Only author-side claims: story documents and sample outputs; no test suite, no CI evidence, no third-party reproduction material; fictional narrative (cat/dog characters) and factual claims are mixed without clear separation, and key conclusions cannot be independently verified.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →