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.
git clone --depth 1 https://github.com/justinjdev/fellowshipWrote 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/commands/justinjdev/fellowship/guide)<a href="https://agentmods.dev/commands/justinjdev/fellowship/guide"><img src="https://agentmods.dev/badge/commands/justinjdev/fellowship/guide/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/commands/justinjdev/fellowship/guide"><img src="https://agentmods.dev/badge/commands/justinjdev/fellowship/guide.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00027 | $0.01334 |
| Opus 5.5 | $0.00011 | $0.00534 |
| Sonnet 5.5 | $0.00005 | $0.00267 |
| Haiku 4.5 | $0.00003 | $0.00133 |
Grade A, and why
guide 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 1mo 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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fellowship Guide — Learn by Doing
Overview
This guide teaches fellowship by running a real task on the user's codebase. It does NOT explain concepts upfront — it demonstrates them, then names them afterward.
Do not mention gates, lembas, history, worktree, balrog, warden, council, palantir, events, health, todos, groups, or scouts until Act 3 (Graduation). Use plain language throughout Acts 1–2.
Act 1: Pitch
Present this to the user:
Fellowship helps Claude work through complex tasks more carefully — researching before planning, planning before coding. Each step has a checkpoint where you review the work before moving on.
Let's try it on something real in your codebase.
Then ask using AskUserQuestion:
What would you like to work on?
Pick something real — a bug to fix, a feature to add, or a refactor to do. It should be non-trivial (more than a one-line change) but completable in a single session.
Wait for their response. This is the task description used for the rest of the guide.
Act 2: Guided Quest
Setup
- Determine the current branch:
git branch --show-current - Create a new branch:
git checkout -b guide/<slug>where<slug>is a short kebab-case summary of the task (e.g.,guide/fix-auth-redirect) - Read the project's CLAUDE.md (if it exists) to understand conventions and reference files. Do not present this to the user — absorb it silently.
Stage 1: Research
Goal: Understand the relevant parts of the codebase before making a plan.
- Use the Explore agent (Agent tool with subagent_type=Explore, passing
model: "haiku"— ormodels.explorefrom fellowship config if set) to find files related to the task. Focus on:- Files that will likely need modification
- Files that define patterns to follow
- Test files for the affected area
- Types, configs, or dependencies that constrain the work
- If CLAUDE.md lists reference files for the relevant area, read them and note the patterns (naming, error handling, structure, data flow). Do not present the raw pattern analysis — internalize it.
- Present a structured research summary to the user:
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.
- 1mo ago Changed 7100d4abd481
- 1mo ago First seen · 144 lines · 27 tokens per session scan A ad0f5511a071
guide is a command published in the GitHub repository justinjdev/fellowship (5 stars, last pushed 13d ago), licensed Apache-2.0. It adds 27 tokens to every session and 1,334 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
Other commands, from other repositories
auto-mode
Idea-to-running-code lifecycle orchestration. 10-phase pipeline with 5 hard decision gates, wave-based parallelism, and STATE.json resumability. Composes /deep-research, /auto-swarm-nth, /production-upgrade, /security-audit, and /ship into a single end-to-end flow.
max-research
Nuclear-scale autonomous research — deploys 500-1000 agents in ONE massive simultaneous wave for exhaustive topic saturation. Deep-research methodology × auto-swarm scale = maximum parallel intelligence. WARNING: Extreme resource consumption.
omni-plan
ProductionOS flagship — 13-step orchestrative pipeline with tri-tiered evaluation, recursive convergence, CEO/Eng/Design review chain, CLEAR framework evaluation, multi-model judge tribunal, and autonomous PIVOT/REFINE/PROCEED decisions. Targets 100% production-ready output.
auto-swarm-nth
Nth-iteration agent swarm — spawns parallel agent waves, evaluates strictly per wave, re-swarms gaps until 100% coverage and 10/10 quality. Can invoke any ProductionOS skill or command within waves.
frontend-upgrade
Full-stack frontend upgrade pipeline — fuses /production-upgrade iterative audit with /plan-ceo-review vision and /plan-eng-review rigor. Deploys parallel auto-swarm agents for iterative audit and execution. Enriched with /deep-research for competitive parity.
omni-plan-nth
Nth-iteration omni-plan — recursive orchestration that chains ALL ProductionOS skills and agents, evaluates strictly per iteration, and loops until 10/10 is achieved. Each iteration can invoke any command or skill in the system.