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 agentmods add commands/justinjdev/fellowship/chroniclegit clone --depth 1 https://github.com/justinjdev/fellowshipWhat 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 | $0.00038 | $0.01036 |
| Opus 5 | $0.00019 | $0.00518 |
| Sonnet 5 | $0.00008 | $0.00207 |
| Haiku 4.5 | $0.00004 | $0.00104 |
Grade A, and why
chronicle 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 2d 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chronicle — Bootstrap Conventions From Your Codebase
Overview
Interactive skill that walks through your codebase and extracts the implicit conventions into explicit, AI-readable rules. Produces a ## Reference Files section and a ## Review Conventions section for your CLAUDE.md.
This is a one-time setup skill. After running it, conventions are maintained incrementally through PR feedback capture.
Process
Step 1: Understand the Codebase Shape
Ask the user:
- "What kind of codebase is this?" (monorepo, single service, library, etc.)
- "What areas do you work in most often?"
- "Who are your primary code reviewers?"
Then explore the directory structure. Identify:
- Where source code lives
- How it's organized (by feature, by layer, by domain)
- What test patterns exist
- Config files that enforce standards (linters, formatters, CI checks)
Step 2: Find Reference Files
For each area the user works in, ask:
"Point me to 1-2 files in [area] that you know passed review cleanly — files your reviewer would consider 'the right way to do it.' If you're not sure, point me to the most recently merged PR in this area and I'll look at what was approved."
If the user can't identify reference files, help them:
- Look at recent merged PRs:
git log --oneline --merges -20 - Find files with few review iterations
- Ask: "Which files does your reviewer point to when they say 'do it like X'?"
For each reference file identified, record:
### [Category]: [file path]
- Approved by: [reviewer, if known]
- Good example of: [what pattern this demonstrates]
- Last updated: [date of last significant change]
Step 3: Extract Conventions by Comparison
Read 3-5 reference files across different areas. For each one, extract observable patterns in these categories:
Structure & Organization
- File layout (imports, types, constants, logic, exports)
- Naming conventions (files, functions, variables, types)
- Module/package organization
Architecture & Patterns
- How dependencies are accessed (DI, imports, singletons, context)
- Error handling (custom types, propagation style, recovery)
- Data flow (layers, where validation happens, where transformations happen)
- What abstractions are used (and which are NOT — equally important)
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.
- 2d ago First seen · 110 lines · 38 tokens per session scan A b78677dc35c2
chronicle is a command published in the GitHub repository justinjdev/fellowship (5 stars, last pushed 19d ago), licensed Apache-2.0. It adds 38 tokens to every session and 1,036 once invoked, about $0.0002 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 2026-08-31.
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.