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 skills add wrg32786/aigent-os --skill first-10-movesgit clone --depth 1 https://github.com/wrg32786/aigent-osWrote 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/skills/wrg32786/aigent-os/first-10-moves)<a href="https://agentmods.dev/skills/wrg32786/aigent-os/first-10-moves"><img src="https://agentmods.dev/badge/skills/wrg32786/aigent-os/first-10-moves/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/skills/wrg32786/aigent-os/first-10-moves"><img src="https://agentmods.dev/badge/skills/wrg32786/aigent-os/first-10-moves.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00057 | $0.01057 |
| Opus 5.5 | $0.00023 | $0.00423 |
| Sonnet 5.5 | $0.00011 | $0.00211 |
| Haiku 4.5 | $0.00006 | $0.00106 |
Grade A, and why
First 10 Moves 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
First 10 Moves
When dropped into an unfamiliar codebase or asked to fix something in code you don't know — run this sequence before doing anything else. The principle: most "clarification" questions are answered by 5 minutes in the relevant file. If the answer is in the code, the docs, or the file tree — find it there before asking.
When to use
- Fresh in an unfamiliar codebase
- Asked to fix or change code in an area you haven't worked in
- Tempted to ask "where does X live?" or "how does Y work?"
- Triggered by Caddy on prompts like: "fix the X", "investigate", "trace this bug", "how does X work", "unfamiliar codebase", "where is X defined", "what calls Y", "navigate the code", "find the bug in"
The sequence (run top-to-bottom)
1. Re-read the user's request
Look for clues. "Fix" implies a bug. "The headline generator" implies a named module. Note any screenshots, errors, or examples included. The exact phrasing embeds most of the diagnosis — don't abstract it away.
2. Get the lay of the land (~30 seconds)
- List project root
- Identify main source directory
- Read
package.json/pyproject.toml/Cargo.toml(whatever the manifest is) - Read README if short
This rules out a lot. You learn the language, framework, build system, and entry point in 30 seconds.
3. Search for the keyword across the project
One ripgrep, filtered to relevant extensions. The matches will cluster around the real module.
4. Pick the most likely 2-3 matched files
Filenames are huge signal. aiHeadlineGenerator.ts beats headlineUtils.test.ts. Production code beats test code. Implementation beats type definitions.
5. Read the public surface of the top candidate
Imports + exports, not bodies. Shape first. What does this module take in and put out? What does it depend on?
6. Find the entry point — who calls this?
Search for the main exported function name. Call sites tell you how the module fits into the larger pipeline.
7. Read one upstream call site fully
Concrete example of "what inputs does this receive in real use?" That constrains everything downstream. Don't read 5 call sites — one is usually enough.
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 First seen · 87 lines · 57 tokens per session scan A 82234428f4b0
First 10 Moves is a skill published in the GitHub repository wrg32786/aigent-os (23 stars, last pushed yesterday), licensed MIT. It adds 57 tokens to every session and 1,057 once invoked, about $0.0002 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 2026-09-01.
Other skills, from other repositories
skillify-meta-loop
Use when a recurring bug, support question, client incident, or manual task surfaces 2+ times, when a codified rule keeps getting violated despite being written down, when a teammate or client hits an issue anyone could hit, or when delivery/setup friction repeats. Triggers: "skillify this", "this keeps happening"…
diagnose
Use when an AI Brain vault needs a health check or something feels off: Claude ignores CLAUDE.md, journal entries missing from /weekly, hooks not firing, something broke after a git pull, a friend's install misbehaves, Obsidian crashes or pegs the CPU, a .claude/worktrees folder appeared, Granola/WhatsApp/Slack/Gmail…
error-recovery
A guide to detecting and recovering from failures in long-running AI agents. It covers API outages, bad tool calls, broken conversation state, stalled streams, repeated loops, retries, fallback models, and circuit breakers that stop repeated failure.
coding-agent-harness
A guide to building the surrounding system that lets an AI agent safely work on code. This system provides tools, rules, tests, feedback, permissions, and ways to undo changes.
graphify
Use when the user invokes /graphify or asks to build, update, or query a knowledge graph from a folder of files (code, docs, papers, notes, images). Triggers: 'map this codebase', 'graph my notes', 'what is connected across these files', 'community detection', 'GraphRAG', incremental graph update, Neo4j/SVG/GraphML…
insights
Use when the user types /weekly or /monthly, asks for a weekly or monthly journal review or retrospective, asks 'how was my week' or 'how was my month', or wants trends, anomalies, or patterns across recent journal entries. Also fires when regenerating an insight report for a past week or month. NOT for writing…