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 ladiossoop5star/open_magi --skill magigit clone --depth 1 https://github.com/ladiossoop5star/open_magiWrote 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/ladiossoop5star/open_magi/magi)<a href="https://agentmods.dev/skills/ladiossoop5star/open_magi/magi"><img src="https://agentmods.dev/badge/skills/ladiossoop5star/open_magi/magi.svg" alt="Measured on agentmods" 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 | $0.00031 | $0.04248 |
| Opus 5 | $0.00015 | $0.02124 |
| Sonnet 5 | $0.00006 | $0.00850 |
| Haiku 4.5 | $0.00003 | $0.00425 |
Grade A, and why
magi 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 7d 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 — 390 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Magi
Overview
Run a coding-agent proposal-first deliberation loop. The main agent owns decisions, edits, verification, commits, rollback, and final reporting. Three read-only sub-agents only research and report.
Core rule: completion requires explicit acceptanceCriteria and
verificationCommands, not confidence, plus an approved adversarial review of
the actual diff before final-report.md.
Proposal-first rule: before any fix direction is selected, the main agent prepares an evidence packet and does not propose a fix. The deliberators propose directions first; the main agent selects one, then they review it before execution.
Council modes: the council runs in three modes sharing one launch and report
mechanism. recon (round 1 Phase 1b) gathers evidence in parallel before the
main agent commits to a direction. decision (Phases 2-4) selects the
direction proposal-first. review (Phase 6) adversarially reviews the actual
diff before completion. Track the active mode in state.json currentCouncilMode.
Required Reference Loading
These files are part of the skill contract. Load the listed reference before acting in that situation:
| Situation | Required reference |
|---|---|
| Starting or resuming Magi | references/protocol.md |
Creating checklist.md or changing phase |
references/checklist-template.md |
| Writing prompts, reports, synthesis, or verdict | references/deliberation.md |
| Launching subagents or handling runtime adapter behavior | references/runtime.md |
| Before any user-facing question | references/question-firewall.md |
| Executing changes, verification, checkpoint, rollback, or next-round evidence | references/execution-and-verification.md |
| Plugin repair, corrupt state, timeout, or repeated failure | references/troubleshooting.md |
Do not rely on memory for phase transitions. Before every phase transition,
read .open_magi/magi-log/checklist.md.
When to Use
Use this skill when the user says start deliberation, magi, three sages,
deliberation loop, loop until done, or requests repeated research ->
synthesize -> act -> verify until completion.
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- prompts/balthasar.md 2.6 KB
- prompts/casper.md 2.6 KB
- prompts/melchior.md 2.6 KB
- references/checklist-template.md 6.6 KB
- references/deliberation.md 13 KB
- references/execution-and-verification.md 3.3 KB
- references/protocol.md 10 KB
- references/question-firewall.md 2.5 KB
- references/runtime.md 6.8 KB
- references/troubleshooting.md 3.1 KB
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.
- 7d ago First seen · 390 lines · 31 tokens per session scan A 630560ec88d9
magi is a skill published in the GitHub repository ladiossoop5star/open_magi (3 stars, last pushed 10d ago), licensed MIT. It adds 31 tokens to every session and 4,248 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.
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Escalate unresolvable or sensitive requests to a human agent by recording an escalation entry. Use when the user asks to speak to a human, the bot cannot answer confidently, the request involves financial, legal, or security concerns, a safety issue is detected, or the user is frustrated after repeated failures.
fabric-rlm
Recursively decomposes oversized tasks into bounded child Pi agents with fresh context windows. Use for whole-repo audits, massive-context analysis, and multi-file refactors that do not fit one context.
fabric-supervisor
Starts a persistent Pi Fabric supervisor that watches the main session toward a concrete goal and steers only when needed. Use for long-running goal supervision without another extension.