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/mooreslaws/expert-mind-skill/rungit clone --depth 1 https://github.com/mooreslaws/expert-mind-skillWrote 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/mooreslaws/expert-mind-skill/run)<a href="https://agentmods.dev/commands/mooreslaws/expert-mind-skill/run"><img src="https://agentmods.dev/badge/commands/mooreslaws/expert-mind-skill/run.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 | $0.00025 | $0.00450 |
| Opus 5 | $0.00013 | $0.00225 |
| Sonnet 5 | $0.00005 | $0.00090 |
| Haiku 4.5 | $0.00003 | $0.00045 |
Grade A, and why
run 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 4d 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.
What it actually says
Run the pipeline
Trigger the full refresh cycle for one persona or all enabled personas.
Steps
1. Check pipeline readiness
First run python3 ${CLAUDE_PLUGIN_ROOT}/scripts/wizard_state.py --json and
read _pipeline_ready. If False, show _blockers and stop — point user at
/expert-mind-skill:init to resolve them.
2. Parse arguments
If invoked as /expert-mind-skill:run, run for all enabled personas.
If invoked as /expert-mind-skill:run <persona-id> (or multiple ids),
restrict to those.
3. Confirm
Show a one-line summary of what's about to happen:
Running pipeline:
<N> personas: <ids joined>
<M> sources enabled
AskUserQuestion: Run now / Cancel.
4. Execute
python3 ${CLAUDE_PLUGIN_ROOT}/scripts/orchestrator.py [persona-ids...]
Stream stdout to the chat. The orchestrator handles dispatch to each adapter script, then judge, then distill.
5. Summarize
After completion, read output/_evaluation.json and show the per-persona
table:
persona accepted/judged fullness applicability
<id> <a>/<b> <f> <app>
...
Flag any persona that:
- Dropped to
lowfullness (regression vs last run, if logs exist) - Has 0 frameworks or 0 voice_samples
- Hit
hard_cap_tokens
Flags (pass to orchestrator)
--force— re-scrape even if staging files exist--skip-ingest— only judge + distill on existing staging data--skip-judge— only distill on existing scored data--skip-distill— ingest + judge only, no rewrite of skill files
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.
- 4d ago First seen · 65 lines · 25 tokens per session scan A e8beeea0d3a2
run is a command published in the GitHub repository mooreslaws/expert-mind-skill (5 stars, last pushed 2mo ago), licensed MIT. It adds 25 tokens to every session and 450 once invoked, about $0.0001 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.