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 skills/xcrft/mastermind/mastermind-runtime-researchnpx skills add xcrft/mastermind --skill mastermind-runtime-researchgit clone --depth 1 https://github.com/xcrft/mastermindWhat 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 | $0.00065 | $0.00978 |
| Opus 5 | $0.00032 | $0.00489 |
| Sonnet 5 | $0.00013 | $0.00196 |
| Haiku 4.5 | $0.00006 | $0.00098 |
Grade A, and why
mastermind-runtime-research 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mastermind runtime research
[[mastermind-architecture-review]] reconstructs the runtime path and judges the design. This runs first and does something narrower: find who already depends on what you are about to change, and state where the graph stops being able to tell you. Feed both into the review.
Zero callers is not zero callers
Start here, because every other answer inherits it. The graph is syntactic, so whole classes of real invocation produce no edge at all:
- A queue, topic, or bus. A producer and its consumer are two separate static islands. There is no edge between them and there never will be.
- Framework registration. A route table, a DI container, a decorator-based dispatcher, or a config-declared handler calls code the graph never links.
- Reflection, dynamic dispatch, and interface indirection. The call lands on a name the source never spells.
- Cross-process and cross-language. A worker invoked by a scheduler, or a service reached over HTTP, is outside the index entirely.
So mmcg_callers returning nothing on a handler means no static caller was
found, not nothing calls this. Report it that way, and go find the
registration, the topic name, or the schedule by reading. A change that looks
unreferenced is the single most expensive thing to get wrong here.
Who depends on this today
mmcg_api_surface src/orders/ # symbols under a prefix used from OUTSIDE it
mmcg_callers <symbol> # static callers, with the caveat above
mmcg_impact <symbol> --depth 3 # transitive blast radius
mmcg_imported_by <symbol> # importing files, including barrels
mmcg_api_surface is the one most often skipped and the one that matters most:
it reports what the rest of the codebase actually reaches into, independent of
what is declared public. A module can export twenty symbols and have three real
consumers, or export none and still be reached through a re-export. Change the
three, not the twenty.
What ships with it
1 file 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.
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 · 101 lines · 65 tokens per session scan A e0a7befb39e4
mastermind-runtime-research is a skill published in the GitHub repository xcrft/mastermind (11 stars, last pushed 7d ago), licensed MIT. It adds 65 tokens to every session and 978 once invoked, about $0.0003 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 2026-08-30.
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orchestrate
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bonsai-ninja
Use bonsai-ninja as compiler-backed structural evidence when mapping a codebase, finding symbols, tracing behavior, inspecting dataflow, debugging across files, reviewing change impact, exporting graph facts, or running SAST.
task-splitting-evaluation
Recursive pre-implementation GitHub task splitting and evaluation flow. Use when the user wants Claude agents to evaluate unhandled tasks, skip already-processed tasks, mark easy leaves with detailed executor-ready comments, split broad tasks into GitHub subtasks, and keep recursing until every leaf is well described…
gh-task
Run a GitHub issue end-to-end in this repository using the committed isolated-worktree workflow. Use when the user invokes $gh-task, asks to run a GitHub task, or provides a GitHub issue number.
stacklit-navigator
Use stacklit.json to navigate codebases without burning tokens on file exploration.