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.
git clone --depth 1 https://github.com/mizukaizen/hive-doctrine-mcpWrote 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/mizukaizen/hive-doctrine-mcp/analyse)<a href="https://agentmods.dev/commands/mizukaizen/hive-doctrine-mcp/analyse"><img src="https://agentmods.dev/badge/commands/mizukaizen/hive-doctrine-mcp/analyse/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/commands/mizukaizen/hive-doctrine-mcp/analyse"><img src="https://agentmods.dev/badge/commands/mizukaizen/hive-doctrine-mcp/analyse.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00000 | $0.00457 |
| Opus 5 | $0.00000 | $0.00229 |
| Sonnet 5 | $0.00000 | $0.00091 |
| Haiku 4.5 | $0.00000 | $0.00046 |
Grade A, and why
analyse 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 9d 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/analyse
Run an analysis pipeline from data cleaning through hypothesis testing.
Usage
/analyse --data "<path>" --hypothesis "<hypothesis>" [--test "t-test|anova|correlation|regression"] [--alpha 0.05]
Behaviour
1. Pre-registration Check
Before any analysis, verify analysis/preregistration.md exists. If not, create one:
# Pre-registration
## Hypothesis
[From --hypothesis argument]
## Variables
- Independent: [identify from data]
- Dependent: [identify from data]
- Covariates: [if applicable]
## Planned Analysis
- Test: [from --test or inferred from hypothesis]
- Alpha: [from --alpha or 0.05]
- Correction: [if multiple tests planned]
- Minimum effect size of interest: [specify]
## Exclusion Criteria
- [Data-driven exclusions specified before analysis]
## Deviations
[Document any changes from this plan during analysis]
2. Data Cleaning
- Read the data file and report basic properties (rows, columns, types).
- Check for missing values — report percentage per variable.
- Check for duplicates.
- Check for outliers using IQR method — report but do not remove without confirmation.
- Create a processed data file with a processing log.
3. Exploratory Analysis
- Descriptive statistics for all variables (M, SD, range, or frequencies).
- Distribution checks (skewness, kurtosis).
- Correlation matrix for numeric variables.
- Flag any patterns that might affect the planned analysis.
- Clearly label all output as "EXPLORATORY — not pre-registered."
4. Hypothesis Testing
- Verify assumptions for the planned test.
- Run the pre-registered analysis.
- Report in APA format with effect size and confidence interval.
- Run sensitivity analysis if assumptions are violated.
5. Output
Save results to analysis/outputs/ with:
- Summary statistics table
- Test results in APA format
- Assumption check results
- Any figures described in sufficient detail for recreation
Display the key result and whether the hypothesis was supported.
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.
- 9d ago First seen · 73 lines · 0 tokens per session scan A 6d02414d170d
analyse is a command published in the GitHub repository mizukaizen/hive-doctrine-mcp (0 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 457 tokens. 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
mega-approve
Approve the mega-plan and start feature execution. Creates worktrees and generates PRDs for each feature. Usage: /plan-cascade:mega-approve [--flow ] [--tdd ] [--confirm] [--no-confirm] [--spec ] [--first-principles] [--max-questions N] [--auto-prd] [--agent ] [--prd-agent ] [--impl-agent ].
auto
AI auto strategy executor. Analyzes task and automatically selects and executes the best strategy: direct execution, hybrid-auto PRD generation, hybrid-worktree isolated development, or mega-plan multi-feature orchestration.
design-generate
Generate a technical design document. Auto-detects level: project-level from mega-plan.json, or feature-level from prd.json. Provides architectural context for story execution.
worktree
Start a new task in an isolated Git worktree for parallel multi-task development. Creates a task branch, worktree directory with planning files, and leaves the main directory untouched. Usage: /plan-cascade:worktree [task-name] [target-branch]. Example: /plan-cascade:worktree feature-login main.
design-import
Import an external design document (Markdown, JSON, or HTML from Confluence/Notion) and convert it to designdoc.json format for Plan Cascade integration.
design-review
Review and interactively edit the current designdoc.json. Displays the design document in a readable format and allows modifications to components, patterns, decisions, and story mappings.