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/nmime/motiv-buy/full-contextgit clone --depth 1 https://github.com/nmime/motiv-buyWrote 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/nmime/motiv-buy/full-context)<a href="https://agentmods.dev/commands/nmime/motiv-buy/full-context"><img src="https://agentmods.dev/badge/commands/nmime/motiv-buy/full-context.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 | $0.00000 | $0.01488 |
| Opus 5 | $0.00000 | $0.00744 |
| Sonnet 5 | $0.00000 | $0.00298 |
| Haiku 4.5 | $0.00000 | $0.00149 |
Grade A, and why
full-context scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are working on the current project. Before proceeding with the user's request "$ARGUMENTS", you need to intelligently gather relevant project context using an adaptive sub-agent strategy.
Auto-Loaded Project Context:
@/CLAUDE.md @/docs/ai-context/project-structure.md @/docs/ai-context/docs-overview.md
Step 1: Intelligent Analysis Strategy Decision
Think deeply about the optimal approach based on the project context that has been auto-loaded above. Based on the user's request "$ARGUMENTS" and the project structure/documentation overview, intelligently decide the optimal approach:
Strategy Options:
Direct Approach (0-1 sub-agents):
- When the request can be handled efficiently with targeted documentation reading and direct analysis
- Simple questions about existing code or straightforward tasks
Focused Investigation (2-3 sub-agents):
- When deep analysis of a specific area would benefit the response
- For complex single-domain questions or tasks requiring thorough exploration
- When dependencies and impacts need careful assessment
Multi-Perspective Analysis (3+ sub-agents):
- When the request involves multiple areas, components, or technical domains
- When comprehensive understanding requires different analytical perspectives
- For tasks requiring careful dependency mapping and impact assessment
- Scale the number of agents based on actual complexity, not predetermined patterns
Step 2: Autonomous Sub-Agent Design
For Sub-Agent Approach:
You have complete freedom to design sub-agent tasks based on:
- Project structure discovered from the auto-loaded
/docs/ai-context/project-structure.mdfile tree - Documentation architecture from the auto-loaded
/docs/ai-context/docs-overview.md - Specific user request requirements
- Your assessment of what investigation approach would be most effective
CRITICAL: When using sub-agents, always launch them in parallel using a single message with multiple Task tool invocations. Never launch sequentially.
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.
- yesterday First seen · 139 lines · 0 tokens per session scan A a8462d4f2a16
full-context is a command published in the GitHub repository nmime/motiv-buy (0 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,488 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 2026-09-04.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.