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/mixpanel/ai-plugins/statusgit clone --depth 1 https://github.com/mixpanel/ai-pluginsWhat 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 | $0.00000 | $0.00577 |
| Opus 5 | $0.00000 | $0.00289 |
| Sonnet 5 | $0.00000 | $0.00115 |
| Haiku 4.5 | $0.00000 | $0.00058 |
Grade A, and why
status 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 — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Command: status
The unified AI-readiness readout. Scores both layers in one view — business context completeness and Lexicon coverage — and tells the user exactly what's missing and which command fixes it. This is the re-engagement hook: run it on any account to see where it stands and what to do next. It is read-only.
Session reads: org_id, org_name, target_level, project_id, project_name, existing_context, lexicon_score Session writes: existing_context, lexicon_score (refreshes both)
Step 1 — Business-context layer
For the org and (if a project is in scope) the project:
- Read current context if not already in
existing_context. - Score completeness against
references/context-template.md: which required sections are present and non-empty. Weight the high-value sections (north star, qualified-user definition, authority & governance) more heavily — a doc with vocabulary but no authority section is weaker than the raw section count suggests. - Flag staleness: if a Schema Snapshot section exists, compare its timestamp to now and warn if old.
Step 2 — Data layer (Lexicon)
If a project is in scope and manage-lexicon is available, ask it to score current coverage (or reuse lexicon_score if fresh this session) to get event-description, property-description, and tag coverage. If manage-lexicon is unavailable, mark the data layer "not measured" rather than guessing.
Step 3 — Present one readout
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
AI Readiness — [Project Name]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
BUSINESS CONTEXT
Org level ●●●●○ present, missing: customer segments
Project level ●●○○○ thin — no authority section, no qualified-user def
DATA (LEXICON)
Event descriptions 45%
Property descriptions 30%
Events tagged 12%
TOP GAPS (most impact first)
1. Project authority & governance → import-context / setup-context
2. Property descriptions (30%) → enrich-data
3. Event tags (12%) → enrich-data
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
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 · 48 lines · 0 tokens per session scan A e92210348d44
status is a command published in the GitHub repository mixpanel/ai-plugins (15 stars, last pushed 8d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 577 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 2026-08-30.
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.
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.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.