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/setup-contextgit 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 | $0.00514 |
| Opus 5 | $0.00000 | $0.00257 |
| Sonnet 5 | $0.00000 | $0.00103 |
| Haiku 4.5 | $0.00000 | $0.00051 |
Grade A, and why
setup-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 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 — 37 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Command: setup-context
Build business context from scratch via a guided interview, when the customer has nothing written down to import. Pulls schema facts first to make questions concrete, drafts to the fixed template, previews with a diff, and writes on CONFIRM. Handles org level, project level, or both.
Session reads: org_id, target_level, project_id, project_name, caller_role, existing_context Session writes: schema_facts, interview_answers, draft_context
Step 0 — Confirm the user wants interview-based setup
Run SKILL.md's "Offer import-context first" step. If they have a source, hand off to import-context immediately. Only proceed with this command if that step clears (per its own exemptions).
Step 1 — Research and pull schema first
Before asking the user anything (per references/interview-questions.md):
- Web search the company and draft the Business and Customer Segments sections from public sources — these are confirmed, not asked cold. If no web search tool is available, skip this pre-fill and ask those questions directly in Step 2 instead.
- Pull schema facts into
schema_facts(top ~10 events, ~15 properties, integrations, timezone, recency), timestamped, per SKILL.md's "Quarantine volatile facts" rule. Partial failure: continue, note the gap in Open Questions.
Step 2 — Interview (gaps and internal-only facts)
Work references/interview-questions.md, in small batches, seeded with research and schema_facts. Present the web-researched Business/Segments drafts for correction rather than asking from scratch.
Cover references/interview-questions.md's "Always ask if not already covered" list in full — do not skip any item on it. Record in interview_answers (unknowns → Open Questions per SKILL.md's "Ground everything" rule).
Step 3 — Compose draft
Compose draft_context per SKILL.md's Write flow composition rules, with the qualitative sections drawn from interview_answers and the research pre-fill.
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 · 37 lines · 0 tokens per session scan A ed0d2d095708
setup-context 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 514 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.