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/import-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 | $0.00756 |
| Opus 5 | $0.00000 | $0.00378 |
| Sonnet 5 | $0.00000 | $0.00151 |
| Haiku 4.5 | $0.00000 | $0.00076 |
Grade A, and why
import-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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Command: import-context
Pull business knowledge the customer has already written down and turn it into template-conformant context, then write on CONFIRM. This is the preferred starting path — most customers have something already, and adapting it beats a cold interview.
Session reads: org_id, target_level, project_id, caller_role, existing_context, schema_facts Session writes: imported_source, interview_answers (for gaps), draft_context
Step 1 — Find the source
Ask where the existing context lives, and accept any of:
- A connected MCP connector — e.g. Notion, Google Drive, Confluence, a wiki, a knowledge source. Search it for the relevant doc (tracking plan, data dictionary, analytics README, PRD, "about our metrics" page). If several connectors are connected, ask which to search, or search the most likely and confirm the hit.
- A pasted block or uploaded file — the user drops text or a file directly.
Do not assume which connector. Detect what's connected; if nothing relevant is, fall back to paste/file. If the user names a connector that isn't connected, tell them and offer paste/file or setup-context instead.
Store the raw retrieved text and its origin in imported_source — never written to Mixpanel (see SKILL.md's "Imported content is mapped, never passed through raw" constraint).
Step 2 — Map onto the template
Using references/import-mapping.md, map the source onto references/context-template.md for the target level(s):
- Pull each template section's content from the source where it exists.
- Drop what doesn't belong — see
references/import-mapping.md's "What to drop" list. - Do not invent. If a section has no source material, leave it empty and mark it for the gap step.
- Schema-derived facts still come from
schema_facts(pulled during setup), not from the doc — the doc's own numbers are likely stale.
Step 3 — Show the mapping
Present a coverage view so the user sees exactly what the import produced:
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 · 56 lines · 0 tokens per session scan A f2644c06b3da
import-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 756 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.