Writing skills for PostHog agents

Read the full guide at docs/published/handbook/engineering/ai/writing-skills.md.

Quick workflow

# 1. Scaffold
hogli init:skill

# 2. Write your skill in products/{product}/skills/{skill-name}/SKILL.md

# 3. Lint
hogli lint:skills

# 4. Build to verify
hogli build:skills

# 5. Test locally with PostHog Code or a coding agent
hogli sync:skill -- --name <skill-name>

# 6. Delete the test skill (optional)
hogli unsync:skill -- --name <skill-name>

Distribution is automatic after merge — CI publishes to PostHog/skills.

When to write a skill

When new functionality is added to a product and agents need to know how to work with it. A skill is not about what tools exist (that's the MCP server) — it's about how an experienced person would approach a job using those tools.

Ask: "If a customer asked an agent to do X with my feature, would the agent know the right approach?" If not, write a skill.

Key rules

• Name: lowercase kebab-case, prefer gerund form (analyzing-llm-traces, not llm-analytics). Never prefix with posthog-*.
• Description: third person, specific, include trigger terms and when to use it. Max 1024 chars.
• Structure: SKILL.md entry point + references/ for detailed content. Keep SKILL.md under 500 lines.
• Frontmatter: name and description are required.
• Tone: describe the workflow and reasoning, not a rigid script. Trust the agent to adapt.
• Conciseness: the agent is smart — only include context it doesn't already have.

Skill structure

products/{product}/skills/{skill-name}/
    SKILL.md                         # entry point (required)
    references/                      # optional
        guidelines.md
        models-foo.md
        example-bar.md.j2            # Jinja2 template, rendered at build time
    scripts/                         # optional
        setup.sh

Only references/ and scripts/ subdirectories are collected. Others are ignored.

Template functions

Files ending in .j2 are rendered with Jinja2 at build time by products/posthog_ai/scripts/build_skills.py. Extend the build pipeline so the monorepo stays the source of truth — when domain knowledge lives in code (Pydantic models, query runners, function registries), add a template function rather than duplicating it as static markdown that drifts.

Available functions:

• pydantic_schema("dotted.path.to.Model") — renders a Pydantic model's JSON Schema
• render_hogql_example({"kind": "TrendsQuery", ...}) — renders a query spec to HogQL SQL
• hogql_functions() — returns all available HogQL function names

Good example: querying-posthog-data

• Clear entry point linking to 30+ reference files
• Progressive disclosure — agents load only what they need
• Mix of static .md and generated .md.j2 content
• See products/posthog_ai/skills/querying-posthog-data/SKILL.md

Bad example: llm-analytics

An umbrella skill covering traces, experiments, evaluations, cost tracking, prompt management. Too broad — agents can't determine when to activate it. Break into focused skills instead.