auto-trigger

Overview of auto-trigger skill

What auto-trigger actually does

The auto-trigger skill is a workflow configuration skill for Agent Orchestration, not a task skill you run by itself. Its job is to define what should happen after another skill finishes, starts, or reaches a milestone, so an orchestrator can chain skills together with less manual prompting.

Who should install auto-trigger

This auto-trigger skill is best for people building multi-step agent workflows inside agent-playbook, especially if they want predictable handoffs such as PRD creation → improvement pass → session logging, or implementation → review → PR creation. If you only use one-off prompts or single-skill workflows, this skill will add little value.

The real job-to-be-done

Users typically want to stop micromanaging follow-up steps. Instead of remembering to manually call a logger, reviewer, or PR helper after each stage, auto-trigger centralizes those relationships so the next action can happen automatically, in the background, or with confirmation.

What makes auto-trigger different

The main differentiator is that auto-trigger separates workflow wiring from task execution. The skill defines hook patterns such as:

• trigger another skill automatically
• ask before running a follow-up skill
• run a follow-up skill in the background
• attach context or conditions to the next step

That makes it more useful as shared orchestration infrastructure than as a standalone prompt asset.

What to know before installing

The biggest adoption blocker is expectation mismatch: auto-trigger install does not give you a user-facing command that solves work on its own. It only becomes valuable when another orchestrator or skill reads these hook definitions and honors them. If your environment does not support skill-to-skill triggering, this skill will mostly serve as a reference pattern.

How to Use auto-trigger skill

Install context for auto-trigger

Install auto-trigger as part of the agent-playbook collection:

npx skills add https://github.com/zhaono1/agent-playbook --skill auto-trigger

Because this is a configuration-oriented skill, installation makes the hook definitions available to your workflow system; it does not mean you should invoke auto-trigger directly in normal task prompts.

Read these files first

For a fast decision, start with:

• skills/auto-trigger/SKILL.md
• skills/auto-trigger/README.md

SKILL.md contains the actual hook structures and examples. README.md confirms the intended usage model: this skill is meant to be consumed by workflow-orchestrator or similar orchestration logic, not called manually as a primary worker.

How auto-trigger is meant to be called

In practice, auto-trigger usage is indirect. A parent workflow, orchestrator, or another skill checks hook definitions and then launches the next skill based on an event such as:

• prd_complete
• prd_implemented
• implementation_complete
• session_start
• session_end

So the actual call pattern is closer to: “when event X occurs, inspect configured triggers, then run the listed follow-up skills with the specified mode and context.”

Understand the trigger modes before relying on it

The examples show multiple trigger behaviors:

• auto: run the next skill automatically
• background: run it without interrupting the main workflow
• ask_first: request confirmation before the follow-up action

This matters operationally. Use auto for low-risk logging or standard handoffs, ask_first for expensive or disruptive steps like review, and background when the follow-up should not block the main path.

What input the skill really needs

auto-trigger needs structured workflow events, not a vague natural-language request. The useful inputs are:

• a named event or lifecycle moment
• the skill to trigger
• the mode
• optional conditions
• optional context to pass forward
• optional action names for session-style hooks

Without those pieces, the orchestrator has to guess.

Turn a rough goal into a usable auto-trigger prompt

Weak input:

• “Set up automatic workflow steps.”

Strong input:

• “When implementation_complete fires, ask before running code-reviewer, then auto-run create-pr only if changes are staged. Pass the feature name into the follow-up context.”

Why this is better:

• it names the event
• it defines each downstream skill
• it selects execution mode deliberately
• it includes a condition that prevents bad automatic behavior
• it preserves context for the next skill

Example hook patterns worth copying carefully

From the repository, the strongest reusable patterns are:

• PRD completion triggering improvement and session logging
• implementation completion triggering review and PR creation
• session lifecycle hooks creating and updating logs

These are good templates because they show different orchestration intents: quality improvement, bookkeeping, and end-of-task progression.

Where users often misapply auto-trigger

A common mistake is trying to use auto-trigger as if it were a direct task executor. It does not write PRDs, review code, or create PRs itself. It only declares relationships. If your stack does not have an agent or orchestrator that reads hooks: definitions, the skill will not produce automation by itself.

How to add auto-trigger to another skill

The repository shows that hook definitions can be added to another skill’s front matter with a hooks: block. That is the practical integration point. For example, you would extend a task skill so that after completion it points to session-logger, code-reviewer, or another follow-up skill.

This is the main implementation path for auto-trigger for Agent Orchestration: attach lifecycle hooks to task skills rather than asking users to remember the chain manually.

Suggested workflow for first adoption

1. Pick one stable event, such as session_end or implementation_complete.
2. Add only one or two follow-up triggers.
3. Start with low-risk actions like logging.
4. Test whether your orchestrator reads and executes the hook schema correctly.
5. Only then add conditional or multi-step chains.

This reduces debugging complexity and makes it obvious whether failures come from the trigger config or the downstream skills.

Practical constraints that affect output quality

The repository examples imply several constraints:

• trigger names are convention-based, so consistency matters
• conditions must be checkable by the orchestrator
• context strings are only useful if downstream skills can consume them
• chaining too many automatic steps can make workflows harder to debug

In short: simple, explicit hooks are more reliable than clever ones.

auto-trigger skill FAQ

Is auto-trigger useful on its own

Usually no. The auto-trigger skill is mainly useful when paired with an orchestrator or a broader skill system that reads hook definitions and executes the next step.

Is auto-trigger beginner-friendly

Yes for reading, not always for setup. The examples are easy to understand, but beginners may struggle if they expect a standalone command. You need at least a basic mental model of event-driven workflow chaining.

How is auto-trigger different from a normal prompt

A normal prompt asks the model to do work now. auto-trigger defines what should happen after some other work completes. It is workflow plumbing, not the work unit itself.

When should I not use auto-trigger

Skip auto-trigger if:

• you do not have repeated multi-step workflows
• you are not using an orchestration layer
• manual follow-ups are rare and cheap
• your team is still changing process steps daily

In those cases, static hooks may create more maintenance than value.

Does auto-trigger replace workflow-orchestrator

No. The repository explicitly positions it as configuration that something like workflow-orchestrator reads. It complements an orchestrator; it does not replace one.

Can I use auto-trigger for non-code workflows

Potentially yes, if your orchestration system can map events to follow-up skills in other domains. But the shipped examples are centered on agent-playbook development flows like PRDs, implementation, review, PR creation, and session logging.

How to Improve auto-trigger skill

Start with clearer event names

The easiest way to improve auto-trigger results is to standardize event names across your skills. Ambiguous events like done or finished create confusion. Specific names like prd_complete or implementation_complete make trigger logic easier to maintain and debug.

Add conditions wherever automatic actions can misfire

A strong auto-trigger guide practice is to protect risky actions with conditions. The example changes_staged check is a good model. Add conditions when:

• files must exist
• output must be complete
• a branch or PR state matters
• a downstream skill should only run after validation

This keeps automation from feeling brittle.

Pass better context to downstream skills

If a follow-up skill needs to know what just happened, include that in the trigger context. For example, Implemented PRD: {feature_name} is better than a generic “task complete” message because it gives the next skill enough signal to act intelligently.

Prefer shallow chains before deep chains

A common failure mode is over-automation: one event triggers several skills, which trigger more skills, until nobody knows why something ran. Keep the first version of auto-trigger to one event and one or two follow-up actions. Expand only after the chain is stable.

Use ask_first for human checkpoints

If a downstream step is expensive, externally visible, or potentially noisy, switch from auto to ask_first. This is especially helpful for review, PR creation, or anything that can create churn if triggered too eagerly.

Improve the repository usage path for your team

If you adopt this skill internally, document:

• which events your team supports
• which skills are allowed as follow-ups
• which modes are approved
• which conditions are required for sensitive actions

That closes the biggest gap in the current auto-trigger usage: the repository gives patterns, but teams still need local conventions to avoid inconsistent hook design.

Iterate by auditing actual trigger outcomes

After the first rollout, review:

• which triggers fired correctly
• which conditions were too loose or too strict
• whether downstream skills had enough context
• where users still manually intervened

That audit will tell you whether to simplify the chain, enrich the context, or move some triggers from auto to ask_first.

Best way to get more value from auto-trigger

The highest-value improvement is not adding more hooks. It is choosing the few workflow transitions that are repeated, predictable, and costly to forget. auto-trigger works best when it removes routine orchestration friction, not when it tries to automate every edge case.