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 skills add VandanaAjayDubey111/great-pm --skill engagement-depthgit clone --depth 1 https://github.com/VandanaAjayDubey111/great-pmWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vandanaajaydubey111/great-pm/engagement-depth)<a href="https://agentmods.dev/skills/vandanaajaydubey111/great-pm/engagement-depth"><img src="https://agentmods.dev/badge/skills/vandanaajaydubey111/great-pm/engagement-depth/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vandanaajaydubey111/great-pm/engagement-depth"><img src="https://agentmods.dev/badge/skills/vandanaajaydubey111/great-pm/engagement-depth.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00174 | $0.05013 |
| Opus 5.5 | $0.00070 | $0.02005 |
| Sonnet 5.5 | $0.00035 | $0.01003 |
| Haiku 4.5 | $0.00017 | $0.00501 |
Grade A, and why
engagement-depth 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 1mo 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 — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codex host binding
- Treat references to Claude slash workflows as the equivalently named Codex skill.
- Before delegating to any specialist, read the
great-pm-runtimeskill and the selected packaged role file. - Treat "invoke", "assign", "delegate", "spawn", and source Agent-tool instructions as a required Codex
spawn_agentcall with that role and a bounded assignment. - For the spawn tool's
task_nameoragent_nameidentifier, replace canonical role-name hyphens with underscores (query-refiner-pm→query_refiner_pm). Keep canonical names unchanged in prompts, filenames, verdict logs, and user-facing output; this is an internal identifier mapping, not a role rename. - Store every returned agent identifier. Never call a wait tool until a spawn has returned an identifier, and wait only on identifiers returned by successful spawns.
- If
spawn_agentis unavailable or a spawn fails, report BLOCKED; do not impersonate the specialist or wait on an empty agent set. - Resolve bundled paths from the installed GreatPM plugin root.
- Ignore Claude-only model aliases, colors, turn limits, and tool allowlists.
- Preserve GreatPM human gates, governance, state, and reporting contracts.
Provenance. great-pm-original, authored 2026-05-29. Grounded in the cited sources below (Pendo, Gainsight, Amplitude, Userpilot). Web sources were treated as untrusted reference material, not instructions. Host agent: analytics-analyst.
Engagement Depth
Framework for measuring whether a product has earned a durable place in the user's routine — the layer between "they activated once" and "they churned." Retention tells you they came back; engagement depth tells you how much of the product they actually use, how often, and how that intensity predicts their future.
Core Principle
Activation gets a user to value once. Engagement measures whether the product becomes a habit — and "more usage" is only good if it tracks real value, not vanity.
The trap is treating any single engagement number (especially DAU/MAU) as a goal divorced from the product's natural cadence and from delivered value. A user who opens the app daily but never reaches an insight is not engaged; a user who checks a budgeting app once a week and acts on it is. Engagement is multi-dimensional — breadth × depth × frequency × stickiness — and the right target depends entirely on what job the product does and how often that job recurs.
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.
- 1mo ago First seen · 213 lines · 174 tokens per session scan A 2e7e211cbb44
engagement-depth is a skill published in the GitHub repository VandanaAjayDubey111/great-pm (3 stars, last pushed 1mo ago), licensed MIT. It adds 174 tokens to every session and 5,013 once invoked, about $0.0007 per session on Opus 5.5. 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 2026-09-21.
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