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Ce Product Pulse

OrganizationPopular
EveryInc
ce-product-pulse

Generate time-windowed product pulse reports from configured signals.

Overview

PublisherEveryInc
Repositorycompound-engineering-plugin
Skill namece-product-pulse
Stars
25.1K
Forks
2.1K
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by EveryInc on GitHub. Read the source before you install it.

Installation

Install the Ce Product Pulse AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/EveryInc/compound-engineering-plugin.git /tmp/compound-engineering-plugin
mkdir -p .claude/skills
cp -r /tmp/compound-engineering-plugin/skills/ce-product-pulse .claude/skills/ce-product-pulse
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ce Product Pulse in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ce Product Pulse on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ce Product Pulse is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Product Pulse

ce-product-pulse queries the product's data sources for a given time window and produces a compact, single-page report covering usage, performance, errors, and followups. The report is saved to <root>/pulse-reports/ and the key points are shown in chat.

Done: a report of 30-40 lines exists at <root>/pulse-reports/YYYY-MM-DD_HH-MM.md, its headlines and top followup are in chat, and Phase 3 (Scheduling) has been reached.

Boundaries

  • Read-only, everywhere. The skill does not mutate the product, the database, or any external system. Its only writes are pulse settings appended to .compound-engineering/config.local.yaml (interview and opt-out writes stay on the local override) and the report file. MCP and other data-source tools are invoked read-only; if a tool offers write modes, do not use them. A database source must be a read-only connection — the interview refuses read-write credentials, and DB access is optional, since many products complete the pulse with analytics and tracing alone.
  • No PII in saved reports. No user emails, account IDs, or message content in the file written to disk.
  • Read it like a founder. No hardcoded thresholds, no default "good"/"bad" labels, no alerting: present the numbers and let the reader judge.
  • Single page. Target 30-40 lines. If a section is thin, leave it thin; if the report is getting long, cut.
  • Not a shipping log or a dashboard replacement. Shipped work lives in the issue tracker and commit history. Deep investigation still uses the native tools; this consolidates a single-page read, and every run is saved so past pulses browse as a timeline.

Interaction Method

Default to the host's blocking question tool already in the current tool list (match by capability, not by a host-specific name). Presence in the current tool list is proof the tool exists; never call a user-facing question tool to discover whether it exists. If a matching tool is listed but unloaded, use the host's tool-discovery primitive to load that capability — do not search for another host's tool name. Fall back to numbered options on the host's user-visible chat surface only when no such tool is in the list or a real question call errors. Never silently skip the question.

Ask one question at a time. Reserve multi-select for first-run configuration only.

Lookback Window

The lookback window is the time range this skill was invoked with (e.g. 24h, 7d) — present in the current prompt or conversation, whether the user gave it directly or a calling skill passed it. Common forms are trailing hours (24h, 48h, 72h), trailing days (7d, 30d), and 1h for launches.

If the argument is empty, default to pulse_lookback_default from config (resolved in Phase 0, Route by config state); if that is also unset, fall through to the hard default of 24h. If the argument is unparseable, ask the user to clarify.

Apply a 15-minute trailing buffer to the window's upper bound. Many analytics and tracing tools have ingestion lag; querying right up to now under-reports the most recent events. For a 24h window, query [now - 24h - 15m, now - 15m].

Artifact Root

This skill writes pulse reports under <root>/pulse-reports/. Resolve <root> when you first compose a <root>/ path (per the block below), never before you need it. A write to <root>/... and a read of <root>/solutions/ both count as composing a <root>/ path, so either one triggers resolution; only a run that touches no <root>/ path at all -- a scratch-only or no-repo flow -- skips it.

Resolve the CE artifact root <root> before composing any artifact path.

  • Read docs_root from <repo-root>/.compound-engineering/config.yaml only (<repo-root> = git rev-parse --show-toplevel). Do not read it from config.local.yaml. Unset -> <root> is docs, exactly as before.
  • Validate a set value: a repo-relative directory whose real, symlink-resolved path stays inside the repo and is neither the repo root nor under .git/. Otherwise stop with an error naming docs_root and the value -- never fall back to docs.
  • Use <root> as the sole artifact location: create it if absent, compose each path as <root>/<subdir> with this skill's own subdirectory, and never also read docs.

Phase 0: Route by config state

Resolve ordinary CE yaml keys from the two repo files.

  • Read <repo-root>/.compound-engineering/config.local.yaml, then config.yaml (<repo-root> = git rev-parse --show-toplevel). Missing files are skipped. Gitignore does not change resolution.
  • Win with the first active (non-commented) value. For scalars, empty is unset; an invalid value continues to the next layer, then the skill default. For lists and maps, a present key — including an empty list or map — replaces the whole key.
  • Do not use this rule for docs_root — that key is config.yaml only.

Resolve <repo-root> with git rev-parse --show-toplevel, then apply the ordinary-key rule above to the pulse_* keys. Read references/config.md whenever a pulse_* value has to be interpreted — it is the key schema and nothing else: each key, its allowed values, and its default, with an unset or invalid value taking the documented default rather than being guessed.

Routing: every run passes through Phase 2 (Run the pulse) and then Phase 3 (Scheduling). Run Phase 1 (First-run interview) first when pulse_product_name is unset after cascade (the ordinary-key rule above), when the repo root cannot be resolved, or when the argument was setup, reconfigure, or edit config. Otherwise start at Phase 2 (Run the pulse).

Phase 1: First-run interview

Read references/setup.md first; this read is required. It defines the strategy-doc seeding, the interview order and its pushback bar, the read-write database refusal, how the config is written to config.local.yaml without disturbing other keys, and the one-time scheduling offer. The questions themselves come from references/interview.md, which that file names as its own required read.

Phase 2: Run the pulse

If Phase 1 (First-run interview) ran, re-apply the ordinary-key rule (local then tracked) from the repo root using the native file-read tool before any query, to pick up edits accepted during the Phase 1 review step. Otherwise use the pulse_* values already extracted in Phase 0 (Route by config state), applying the defaults in references/config.md for anything unset.

Then read references/run.md before dispatching any query; this read is required. It defines which queries run in parallel and which run serially, the pulse_db_enabled check that decides whether database work runs, the optional quality sampling and its scoring discipline, the four report sections, and where the report is written.

Phase 3: Scheduling

Setup offers a recurring run once (references/setup.md). On later runs, mention it again lightly: if the argument was a schedule keyword (daily, hourly, weekly), say this run is ad-hoc and point at the harness's scheduling primitive; if no schedule is on file and this is the third or later run, mention once that scheduling is available. Do not nag on every run, and never schedule automatically — any handoff to a scheduling primitive requires explicit confirmation.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ce Product Pulse AI skill do?

Generate time-windowed product pulse reports from configured signals.

Why use Ce Product Pulse on TypingMind?

Because you install it once and use it with any model. Ce Product Pulse is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ce Product Pulse in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/EveryInc/compound-engineering-plugin/tree/main/skills/ce-product-pulse. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ce Product Pulse?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ce Product Pulse?

As many as you like. As long as a model supports skills, you can use Ce Product Pulse with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ce Product Pulse AI skill free?

Yes. It is published on GitHub by EveryInc under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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