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 cnfeat/top-pm-skills --skill dogfoodinggit clone --depth 1 https://github.com/cnfeat/top-pm-skillsWrote 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/cnfeat/top-pm-skills/dogfooding)<a href="https://agentmods.dev/skills/cnfeat/top-pm-skills/dogfooding"><img src="https://agentmods.dev/badge/skills/cnfeat/top-pm-skills/dogfooding/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/cnfeat/top-pm-skills/dogfooding"><img src="https://agentmods.dev/badge/skills/cnfeat/top-pm-skills/dogfooding.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 | $0.00042 | $0.00591 |
| Opus 5 | $0.00021 | $0.00296 |
| Sonnet 5 | $0.00008 | $0.00118 |
| Haiku 4.5 | $0.00004 | $0.00059 |
Grade A, and why
dogfooding 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 13d 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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dogfooding
Help the user implement effective dogfooding practices using frameworks from 2 product leaders who have built cultures of intense internal product usage.
How to Help
When the user asks for help with dogfooding:
- Assess current state - Determine how much the team currently uses their own product
- Identify the gap - Find where team members lack firsthand experience with user pain points
- Design the program - Help create systems that make dogfooding natural and required
- Measure impact - Track how dogfooding improves product decisions
Core Principles
Require team members to become users
Maya Prohovnik: "I am constantly yelling at my product team who do not have podcasts and being like, I really don't think that you can build the right things. If they talk to users all the time, they see the data, but all of them, once they finally start doing their podcast, they're like, I get it." Force the entire team to become creators/users to deeply understand user pain points.
Use the tool intensely every day
Michael Truell: "From the very start, our product development process was really about dogfooding, and using the tool intensely every day. And we never wanted to ship anything that wasn't useful to us." 'Intense' daily use provides the realism needed to build useful features, especially for AI products.
Questions to Help Users
- "How often does each team member actually use the product as a real user?"
- "What's preventing your team from being heavy users of your own product?"
- "What would it take to make internal usage feel natural rather than forced?"
- "Are you learning different things from dogfooding vs. customer feedback?"
- "How quickly do you feel the pain of bugs or friction when using your own product?"
Common Mistakes to Flag
- Superficial testing - Using the product only in demo mode, not for real work
- Delegating to QA - Relying on testers instead of requiring team members to be real users
- Ignoring non-obvious use cases - Only testing the happy path rather than edge cases
- Not acting on findings - Dogfooding without a process to fix discovered issues
- Excluding non-product roles - Only having engineers dogfood when designers and PMs should too
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 13d ago First seen · 53 lines · 42 tokens per session scan A 3b8317a5f11e
dogfooding is a skill published in the GitHub repository cnfeat/top-pm-skills (48 stars, last pushed 3mo ago), licensed MIT. It adds 42 tokens to every session and 591 once invoked, about $0.0002 per session on Opus 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 2026-08-30.
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