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 serejaris/personal-corp-os --skill pm-metricsgit clone --depth 1 https://github.com/serejaris/personal-corp-osWrote 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/serejaris/personal-corp-os/pm-metrics)<a href="https://agentmods.dev/skills/serejaris/personal-corp-os/pm-metrics"><img src="https://agentmods.dev/badge/skills/serejaris/personal-corp-os/pm-metrics/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/serejaris/personal-corp-os/pm-metrics"><img src="https://agentmods.dev/badge/skills/serejaris/personal-corp-os/pm-metrics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00160 | $0.03076 |
| Opus 5 | $0.00080 | $0.01538 |
| Sonnet 5 | $0.00032 | $0.00615 |
| Haiku 4.5 | $0.00016 | $0.00308 |
Grade A, and why
pm-metrics 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 12d 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 — 282 lines — stays where its author put it; the contents beside it link to each section on GitHub.
pm-metrics — Product metrics review
Part of the Personal Corp framework — running a one-person business through AI agents. Systematically review product metrics, identify trend changes, locate root causes, output action recommendations. Includes North Star decomposition, retention diagnostics, funnel methodology, and A/B experiment reading.
Inputs
| Field | Required | Notes |
|---|---|---|
| Metric data | yes | Excel / CSV / pasted table / verbal description |
| Cycle | no | Weekly / monthly / quarterly review; default weekly |
| Focus | no | Full review / single-metric anomaly / experiment readout |
| Business context | no | Releases, campaigns, incidents in the period |
Mode: full data → complete review; single-metric change → focused anomaly analysis.
Step 1 — Data integrity check
- Confirm time coverage (current vs comparison period)
- Confirm metric coverage (which North Star / L1 / L2 are present)
- Flag missing critical data
Step 2 — North Star metric system
Decomposition: North Star → L1 → L2.
L1 dimensions:
- User growth: DAU/WAU/MAU, new, returning
- User engagement: core action frequency, session length, feature reach
- User retention: D1 / D7 / D30
- Conversion efficiency: signup → activation → paid step-by-step rates
- Business value: paid rate, ARPU, LTV
- Satisfaction: NPS, complaint rate, ratings
North Star selection guide:
| Product type | Recommended NSM | Typical L1 |
|---|---|---|
| Social / community | Weekly active posters | DAU/MAU ratio, interactions per user, D7 retention |
| Tools / productivity | Weekly users completing core task | Task completion rate, frequency, feature reach |
| E-commerce | Weekly transacting users | GMV, AOV, repeat rate, conversion |
| Content / media | Weekly content-consumption time | Time per user, completion rate, return rate |
| SaaS / B2B | Weekly active teams | Team penetration, feature depth, renewal rate |
Step 3 — Growth metric analysis
Definitions:
- DAU: distinct users with valid action that day
- WAU: distinct users active ≥ 1 day in 7
- MAU: distinct users active ≥ 1 day in 30
- DAU/MAU ratio (stickiness): > 0.5 very high, 0.3-0.5 high, 0.2-0.3 medium, < 0.2 low
What ships with it
3 files 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.
- 12d ago First seen · 282 lines · 160 tokens per session scan A 5060eef5ea0e
pm-metrics is a skill published in the GitHub repository serejaris/personal-corp-os (225 stars, last pushed 16d ago), licensed MIT. It adds 160 tokens to every session and 3,076 once invoked, about $0.0008 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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figure-rhetoric
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manuscript-provenance
Computational provenance audit verifying every number, table, and figure in a manuscript derives from code, not manual entry. Triggers on: "check provenance", "verify reproducibility", "audit my pipeline", "are my numbers from code", "provenance audit". Companion to manuscript-review (prose audit).
code-refiner
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competitive-analyzer
Competitive landscape analysis: Porter's Five Forces, competitor discovery, feature/pricing matrices, positioning maps, moat assessment via WebSearch. Triggers on: "competitive analysis", "competitor comparison", "competitive landscape", "Porter's Five Forces", "market positioning", "moat assessment", "defensibility…