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 forsvn-labs/meta-skills --skill measure-growthgit clone --depth 1 https://github.com/forsvn-labs/meta-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/forsvn-labs/meta-skills/measure-growth)<a href="https://agentmods.dev/skills/forsvn-labs/meta-skills/measure-growth"><img src="https://agentmods.dev/badge/skills/forsvn-labs/meta-skills/measure-growth/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/forsvn-labs/meta-skills/measure-growth"><img src="https://agentmods.dev/badge/skills/forsvn-labs/meta-skills/measure-growth.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 | $0.00060 | $0.00468 |
| Opus 5 | $0.00030 | $0.00234 |
| Sonnet 5 | $0.00012 | $0.00094 |
| Haiku 4.5 | $0.00006 | $0.00047 |
Grade A, and why
measure-growth 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Measure growth outcomes
Connect measurement to a decision. Do not create dashboards without an operator action.
Define the decision system
Specify:
- business outcome and decision owner;
- user behavior that represents value;
- primary signal and why it predicts the outcome;
- diagnostic signals for reach, attention, comprehension, belief, motivation, friction, and value;
- guardrails for quality, cost, retention, and harm;
- segments and observation window;
- decision date and keep, revise, stop, or scale thresholds.
Define event names, properties, identity rules, source of truth, and QA steps when implementation detail is requested. Distinguish leading, lagging, and diagnostic measures.
Read results carefully
Check:
- exposure and opportunity volume;
- baseline and comparable period;
- audience, channel, device, geography, and customer mix;
- instrumentation changes and missing data;
- seasonality, releases, promotions, outages, and competitor movement;
- downstream quality, revenue, or retention;
- qualitative objections and customer language.
Do not turn correlation into causation. Prefer a comparison or test that creates different predictions for competing explanations. State uncertainty and accept inconclusive results.
Produce a decision
Return:
- result summary with denominator, baseline, window, and confidence;
- what changed and what did not;
- plausible mechanisms and alternative explanations;
- Keep, drop, test decision;
- next experiment with one intentional change;
- durable learning record.
Write each durable learning as:
- observation;
- audience, offer, channel, and time boundary;
- evidence and confidence;
- implication;
- where it must not be generalized.
Promote a learning only from observed behavior or a documented test. Never invent unavailable analytics or silently treat missing observations as zero.
Use the narrowest permitted data access. Keep tracking changes, experiment activation, messages, and external writes behind explicit approval.
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.
- 12d ago First seen · 67 lines · 60 tokens per session scan A 09c177c54192
measure-growth is a skill published in the GitHub repository forsvn-labs/meta-skills (14 stars, last pushed 1mo ago), licensed MIT. It adds 60 tokens to every session and 468 once invoked, about $0.0003 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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