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 Ingramradical235/anty-framework --skill kpi-treegit clone --depth 1 https://github.com/Ingramradical235/anty-frameworkWrote 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/ingramradical235/anty-framework/kpi-tree)<a href="https://agentmods.dev/skills/ingramradical235/anty-framework/kpi-tree"><img src="https://agentmods.dev/badge/skills/ingramradical235/anty-framework/kpi-tree/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/ingramradical235/anty-framework/kpi-tree"><img src="https://agentmods.dev/badge/skills/ingramradical235/anty-framework/kpi-tree.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 | $0.00062 | $0.01306 |
| Opus 5 | $0.00031 | $0.00653 |
| Sonnet 5 | $0.00012 | $0.00261 |
| Haiku 4.5 | $0.00006 | $0.00131 |
Grade A, and why
kpi-tree 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 9d 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.
This is a copy
100% identical to kpi-tree — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
KPI Tree Decomposition
When to Apply
- Decomposing a new Goal into Drivers
- Re-evaluating Driver priorities after metric updates
- Identifying the constraint (Crux) Driver
- When the founder asks "where should we focus?"
- Periodic impact_weight recalculation
Core Framework
Goal -> Driver Decomposition
Decompose the Goal metric into its constituent factors using a formula:
Goal: 100 paying customers
Formula: customers = visitors x CVR x trial_to_paid_rate
KPI Tree:
visitors (1,200 -> 10,000)
organic_search (400 -> 5,000) -> Driver: SEO content
social (300 -> 2,000) -> Driver: Social growth
referral (200 -> 1,500) -> Driver: Referral program
direct (300 -> 1,500) -> Driver: PR & media
CVR (1.5% -> 3.0%)
lp_optimization -> Driver: Landing page A/B
cta_improvement -> Driver: CTA copy
trial_to_paid (20% -> 40%)
onboarding -> Driver: Tutorial improvement
follow_up -> Driver: Nurture email design
Each Driver inherits a sub-metric from the KPI tree.
Impact_weight Assignment
Initial assignment based on estimated effort-to-impact ratio. Updated dynamically as measured results come in:
Initial (agent estimate): After 2 weeks (measured):
SEO content: 0.30 SEO content: 0.15 (down - no traffic change)
Social growth: 0.25 Social growth: 0.35 (up - viral post)
Referral: 0.20 Referral: 0.25 (up - high CVR)
LP A/B: 0.15 LP A/B: 0.15 (pending)
Nurture: 0.10 Nurture: 0.10 (not started)
Update rules:
- Actions completed but no metric movement -> weight reduced
- Metric improvement detected -> weight increased
- Highest weight = current bottleneck = constraint Driver
Crux Identification (Importance x Tractability)
Every Driver evaluated on two axes:
Tractability ->
Low High
Importance +---------------+---------------+
| | | |
High | AVOID | CRUX | <- All effort here
| (important | (important |
| but can't | AND solvable)|
| solve now) | |
+---------------+---------------+
Low | IGNORE | NICE TO HAVE |
| | (do later) |
+---------------+---------------+
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.
- 9d ago First seen · 136 lines · 62 tokens per session scan A 8fa0e2e37cbc
kpi-tree is a skill published in the GitHub repository Ingramradical235/anty-framework (1 stars, last pushed yesterday), licensed MIT. It adds 62 tokens to every session and 1,306 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to kpi-tree, differing in 0 lines, and is treated as a copy.
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