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 timurgaleev/memex --skill functional-area-resolvergit clone --depth 1 https://github.com/timurgaleev/memexWrote 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/timurgaleev/memex/functional-area-resolver)<a href="https://agentmods.dev/skills/timurgaleev/memex/functional-area-resolver"><img src="https://agentmods.dev/badge/skills/timurgaleev/memex/functional-area-resolver/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/timurgaleev/memex/functional-area-resolver"><img src="https://agentmods.dev/badge/skills/timurgaleev/memex/functional-area-resolver.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 | $0.00089 | $0.03793 |
| Opus 5.5 | $0.00036 | $0.01517 |
| Sonnet 5.5 | $0.00018 | $0.00759 |
| Haiku 4.5 | $0.00009 | $0.00379 |
Grade A, and why
functional-area-resolver 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 1mo 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
88% identical to functional-area-resolver — 201 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 — 325 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Functional-Area Resolver — Pattern for Compressing Routing Tables
Problem
Routing files (RESOLVER.md, AGENTS.md) grow as skills are added. Each skill gets its own row (trigger -> skill path). At ~200+ skills this hits 25-30KB, eating context budget that should go to actual work.
Solution: Functional-Area Dispatchers
Replace N rows per area with one entry per functional area. Each entry
lists all sub-skills it can dispatch to in a (dispatcher for: ...) clause.
Before (270 rows, 25KB)
- Creating/enriching a person or company page -> `enrich`
- Fix broken citations in brain pages -> `citation-fixer`
- Publish/share a brain page as link -> `publish`
- Generate PDF from brain page -> `brain-pdf`
- Read a book through lens of a problem -> `strategic-reading`
- Personalized book analysis -> `book-mirror`
- Brain integrity -> `maintain`
...
After (13 rows, 13KB)
- **Brain & knowledge**: create/enrich/search/export brain pages, filing,
citations, publishing, book analysis, strategic reading, concept synthesis,
archive mining -> `brain-ops` (dispatcher for: enrich, query, brain-pdf,
publish, maintain, citation-fixer, book-mirror,
strategic-reading, concept-synthesis, archive-crawler, ...)
Why It Works
The LLM doesn't need one row per sub-skill. It needs:
- Area recognition — "this is about brain pages" -> Brain & Knowledge
- Sub-skill visibility — the
(dispatcher for: ...)list shows what's available - The skill file itself — once the LLM reads
brain-ops/SKILL.md, it has full routing detail
This is a two-layer dispatch: routing file routes to the area, the area skill routes to the specific sub-skill. Each layer does one job well.
A/B Eval Results
Three resolver architectures were tested across three Anthropic frontier models (Opus 4.7, Sonnet 4.6, Haiku 4.5) on real production AGENTS.md content, 20 hand-authored training fixtures + 5 held-out blind fixtures, n=3 seeded repeats per (fixture, variant). Two scoring rules: STRICT (predicted slug exactly equals expected) and LENIENT (predicted is in the same dispatcher area as expected). Both matter:
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.
- 1mo ago First seen · 325 lines · 89 tokens per session scan A d04466dc2a4b
functional-area-resolver is a skill published in the GitHub repository timurgaleev/memex (11 stars, last pushed 21d ago), licensed MIT. It adds 89 tokens to every session and 3,793 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to functional-area-resolver, differing in 201 lines, and is treated as a copy.
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