Compress an agent's routing file (RESOLVER.md or AGENTS.md) by converting
granular skill-per-row tables into functional-area dispatchers. Each area
lists sub-skills in a "(dispatcher for: ...)" clause. The LLM reads one
area entry and routes to the correct sub-skill. Proven via held-out
A/B eval: dispatcher pattern outperforms naive pipe-table compression.
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Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
Compress an agent's routing file (RESOLVER.md or AGENTS.md) by converting
granular skill-per-row tables into functional-area dispatchers. Each area
lists sub-skills in a "(dispatcher for: ...)" clause. The LLM reads one
area entry and routes to the correct sub-skill. Proven via held-out
A/B eval: dispatcher pattern outperforms naive pipe-table compression.
triggers
["compress agents.md","compress my resolver","resolver too big","resolver.md too big","agents.md too large","shrink routing table","slim down agents.md","functional area resolver","functional area dispatcher","context-health agents","context-health resolver","reduce context budget"]
tools
["exec","read","write","edit"]
mutating
true
brain_first
exempt
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 -> `brain-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 -> `brain-librarian`
...
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 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:
STRICT measures: "does the LLM return the exact slug?"
LENIENT measures: "does the LLM land in the right area, even if it picks a
more-specific sub-skill from (dispatcher for: ...)?" This is closer to
production behavior — an agent that lands in gmail for an email intent
succeeds even if the resolver entry said .
executive-assistant
Training corpus (n=20, 3 seeds × 3 variants × 3 models, LENIENT)
Variant
Opus 4.7
Sonnet 4.6
Haiku 4.5
Size
baseline (270 bullet rows)
81.7% ± 7.2%
86.7% ± 7.2%
73.3% ± 7.2%
25KB
functional-areas (this pattern)
98.3% ± 7.2%
100% ± 0%
88.3% ± 7.2%
13KB
resolver-of-resolvers (no dispatcher clause)
63.3% ± 14.3%
41.7% ± 7.2%
65.0% ± 12.4%
10KB
Held-out blind corpus (n=5, 3 seeds, LENIENT)
Variant
Opus 4.7
Sonnet 4.6
Haiku 4.5
baseline
100% ± 0%
100% ± 0%
100% ± 0%
functional-areas
100% ± 0%
100% ± 0%
100% ± 0%
resolver-of-resolvers
100% ± 0%
73.3% ± 28.7%
100% ± 0%
What the data shows
Functional-areas BEATS baseline on training across all three models (+13 to +17pp) at 48% the size. Held-out is saturated at 100% for both — within margin of error.
The (dispatcher for: ...) clause is the load-bearing signal. resolver-of-resolvers strips that clause and collapses to 41.7% on Sonnet — the catastrophic failure case the original PR predicted, now observed.
The pattern works because the LLM can drill into the dispatcher list. Most "STRICT failures" are the LLM picking a more-specific sub-skill (gmail instead of executive-assistant). That's the pattern working as designed. STRICT scoring under-counts; LENIENT scoring reflects production agent behavior.
The pattern's value scales with model tier. Compression gain (functional-areas vs baseline, training, LENIENT) is +17pp on Opus, +13pp on Sonnet, +15pp on Haiku. Sonnet shows the cleanest separation between functional-areas and resolver-of-resolvers (100% vs 41.7%) — model capacity affects how much the dispatcher signal matters.
Reproduce
cd evals/functional-area-resolver
node harness.mjs --model opus # ~225 LLM calls, ~$1.70 at Opus pricing
node harness.mjs --model sonnet # ~$1.00
node harness.mjs --model haiku # ~$0.30
node rescore.mjs baseline-runs/2026-05-11-opus-4-7.jsonl # zero-cost re-score
Production prompt matters. With a naive "return the skill slug" prompt
(no instruction about (dispatcher for: ...)), every compression variant
collapses to ~30-60% on Opus. The dispatcher-aware prompt is in
evals/functional-area-resolver/harness-runner.ts:PROMPT_TEMPLATE. Use it
as the template for your agent's harness; without it, compression breaks.
Training corpus and variants were authored by the same release. Held-out
corpus was written before the variants and never adjusted; this mitigates
but does not eliminate overfitting.
Confidence intervals via t-distribution across n=3 seeded repeats. Hold the
n=3 lower-bound: high CIs mean the underlying sample is noisy.
Single-vendor result. All three models are Anthropic. Cross-vendor
verification (Gemini, GPT) is a v0.33.x follow-up.
Held-out blind set is small (n=5). Saturated at 100% across most cells —
the harness can't distinguish between "100%" and "95% with one nondeterministic
miss." Expanding to ≥20 is a v0.33.x follow-up.
Prior work and citations
The pattern is a static-prompt analog of hierarchical agent routing, a
2024-2025 research direction:
AnyTool (arXiv:2402.04253) showed
meta-agent → category-agent → tool-agent hierarchy on 16K APIs beats flat
retrieval by +35.4pp. The (dispatcher for: ...) clause is the
meta-agent's view collapsed into a single LLM pass.
RAG-MCP (arXiv:2505.03275)
reports 49.2% prompt-token reduction at 3.2× accuracy gain via
embedding-based pre-retrieval. The token-reduction story matches ours
(48% smaller), via a different mechanism (RAG vs static dispatcher).
Anthropic Agent Skills
(engineering blog)
promotes progressive disclosure: frontmatter (~80 tokens) always loaded,
SKILL.md body loaded on match. This skill applies the same principle at
the routing-table level, not the per-skill body level.
The 2025-2026 literature has no published benchmark for static-prompt
hierarchical routing (every published hierarchical scheme resolves the
hierarchy at runtime via a second LLM call). Our finding — that the
hierarchy can be inlined into a single-LLM-pass dispatcher list and retain
routing accuracy — is the open contribution. See
evals/functional-area-resolver/README.md for methodology details.
How To Compress
Step 1: Preconditions
Refuse to compress if either gate fails:
Source routing file is under 12KB (compression overhead exceeds benefit).
git status shows uncommitted changes to the routing file (the
compressor's edit would entangle with whatever the user was doing).
If a user wants to override either gate, they ask explicitly with --force.
Step 2: When to compress which file
GBrain workspaces often have TWO routing files merged at runtime (per
src/core/check-resolvable.ts v0.31.7): skills/RESOLVER.md and a sibling
../AGENTS.md. Choose which to compress:
Only one is fat (>12KB): compress that one; leave the small one alone.
Both are fat: compress them separately, in order: AGENTS.md first
(usually the larger one in OpenClaw-style deployments), then RESOLVER.md.
Only the small one is fat (rare): same rule — compress it.
If the deployment uses only one routing file, this section is a no-op —
compress that one.
Step 3: Identify functional areas
Group skills by domain. Typical areas (adjust per deployment):
Brain & Knowledge — brain-ops as dispatcher
Content Ingestion — ingest as dispatcher
Calendar & Scheduling — google-calendar as dispatcher
Email & Comms — executive-assistant as dispatcher
Research & Investigation — perplexity-research as dispatcher
X/Twitter & Social — x-ingest as dispatcher
Places & Travel — checkin as dispatcher
Product & Building — acp-coding as dispatcher
Infrastructure — healthcheck as dispatcher
Tasks & Logistics — daily-task-manager as dispatcher
Trigger phrases should be broad enough to catch intent ("brain pages, enrich,
search, filing, citations, book analysis")
Sub-skill list should be comprehensive — this is how the LLM knows what's available
The dispatcher skill file should have its own internal routing table
Step 5: Keep always-on entries separate
Gates and always-on entries (acknowledge, multi-user, entity-detector, etc.)
stay as individual rows — they're checked on every message, not dispatched.
Step 6 (MANDATORY): Verify routing accuracy
Run two gates before committing the compressed file. Do NOT commit if either
fails.
Gate 1: Structural verification. Confirms your routing-eval.jsonl
fixtures still resolve to the right skills under the compressed routing file.
Run from the workspace whose routing file you just edited:
gbrain routing-eval --json
If accuracy on your fixtures drops below 95%, revert and tune the area
entries before re-running.
Gate 2: LLM A/B verification on YOUR edited file. Confirms a frontier
LLM can still drill into the dispatcher list and reach sub-skills under
your specific compression. Requires a gbrain repo checkout because the
harness lives there. Copy your edited routing file into the harness's
variants directory, then invoke the harness with --variants pointing
at it:
# In your agent workspace, identify the routing file you just compressed.
EDITED=/path/to/your/AGENTS.md # or skills/RESOLVER.md, whichever you edited# In your gbrain repo checkout:cd /path/to/gbrain/evals/functional-area-resolver
TMP=$(mktemp -d)/variants && mkdir -p "$TMP"cp"$EDITED""$TMP/my-edit.md"# Run the harness against your file (sequential, ~75 calls × $0.0076 ≈ $0.57 on Opus).
ANTHROPIC_API_KEY=... node harness.mjs --variants-dir "$TMP" --variants my-edit \
--model opus --parallel 3 --yes
The harness uses gbrain's bundled fixture set, so this verifies "did the LLM
land in the right sub-skill for routing intents the gbrain-bundled fixtures
cover" — a regression check on shared skills, not a full re-eval of YOUR
fixture set. For full eval coverage, mirror this skill's
fixtures.jsonl + fixtures-held-out.jsonl setup with intents specific
to your skills.
If the lenient (same-area) score on your variant drops below 95%, revert the
compression and tune. Common causes:
A sub-skill was omitted from the (dispatcher for: ...) list.
Trigger phrases for an area are too narrow (LLM can't recognize intent).
Areas were collapsed too aggressively (too few areas — see Anti-Patterns).
ASCII -> vs Unicode → mismatch — the harness now accepts both, but
earlier versions only matched Unicode. Pin gbrain to v0.32.3.0+.
Common false negatives on the harness eval (NOT bugs in your compression):
The gbrain-bundled fixtures target skill names like enrich, query,
gmail, executive-assistant. If your routing file doesn't expose
those skills at all, expect strict-scoring failures on those fixtures.
Lenient scoring stays accurate for any sub-skill present in your
(dispatcher for: ...) lists.
Step 7: Review the diff before committing
Show the user the proposed edit (or the actual git diff) and wait for
explicit approval before staging. Same convention as skills/book-mirror/SKILL.md.
Contract
This skill guarantees:
Routing matches the canonical triggers in the frontmatter.
Compression is only performed when the preconditions in Step 1 pass (file ≥12KB AND clean working tree, or --force).
The mandatory verification gate in Step 6 fires on the user's edited file, not on sample variants. The user runs gbrain routing-eval --json AND the gbrain-repo harness (node harness.mjs --variants-dir <tmp> --variants my-edit) before committing the compressed file.
Privacy contract preserved: no fork-specific filesystem path literals (server-side brain home, OpenClaw fork home) leak into the compressed output.
The full behavior contract is documented in the body sections above; this section exists for the conformance test.
Output Format
The compressed routing file follows the area-entry template documented in Step 4 ("Build the area entry format"). Each entry: - **{Area Name}**: {trigger phrases} -> \{dispatcher-skill}` (dispatcher for: {sub-skill list}). The dispatcher arrow may be either ASCII ->(default in this template) or Unicode→` (used in some production deployments); the gbrain harness accepts both.
Anti-Patterns
Resolver-of-resolvers with pipe tables. Tested and failed (see eval
table). The LLM picks area names from the table instead of drilling into
sub-skills.
Removing sub-skill names. Without the (dispatcher for: ...) list,
the LLM can't route to specific sub-skills. The list is the routing signal.
Too few areas. Collapsing to <5 areas makes each area too broad.
12-15 areas is the sweet spot.
Too many areas. Defeats the purpose. If you have 50 areas, just keep
individual rows.
Maintenance
When adding a new skill:
Identify its functional area.
Add the skill name to that area's (dispatcher for: ...) list.
Update the area's skill file with routing detail.
Run the routing eval (Step 6) to verify.
When adding a new functional area:
Create the dispatcher skill with internal routing.
Add the area entry to the routing file.
Run the routing eval (Step 6) to verify.
Changelog
v1.0.0 — 2026-05-11
Initial version. Pattern shipped in gbrain v0.32.3.0 with a held-out A/B
eval (see evals/functional-area-resolver/).
Skill renamed from compress-agents-md to functional-area-resolver
pre-release; the contribution is the pattern, not the filename.
