All 32 agent manifests and 17 hands shipped with empty mcp_servers /
skills lists, which the kernel interprets as "no filter" — every
globally-configured MCP server's tools and every installed skill get
injected into the prompt on every LLM call. On a typical instance (9
MCP servers, ~85 MCP tools + ~82 built-in tools) that's ~50k input
tokens per turn spent on definitions the agent never uses.
Changes
-------
32 agents/*/agent.toml:
- mcp_servers: 1-4 per agent. memory wherever state persists across
turns; fetch / exa-search / brave-search only where the prompt
actually calls for web; git / github / filesystem on engineering
agents; gmail / google-calendar / linear / jira on productivity
agents whose prompts mention them.
- skills: per-role allowlist driven by what the system_prompt names
(e.g. coder → rust/python/typescript/git/shell-scripting; devops-
lead → docker/kubernetes/terraform/ansible/ci-cd/helm/prometheus/
sysadmin). Generalists (assistant) keep skills = [] (see "Open
items" below).
- skills_disabled = true on the four short-conversational agents
(hello-world, recipe-assistant, health-tracker, home-automation).
Their system prompts never instruct the LLM to consult any skill,
so loading all 60 was pure waste. They also drop the explicit
max_history_messages override and inherit the kernel default (60).
- max_history_messages tiered by workload shape:
60 short conversational (hello-world, recipe, health-tracker,
home-automation) — inherits the rising kernel default
(`DEFAULT_MAX_HISTORY_MESSAGES = 60`); no override needed.
60 single-turn task agents (writer, translator, doc-writer,
email-assistant, customer-support, sales-assistant, recruit-
er, social-media, personal-finance, tutor, travel-planner,
meeting-assistant, ops, devops-lead, planner) — explicit
override at the same value to lock the cap if the kernel
default moves again.
80 multi-step / tool-heavy (coder, debugger, architect, code-
reviewer, test-engineer, security-auditor, analyst, data-
scientist, academic-researcher, researcher, legal-assistant)
120 coordinators (assistant, orchestrator) — long multi-agent
sessions where prompt-cache continuity is critical
All values sit at or above the kernel default. Pinning lower
would thrash the prompt cache (the failure mode #91 fixed for
the creator hand by *raising* the cap, not lowering it).
17 hands/*/HAND.toml:
- hand-level mcp_servers / skills now declared on every hand, so
every [agents.*] inside inherits a sensible allowlist.
- skills_disabled = true placed on each [agents.*] inside clip and
creator (pure media pipelines that don't benefit from any skill).
HandDefinitionRaw in librefang-hands does NOT have a top-level
skills_disabled field — declaring it at the hand top level would
be silently dropped by serde, so the setting must live on the
AgentManifest of each sub-agent role.
- devteam: expand existing mcp_servers = ["github"] to include
memory / git / filesystem; populate skills with the expected
dev-team expertise (replacing the placeholder skills = []).
- wiki: replace placeholder mcp_servers = [] with [memory, fetch,
filesystem]. Hand-level skills stays [].
- lead: hand-level skills was originally [email-writer, writing-
coach, interview-prep]; interview-prep is for job-interview
preparation, not lead generation. Replaced with data-analyst
(used by the qualification-scoring step in the prompt).
schema.toml: register mcp_servers / skills / max_history_messages on
the agent field schema so machine consumers (RegistrySchema in
librefang-types) see the new top-level fields. The
max_history_messages description now points at
librefang_runtime::agent_loop::DEFAULT_MAX_HISTORY_MESSAGES (60
today) by name, so the schema doesn't go stale when the constant
moves again.
agents/README.md: example block + "Adding a New Agent" checklist
mention the allowlists; max_history_messages example is shown
commented out with a prompt-cache caveat.
Open items
----------
`assistant` (the default user-facing agent) keeps `skills = []`
deliberately. It is the generalist entry point — capping its skill
surface at a small allowlist would defeat its "delegate to any
specialist" job. The trade-off is that this single agent still pays
the full skill-definition load on every turn; operators who want a
strict allowlist for `assistant` can override it after install.
Why not adopt PR #89's approach
-------------------------------
#89 covers similar ground but with three issues this PR avoids:
1. mcp_servers = ["_none"] sentinel. #89's body explicitly notes
it's pending upstream librefang#4808 (mcp_disabled). Shipping a
magic-string today means coming back later to clean it up. This
PR uses real allowlists.
2. max_history_messages = 8 / 12 / 15 / 20. Far below today's
kernel default (60) and #91's direction for long-workflow hands
(80–120). Every turn that hits the cap invalidates the cached
prompt prefix; the cost of cache misses exceeds the saving from
shorter history. This PR uses 60–120.
3. Doubling max_llm_tokens_per_hour (coder 200k→500k, assistant
300k→500k) widens the per-agent budget — the opposite direction
from #87's "reduce per-call cost" goal. Left to the operator's
instance-specific tuning.
Refs librefang/librefang-registry#87, librefang/librefang-registry#89
Hands Registry
Hands are pre-packaged capability bundles that compose agents, tools, skills, MCP servers, and plugins into a working application. Installing a hand gives you a complete, ready-to-use workflow — not just a single agent.
"You have many hands helping you."
A hand can contain one agent (single-agent) or multiple coordinated agents (multi-agent). Each agent in a multi-agent hand can have its own role-specific skills, model config, and capability restrictions.
File Format
Each hand lives in its own subdirectory:
hands/
├── researcher/
│ ├── HAND.toml # required: hand definition
│ └── SKILL.md # optional: shared reference knowledge for all agents
├── devteam/
│ ├── HAND.toml
│ ├── SKILL-pm.md # optional: role-specific knowledge for PM agent
│ ├── SKILL-engineer.md # optional: role-specific knowledge for Engineer agent
│ └── SKILL-qa.md # optional: role-specific knowledge for QA agent
HAND.toml format
id = "researcher"
version = "1.1.1"
name = "Researcher Hand"
description = "Autonomous deep researcher — exhaustive investigation, cross-referencing, fact-checking, and structured reports"
category = "productivity" # productivity | development | data | content | communication
icon = "lucide:flask-conical"
# Tools available to all agents in this hand
tools = [
"shell_exec", "file_read", "file_write", "web_fetch", "web_search",
"memory_store", "memory_recall", "knowledge_query", "event_publish",
]
# MCP servers all agents can use
mcp_servers = ["github"]
# Skills allowlist (empty = all available)
skills = []
# Plugin allowlist
allowed_plugins = ["todo-tracker", "auto-summarizer"]
# ─── Routing ──────────────────────────────────────────────────────────────────
[routing]
aliases = ["deep research", "investigate", "fact check"] # exact activation phrases
weak_aliases = ["research", "look into"] # keyword hints
# ─── Configurable settings ────────────────────────────────────────────────────
[[settings]]
key = "research_depth"
label = "Research Depth"
description = "How exhaustive each investigation should be"
setting_type = "select" # select | toggle | text
default = "thorough"
[[settings.options]]
value = "quick"
label = "Quick (5-10 sources, 1 pass)"
[[settings.options]]
value = "thorough"
label = "Thorough (20-30 sources, cross-referenced)"
# ─── Single-agent definition ──────────────────────────────────────────────────
[agent]
name = "researcher"
base = "researcher" # inherits from agents/researcher/agent.toml
[agent.model]
system_prompt = """Custom prompt override..."""
# ─── Multi-agent definition (alternative to [agent]) ─────────────────────────
[agents.pm]
coordinator = true
base = "planner" # inherits from agents/planner/agent.toml
invoke_hint = "Task coordination and issue triage"
[agents.engineer]
base = "coder"
invoke_hint = "Implementation"
[agents.qa]
base = "test-engineer"
invoke_hint = "Quality assurance and validation"
# ─── Dashboard metrics ────────────────────────────────────────────────────────
[dashboard]
[[dashboard.metrics]]
label = "Reports Written"
memory_key = "metric_reports_written"
format = "number"
# ─── i18n ─────────────────────────────────────────────────────────────────────
[i18n.zh]
name = "研究员"
description = "自主深度研究员 — 详尽调查、交叉核实、事实核查与结构化报告"
Installing and Using Hands
# List all available hands
librefang catalog hands
# Install a hand
librefang hand install researcher
# Install with a specific agent name
librefang hand install researcher --name my-researcher
# List installed hands
librefang hand list
# Remove a hand
librefang hand remove my-researcher
All Hands (18 total)
Productivity
| ID | Name | Category | Description |
|---|---|---|---|
| researcher | Researcher Hand | productivity | Autonomous deep researcher — exhaustive investigation, cross-referencing, fact-checking, and structured reports |
| strategist | Strategist Hand | productivity | Autonomous strategy analyst — market research, competitive analysis, business planning, and strategic recommendations |
| wiki | Wiki Hand | productivity | LLM-maintained personal knowledge base — builds an Obsidian-compatible wiki from raw sources with provenance tracking |
| browser | Browser Hand | productivity | Autonomous web browser — navigates sites, fills forms, clicks buttons, and completes multi-step web tasks |
Development
| ID | Name | Category | Description |
|---|---|---|---|
| devteam | Dev Team | development | Autonomous software development team — PM triages issues, Engineer implements, QA validates |
| devops | DevOps Hand | development | Autonomous DevOps engineer — CI/CD management, infrastructure monitoring, deployment automation, and incident response |
| apitester | API Tester Hand | development | Autonomous API testing agent — endpoint discovery, request validation, load testing, and regression detection |
Data
| ID | Name | Category | Description |
|---|---|---|---|
| analytics | Analytics Hand | data | Autonomous data analytics agent — data collection, analysis, visualization, dashboards, and automated reporting |
| collector | Collector Hand | data | Autonomous intelligence collector — monitors any target continuously with change detection and knowledge graphs |
| lead | Lead Hand | data | Autonomous lead generation — discovers, enriches, and delivers qualified leads on a schedule |
| predictor | Predictor Hand | data | Autonomous future predictor — collects signals, builds reasoning chains, makes calibrated predictions, and tracks accuracy |
| trader | Trading Hand | data | Autonomous market intelligence and trading engine — multi-signal analysis, adversarial bull/bear reasoning, and strict risk management |
Content
| ID | Name | Category | Description |
|---|---|---|---|
| clip | Clip Hand | content | Turns long-form video into viral short clips with captions and thumbnails |
| creator | Creator Hand | content | AI media studio — generates images, videos, music, and speech from text prompts |
Communication
| ID | Name | Category | Description |
|---|---|---|---|
| LinkedIn Hand | communication | Autonomous LinkedIn manager — profile optimization, content creation, networking, and professional engagement | |
| Reddit Hand | communication | Autonomous Reddit manager — monitors subreddits, posts content, replies to threads, and tracks engagement | |
| Twitter Hand | communication | Autonomous Twitter/X manager — content creation, scheduled posting, engagement, and performance tracking |
Data (additional)
| ID | Name | Category | Description |
|---|---|---|---|
| clip | Clip Hand | content | Turns long-form video into viral short clips with captions and thumbnails |
Resource Composition Summary
| Resource | How to compose | Notes |
|---|---|---|
| Agent templates | base = "coder" on [agents.*] |
Inherits prompt, model config, fallbacks from agents/coder/agent.toml |
| Tools | tools = [...] at hand level |
All agents in the hand share these built-in tools |
| Skills | skills = [...] at hand level |
Empty list means all available skills are allowed |
| MCP servers | mcp_servers = [...] at hand level |
Agent interacts via MCP tools, not hardcoded API calls |
| Plugins | allowed_plugins = [...] at hand level |
Empty list means all installed plugins are allowed |
| Per-agent knowledge | SKILL-{role}.md files |
Different reference prompts per agent role |
| Per-agent capabilities | [agents.*.capabilities] |
Fine-grained shell / network / memory per agent |
Adding a New Hand
- Create
hands/<name>/HAND.tomlwith at leastid,name,description, andcategory. - Add
SKILL.md(shared) orSKILL-{role}.md(per-agent) files for reference knowledge. - Use
base = "agent-name"in each[agents.*]block to inherit from existing agent templates. - Specify
mcp_servers,skills, andallowed_pluginsfor resource composition. - Ensure
idmatches the directory name. - Run
python scripts/validate.py. - Submit a PR.
See CONTRIBUTING.md for the full guide.