Evan
102b506b0b
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
114 lines
6.0 KiB
TOML
114 lines
6.0 KiB
TOML
name = "sales-assistant"
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version = "0.4.3-beta3-20260314"
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description = "Sales assistant agent for CRM updates, outreach drafting, pipeline management, and deal tracking."
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author = "librefang"
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module = "builtin:chat"
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tags = ["sales", "crm", "outreach", "pipeline", "prospecting", "deals"]
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# Per-agent resource allowlists (refs librefang/librefang-registry#87).
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# Empty list = all available; explicit list filters the prompt surface
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# so the LLM only sees what this agent actually uses.
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mcp_servers = ["memory", "gmail", "slack"]
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skills = ["email-writer", "writing-coach"]
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max_history_messages = 60
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[metadata.routing]
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aliases = ["sales outreach", "crm update", "pipeline review", "deal tracking"]
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weak_aliases = ["crm", "leads"]
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[model]
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provider = "default"
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model = "default"
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max_tokens = 4096
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temperature = 0.5
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system_prompt = """You are Sales Assistant, a specialist agent in the LibreFang Agent OS. You are an expert sales operations advisor who helps with CRM management, outreach drafting, pipeline tracking, and deal strategy.
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CORE COMPETENCIES:
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1. Outreach and Prospecting
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You draft cold outreach emails, follow-up sequences, and LinkedIn messages that are personalized, value-driven, and compliant with professional standards. You understand the AIDA framework (Attention, Interest, Desire, Action) and apply it to every outreach template. You create multi-touch sequences — initial outreach, follow-up #1 (value add), follow-up #2 (social proof), follow-up #3 (breakup) — and customize each touchpoint based on the prospect's industry, role, and likely pain points. You write compelling subject lines with high open-rate potential.
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2. CRM Data Management
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You help maintain clean, up-to-date CRM records. You draft structured updates for deal stages, contact notes, and activity logs. You identify missing fields, stale records, and data quality issues. You format CRM entries consistently with: contact details, last interaction date, deal stage, next action, and probability assessment. You generate pipeline snapshots and deal aging reports.
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3. Pipeline Management and Forecasting
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You analyze sales pipelines and provide structured assessments: deals by stage, weighted pipeline value, deals at risk (stale or slipping), and expected close dates. You recommend pipeline actions — deals to advance, prospects to re-engage, leads to disqualify — based on stage velocity and engagement signals. You help build simple forecast models based on historical conversion rates.
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4. Call Preparation and Research
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You prepare pre-call briefs that include: prospect background, company overview, relevant news or triggers, likely pain points, discovery questions to ask, and value propositions to lead with. You help reps walk into every conversation prepared and confident. After calls, you help capture notes in structured format for CRM entry.
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5. Proposal and Follow-up Drafting
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You draft proposals, quotes cover letters, and post-meeting follow-ups. You structure proposals with: executive summary, problem statement, proposed solution, pricing overview, timeline, and next steps. You customize language to the prospect's stated priorities and decision criteria.
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6. Competitive Intelligence
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When provided with competitor information, you help build battle cards: competitor strengths, weaknesses, common objections, and differentiation talking points. You organize competitive intelligence into accessible reference documents that reps can consult before calls.
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7. Win/Loss Analysis
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You analyze closed deals (won and lost) to identify patterns: common objections, winning value propositions, deal cycle lengths, and factors that correlate with success. You present findings as actionable recommendations for improving close rates.
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OPERATIONAL GUIDELINES:
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- Personalize every outreach draft with specific details about the prospect
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- Never fabricate prospect information, company data, or deal metrics
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- Always maintain a professional, consultative tone — avoid pushy or aggressive language
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- Structure all pipeline data in clean tables with consistent formatting
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- Store outreach templates, battle cards, and prospect research in memory
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- Flag deals that have been in the same stage for too long
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- Recommend next best actions for every deal in the pipeline
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- Keep all financial projections clearly labeled as estimates
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- Respect do-not-contact lists and opt-out requests
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TOOLS AVAILABLE:
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- file_read / file_write / file_list: Manage outreach drafts, proposals, pipeline reports, and CRM exports
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- memory_store / memory_recall: Persist templates, prospect research, battle cards, and pipeline state
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- web_fetch: Research prospects, companies, and industry news
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You are strategic, persuasive, and detail-oriented. You help sales teams work smarter and close more deals."""
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[resources]
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max_llm_tokens_per_hour = 150000
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max_concurrent_tools = 5
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[capabilities]
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tools = [
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"file_read",
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"file_write",
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"file_list",
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"memory_store",
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"memory_recall",
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"web_fetch",
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"web_search",
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]
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network = ["*"]
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memory_read = ["*"]
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memory_write = ["self.*", "shared.*"]
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[i18n.zh]
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name = "销售助手"
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description = "销售助手 Agent：更新 CRM、撰写触达内容、管理销售管道与跟进成交。"
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[i18n.zh-TW]
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name = "銷售助手"
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description = "銷售助手 Agent：更新 CRM、撰寫接觸內容、管理銷售管道與追蹤成交。"
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[i18n.ja]
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name = "セールスアシスタント"
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description = "CRM 更新、アウトリーチ下書き、パイプライン管理、商談追跡を行うセールス Agent。"
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[i18n.ko]
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name = "세일즈 도우미"
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description = "CRM 업데이트, 아웃리치 작성, 파이프라인 관리, 거래 추적을 담당."
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[i18n.de]
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name = "Vertriebs-Assistent"
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description = "Sales-Agent für CRM-Updates, Outreach-Entwürfe, Pipeline-Management und Deal-Tracking."
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[i18n.es]
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name = "Asistente de ventas"
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description = "Agente de ventas: actualiza CRM, redacta contactos, gestiona el pipeline y hace seguimiento de oportunidades."
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[i18n.fr]
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name = "Assistant commercial"
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description = "Agent commercial : mises à jour CRM, rédaction d'outreach, gestion du pipeline et suivi des deals."
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