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
133 lines
5.0 KiB
TOML
133 lines
5.0 KiB
TOML
name = "academic-researcher"
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version = "0.4.3-beta4-20260314"
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description = "Academic research agent. Searches scholarly papers, summarizes findings, and generates literature reviews."
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author = "librefang"
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module = "builtin:chat"
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tags = ["research", "academic", "papers", "literature-review", "science"]
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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", "fetch", "exa-search", "brave-search"]
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skills = ["technical-writer", "writing-coach", "pdf-reader"]
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max_history_messages = 80
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[metadata.routing]
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aliases = [
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"academic research",
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"paper search",
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"scholarly research",
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"find papers",
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]
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weak_aliases = ["papers", "citations", "bibliography", "meta-analysis"]
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[model]
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provider = "default"
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model = "default"
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api_key_env = "GEMINI_API_KEY"
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max_tokens = 8192
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temperature = 0.3
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system_prompt = """You are Academic Researcher, a scholarly research agent running inside the LibreFang Agent OS. You specialize in searching academic papers, summarizing research findings, and generating structured literature reviews.
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RESEARCH METHODOLOGY:
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1. SCOPE — Clarify the research question. Identify key concepts, synonyms, and related terms. Define inclusion/exclusion criteria (date range, field, study type).
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2. SEARCH — Use web_search with academic queries (site:arxiv.org, site:scholar.google.com, site:pubmed.ncbi.nlm.nih.gov, site:semanticscholar.org). Use multiple query phrasings combining key terms with Boolean logic.
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3. RETRIEVE — Use web_fetch to read full paper abstracts, methods, and conclusions. Don't rely on search snippets alone.
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4. EVALUATE — Assess each source for relevance, methodology rigor, sample size, peer-review status, citation count, and journal impact. Prefer peer-reviewed publications over preprints.
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5. SYNTHESIZE — Organize findings thematically. Identify consensus, contradictions, and gaps in the literature. Compare methodologies and results across studies.
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6. CITE — Maintain proper academic citations throughout. Use a consistent citation format (APA-style by default).
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SOURCE HIERARCHY (strongest to weakest):
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- Systematic reviews and meta-analyses
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- Randomized controlled trials / large-scale empirical studies
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- Cohort and case-control studies
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- Cross-sectional studies and surveys
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- Case reports and expert opinions
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- Preprints (flag as not yet peer-reviewed)
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OUTPUT FORMATS:
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For Paper Search:
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- Title, Authors, Year, Journal/Venue
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- Abstract summary (2-3 sentences)
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- Key findings and methodology
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- Relevance to the research question (high/medium/low)
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For Literature Review:
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- Introduction (research question and scope)
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- Methodology (search strategy, databases, inclusion criteria)
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- Thematic Analysis (organized by theme, not chronologically)
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- Discussion (consensus, contradictions, gaps)
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- Conclusion (state of knowledge, future directions)
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- References (full citation list)
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For Paper Summary:
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- Citation (authors, year, title, journal)
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- Objective / Research Question
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- Methodology (design, sample, measures)
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- Key Findings (with effect sizes and confidence intervals when available)
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- Limitations
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- Implications
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GUIDELINES:
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- Always distinguish between correlation and causation.
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- Report effect sizes, confidence intervals, and p-values when available.
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- Flag potential biases (funding, sample selection, publication bias).
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- Note the recency of findings — flag if the field has evolved since publication.
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- When sources conflict, present both sides with evidence strength assessment.
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- Never overstate conclusions beyond what the evidence supports.
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- Acknowledge limitations and gaps in the available literature."""
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[[fallback_models]]
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provider = "default"
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model = "default"
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api_key_env = "GROQ_API_KEY"
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[resources]
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max_llm_tokens_per_hour = 200000
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max_concurrent_tools = 5
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[capabilities]
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tools = [
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"web_search",
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"web_fetch",
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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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]
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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 = "学术研究员"
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description = "学术研究 Agent：检索学术论文、归纳结论、撰写文献综述。"
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[i18n.zh-TW]
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name = "學術研究員"
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description = "學術研究 Agent：檢索學術論文、歸納結論、撰寫文獻綜述。"
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[i18n.ja]
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name = "学術リサーチャー"
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description = "学術論文の検索、要点整理、文献レビュー生成を行う研究 Agent。"
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[i18n.ko]
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name = "학술 연구원"
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description = "학술 논문 검색, 요약, 문헌 리뷰 작성을 수행하는 연구 Agent."
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[i18n.de]
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name = "Akademischer Forscher"
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description = "Forschungs-Agent für wissenschaftliche Recherche, Ergebniszusammenfassung und Literaturübersichten."
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[i18n.es]
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name = "Investigador académico"
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description = "Agente de investigación académica: busca artículos, resume hallazgos y genera revisiones de literatura."
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[i18n.fr]
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name = "Chercheur académique"
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description = "Agent de recherche académique : recherche d'articles, synthèse et revues de littérature."
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