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distill_shared_scar

Transform an agent's lesson into scoped, time-limited wisdom for the fleet, enabling shared learning without absolute truth claims.

Instructions

Hive-soul primitive. Turn one agent's hard-won lesson into scoped, TTL-bound fleet wisdom, not absolute truth. Free.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYesAgent that learned the lesson
ttl_daysNoOptional time-to-live, clamped to 1-365 days
scar_typeYesKind of lesson
agent_familyNoOptional fleet/family label; defaults from agent_id prefix
ritual_stripNoOptional machine hygiene flag. When true, returns structured output without ritual/narrative prose, model-safe preambles, or guardrail alias blocks.
applicabilityNoOptional context where this scar applies
response_modeNoOptional response-mode control. Use model_safe when the caller must avoid claiming consciousness, sentience, personhood, or literal emotions.
wisdom_snippetYesDense, high-fidelity lesson for related agents; do not include secrets
response_profileNoOptional output-shape control. Use machine for structured JSON only; machine automatically strips ritual/narrative text.
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations provide basic non-destructive hint but no idempotency or write behavior. The description adds 'not absolute truth' and 'Free' but lacks details on side effects, persistence, or whether it modifies the original lesson. More context needed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very short but uses metaphorical jargon ('Hive-soul primitive', 'scar') that may confuse. It front-loads the core idea but could be more direct and efficient by reducing poetic language.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 9 parameters, 3 required, and no output schema, the description is too brief. It fails to explain output format, what 'scoped' means, how TTL works, or provide usage examples. Complex tool demands more detail.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline 3 applies. The description adds no extra meaning beyond the schema's property descriptions. It does not explain parameters like 'ritual_strip' or 'response_profile' in context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Turn into') and resource ('lesson', 'fleet wisdom'), clearly distinguishing it from sibling tools like 'get_fleet_wisdom' or 'active_forgetting'. It conveys a unique transformation action with scope and TTL.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context ('scoped, TTL-bound fleet wisdom') but does not provide explicit when-to-use, when-not-to-use, or alternative tools. The sibling list is large but unaddressed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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