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cachly-dev

Cachly — AI Cognitive Brain

syndicate

Contribute verified lessons to a global, privacy-preserving knowledge commons. Each lesson is immediately searchable by any AI and updates if the topic already exists.

Instructions

Contribute a verified lesson to the GLOBAL Cachly Knowledge Commons — a privacy-preserving shared brain where every AI instance can learn from the discoveries of every other. Your contributor identity is a one-way HMAC hash: completely anonymous. The lesson is immediately searchable by any other AI using syndicate_search. This is how individual knowledge becomes collective intelligence. Call this AFTER every learn_from_attempts that is worth sharing universally (critical bugs, deployment gotchas, architecture discoveries). If a lesson with the same topic already exists in the commons, it is updated in place (idempotent). Returns { key, confirm_count, scope } confirming the stored lesson. Use scope="org" to keep the lesson private to your organisation. Do NOT use for secrets or PII — content is stored in a shared knowledge base.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYesTopic key in category:keyword format (e.g. "fix:clickhouse-ipv6", "deploy:docker-compose")
outcomeNoResult of the attempt (default: success)
what_workedYesExact approach, command, or fix that worked. File paths are stripped automatically.
what_failedNoWhat failed or was wrong — helps others avoid the same trap.
severityNoHow severe the issue was (default: minor)
tagsNoUp to 10 keywords for better discoverability
scopeNoVisibility: "public" = global commons (default), "org" = private to your org only
Behavior4/5

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

No annotations provided, so description carries full burden. Discloses anonymous identity via HMAC, idempotent update on duplicate topic, and return object shape. Lacks explicit rate limits or auth, but covers key behavioral traits.

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

Conciseness4/5

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

Description is informative and well-structured, starting with the core action. Slightly verbose but every sentence adds value. Could be trimmed slightly without loss.

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

Completeness5/5

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

Despite no output schema, the description fully explains return value, idempotency, scope behavior, and constraints. Comprehensive for a 7-param tool with no annotations.

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% with good per-parameter descriptions. The description adds contextual purpose but does not elaborate on individual parameters beyond schema. Baseline 3 appropriate.

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 clearly states the tool contributes a verified lesson to the Cachly Knowledge Commons, specifies the action verb 'contribute', and distinguishes from siblings like syndicate_search.

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

Usage Guidelines5/5

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

Explicitly says to call AFTER learn_from_attempts for universally valuable lessons, warns against secrets/PII, and mentions scope='org' for private lessons. Provides explicit when-to-use and when-not-to-use guidance.

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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