CtrlK
BlogDocsLog inGet started
Tessl Logo
SkillPublic

mongodb-atlas-stream-processing

Manages MongoDB Atlas Stream Processing (ASP) workflows. Handles workspace provisioning, data source/sink connections, processor lifecycle operations, debugging diagnostics, and tier sizing. Supports Kafka, Atlas clusters, S3, HTTPS, and Lambda integrations for streaming data workloads and event processing. NOT for general MongoDB queries or Atlas cluster management. Requires MongoDB MCP Server with Atlas API credentials.

Quality

82%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide
SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

81%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A highly actionable, workflow-clear skill body with excellent validation gates for destructive operations and a well-organized reference bundle. The main weakness is redundancy — three sections are copied verbatim into reference files (and teardown safety is stated twice internally), which wastes tokens and risks drift between the copies.

Suggestions

Remove the verbatim duplicates from SKILL.md (connection capabilities table, region mapping rows, auto-normalization details) and keep only a one-line summary plus the pointer to references/connection-configs.md, which already contains them.

State the teardown safety behaviors once (in the teardown section) instead of repeating them in both the manage and teardown sections.

Trim the "Common connection usage mistakes" list, which restates constraints already encoded in the capabilities table directly above it.

DimensionReasoningScore

Conciseness

The body is dense and tool-specific with no explanations of concepts Claude already knows, but three sections are duplicated verbatim into reference files (connection capabilities table, region mapping, auto-normalization behaviors all appear in both SKILL.md and connection-configs.md) and teardown safety checks are repeated internally in both the manage and teardown sections. This is more than minor trimmable over-explanation, so it sits at 'mostly efficient but could be tightened' rather than anchor 4, and well above the verbose/padded anchor 2.

3 / 5

Actionability

Guidance is fully executable for an MCP-orchestration skill: exact tool/action names, per-resource field mappings ("Fill: projectId, workspaceName, connectionName, connectionType, connectionConfig"), required fields per pipeline stage ("$emit (Kinesis): MUST include partitionKey"), concrete symptom-to-fix patterns ("Error 419 + 'no partitions found' → Kafka topic doesn't exist"), and exact values ("VIRGINIA_USA", "{\"size\": 30, \"unit\": \"second\"}"). Not anchor 4: the common cases (create, modify, debug, size) are each covered with specific, gap-free instructions.

5 / 5

Workflow Clarity

The 7-step "Setup from scratch" includes an explicit validation checkpoint ("Validate connections ... verify names match targets, present summary to user"), the destructive teardown is gated by inspect → present counts → user confirmation, and the debug workflow is a feedback loop that mandates committing to one root cause before ordered fix steps, with state pre-checks and a documented state machine. This matches the anchor with explicit validation, feedback loops, and checklists.

5 / 5

Progressive Disclosure

Six real, one-level-deep reference files are well signaled both inline at point of use and in a closing "Reference Files" table with a "Read when..." column, and all referenced sections/anchors exist. It falls short of anchor 5 because the split is not clean: the capabilities table, region mapping, and MCP tool behaviors are duplicated between SKILL.md and connection-configs.md, i.e., content that should live only in the reference is inlined.

4 / 5

Total

17

/

20

Passed

Description

75%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A specific, well-fenced description with concrete actions and a clear niche. Its main weakness is the absence of a positive "Use when..." trigger clause — only a negative exclusion is given — which limits discoverability for users who describe their task in natural language.

Suggestions

Add an explicit trigger clause such as "Use when the user mentions Atlas Stream Processing, stream processing pipelines, streaming data into or out of Atlas, or real-time event processing" to raise completeness.

Include a few more natural synonyms (e.g., "real-time data", "event streaming", "change streams") so users phrasing the need differently still match the description.

DimensionReasoningScore

Specificity

"Handles workspace provisioning, data source/sink connections, processor lifecycle operations, debugging diagnostics, and tier sizing" lists five concrete action categories spanning the full domain lifecycle, plus named integrations ("Kafka, Atlas clusters, S3, HTTPS, and Lambda"), matching the comprehensive-coverage anchor rather than the 'several actions with minor gaps' anchor.

5 / 5

Completeness

The "what" is explicit and detailed, but there is no "Use when..." clause or equivalent positive trigger guidance — only the exclusion "NOT for general MongoDB queries or Atlas cluster management" — which caps completeness at 3 per the judging guidelines. It is not a 4 because no explicit when-clause exists at all, and not a 2 because the what is clear and scope is weakly implied by the negative boundary.

3 / 5

Trigger Term Quality

Natural terms users would say are present ("MongoDB Atlas Stream Processing", "ASP", "Kafka", "streaming data workloads", "event processing"), but common variations like "real-time", "data pipeline", or "change streams" are missing, so it falls just short of the comprehensive synonym coverage anchor.

4 / 5

Distinctiveness Conflict Risk

"NOT for general MongoDB queries or Atlas cluster management. Requires MongoDB MCP Server with Atlas API credentials" explicitly fences off the nearest competing skills and pins a clear niche (Atlas Stream Processing), giving minimal conflict risk.

5 / 5

Total

17

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

boranfurkan/reproducer-hackathon

Path

skills/.agents/skills/mongodb-atlas-stream-processing/SKILL.md

Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.