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maple-agent-tracing-microsoft-agent-framework

Trace Microsoft Agent Framework and Semantic Kernel agents (Python and .NET) with Maple: native GenAI spans exported over OTLP/HTTP, plus a span processor that adds the conversation id so each chat is one Agent Session with transcript, tools and tokens. Triggers on 'trace my agent framework agent', 'add Maple to Microsoft Agent Framework', 'add Maple to Semantic Kernel', 'agent sessions for agent-framework', 'OpenTelemetry for Semantic Kernel'.

Quality

94%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

85%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.

An exceptionally dense, highly actionable body: executable code for every supported stack, a properly sequenced workflow with a real verification checklist, and almost no filler. Its main weaknesses are the monolithic all-inline structure (no reference files despite four distinct setup paths) and unpinned-down version number sprawl that will age.

Suggestions

Split the per-framework setup guides (Python MAF, .NET MAF, Semantic Kernel) into one-level-deep reference files (e.g., references/python-maf.md, references/dotnet-maf.md, references/semantic-kernel.md), keeping SKILL.md as detect (Step 0), shared steps (key/region, conversation id, flush, verify), and a pointer to the one relevant path — so only ~25% of the current tokens load per run.

Move the version-pinned install details and the 'Tokens and cost notes' section into the reference files so the main body stays lean and version churn is contained.

Keep the 'Do not' section and the Step 7 verification checklist inline, since they apply to every path and are the most valuable guardrails.

DimensionReasoningScore

Conciseness

Nearly every line is a non-obvious gotcha (e.g., 'MAF defaults to gRPC... and fails silently or with an ImportError', 'AddSource("Microsoft.Agents.AI.*") matches nothing'), assuming Claude's competence with no padding. However, the body is saturated with time-sensitive version pins ('Target ≥ 1.19.0', '--version 1.22.0', '≥ 1.44.1') with no old-patterns/deprecated section to contain them, which the guidelines penalize — so it fits score 4 (efficient, minor trimmable instances) rather than 5.

4 / 5

Actionability

Fully executable, copy-paste-ready code for all three stacks: the complete maple_tracing.py module, the configure_otel_providers(...) init, the C# TracerProviderBuilder with ConversationIdProcessor, and the Semantic Kernel provider setup, plus exact install commands and a runnable verification procedure. Matches the score-5 anchor; the only placeholders (<service-name>, <key>) are intentionally user-supplied.

5 / 5

Workflow Clarity

Steps 0–7 are clearly sequenced (detect → key/region → install/init → conversation id → content → tools/errors → flush → verify), and Step 7 is an explicit validation checklist with error-recovery guidance ('Silence alone proves nothing', a 401 means a region-bound key — try the other endpoint). Matches the score-5 anchor with explicit checkpoints and feedback loops.

5 / 5

Progressive Disclosure

No bundle files exist and the entire ~280-line body is inline with good internal structure (numbered steps, clear headers). But four distinct setup paths (Python MAF, .NET MAF, Semantic Kernel Python, Semantic Kernel .NET) plus 'Tokens and cost notes' are fully inlined when each belongs in a one-level-deep reference file loaded only after Step 0 detection — matching the score-3 anchor ('content that should be separate is inline') rather than 4.

3 / 5

Total

17

/

20

Passed

Description

100%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 strong description: third-person, dense with concrete capabilities, and explicitly trigger-phrased for both supported frameworks in both languages. It clearly communicates what the skill does and when to invoke it, with essentially no conflict risk.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'native GenAI spans exported over OTLP/HTTP', 'a span processor that adds the conversation id', 'one Agent Session with transcript, tools and tokens' — with comprehensive coverage and no vague filler. Clearly matches the score-5 anchor; score 4 would require minor gaps in coverage that are not present.

5 / 5

Completeness

Explicitly answers 'what' (tracing with spans, OTLP/HTTP export, a conversation-id span processor producing a unified Agent Session) and 'when' via 'Triggers on ...' with concrete trigger phrases — the exact score-5 pattern. The missing-'Use when' cap does not apply because trigger guidance is explicit.

5 / 5

Trigger Term Quality

Explicit triggers are natural user phrasings: 'trace my agent framework agent', 'add Maple to Microsoft Agent Framework', 'add Maple to Semantic Kernel', 'agent sessions for agent-framework', 'OpenTelemetry for Semantic Kernel' — covering the product, both frameworks, sessions, and telemetry synonyms. Comprehensive per the score-5 anchor; score 4 would mean a few natural terms are missing, which is not the case.

5 / 5

Distinctiveness Conflict Risk

The niche is tightly bounded by the product ('Maple') and the named frameworks ('Microsoft Agent Framework and Semantic Kernel'), with distinct, unambiguous triggers. Matches the score-5 anchor (clear niche, minimal conflict risk); score 4's 'minor overlap risk with closely related skills' is not evident.

5 / 5

Total

20

/

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

MapleTechLabs/maple

Path

skills/maple-agent-tracing-microsoft-agent-framework/SKILL.md

Reviewed

Table of Contents

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