analyze-chat-session

name	analyze-chat-session
description	Analyze the current conversation's tool calls, responses, errors, and approach to produce a structured feedback report for improving MCP tool usage with ARC-1. Use when the user asks to "analyze this session", "review my chat", "give feedback on tool usage", "how did I do", or wants a retrospective on a debugging session.

Analyze Chat Session

Analyze the current conversation's tool calls, responses, errors, and overall approach to produce a structured feedback report. The goal is continuous improvement of MCP tool usage patterns, prompt strategies, and ARC-1 server capabilities.

This skill is introspective — the LLM analyzes its own chat session (the conversation you're currently in, or a referenced one) and generates actionable feedback.

PRIVACY NOTICE — READ FIRST

Before using this skill's output outside the current system (e.g., pasting into a GitHub issue, sharing in a chat), the user MUST review it for sensitive data.

The analysis MUST NOT contain:

• SAP system URLs, hostnames, IP addresses, or ports
• Usernames, passwords, API keys, tokens, or credentials
• Client numbers, system IDs (SID), or environment identifiers
• Real business data (customer names, order numbers, financial values, employee data)
• File paths that reveal internal infrastructure (home directories, mount paths)
• Transport request numbers or package names that reveal internal naming conventions
• Any content from SAP table previews or SQL query results containing business data

The LLM must actively redact these from the output, replacing them with generic placeholders like <SAP_HOST>, <USERNAME>, <OBJECT_NAME>, <TABLE_DATA_REDACTED>, etc.

After generating the report, remind the user: "Please review this report for any remaining sensitive information before sharing it outside your organization."

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Smart Defaults (apply silently, do NOT ask)

Setting	Default	Rationale
Output format	issue (GitHub issue-ready)	Most actionable format
Severity filter	All levels	Don't miss anything
Focus area	Entire session	Comprehensive analysis
Sensitive data	Redact all (see Privacy Notice)	Safe by default

Proceed immediately with Smart Defaults when triggered. No questions needed.

Input

The user triggers this skill at any point during (or after) a conversation. No additional input is required — the LLM analyzes the current chat context. Proceed immediately with Smart Defaults.

Optionally, the user may specify:

• Focus area — e.g., "focus on the write operations" or "focus on the search failures"
• Output format — issue (GitHub issue-ready), internal (detailed, for internal review), or summary (brief, 1 paragraph)
• Severity filter — only report issues above a threshold (e.g., "only blockers")

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Phase 1: Catalog the Session

Walk through the entire conversation and extract a structured catalog. For each turn, note:

1a. Identify the Goal

What was the user trying to accomplish? Categorize it:

Goal Category	Examples
Read / Explore	Read source code, understand a class, explore dependencies
Search / Discover	Find objects by name, search source code, locate implementations
Write / Modify	Create objects, update source, edit methods, batch operations
Diagnose / Debug	Syntax check, unit tests, ATC, short dumps, traces
Navigate / Understand	Find definition, find references, code completion
Query / Data	Table preview, freestyle SQL, data inspection
Context / Architecture	Dependency graphs, usage analysis, API contracts
Activate / Deploy	Activate objects, manage transports
Lint / Quality	Run linter, fix lint issues, check rules

1b. Catalog Tool Calls

For each MCP tool call in the session, record:

Field	What to capture
Tool	Which of the 11 tools was called (SAPRead, SAPSearch, etc.)
Operation	The specific action/type/include (e.g., SAPRead type=CLAS format=structured)
Intent	What information was the LLM trying to get?
Result	success, error, partial, empty, redundant
Error type	If failed: not_found, auth, safety, network, validation, btp_unsupported, session, other
Tokens	Rough estimate: was the response small (< 1KB), medium (1-10KB), or large (> 10KB)?
Necessary?	Was this call necessary, or could the info have been obtained differently?

1c. Identify Patterns

Look for these anti-patterns:

Pattern	Description	Impact
Shotgun search	Multiple broad searches before narrowing	Wasted tokens and time
Missing SAPContext	Reading dependencies one-by-one instead of using SAPContext	5-10x more calls than needed
Unstructured class read	Using plain SAPRead for a class instead of format=structured	Misses metadata, requires extra calls for includes
Retry without change	Repeating the same failing call without modifying parameters	Pure waste
Wrong type guess	Using wrong object type, then searching to find the right one	Could use SAPSearch first
Ignored error hints	Error response contains a hint (e.g., "use SAPSearch") but LLM didn't follow it	Missed recovery path
Redundant reads	Reading the same object multiple times in one session	Could cache/reuse from earlier response
Over-fetching	Reading entire objects when only a method or section was needed	Wasted tokens (use method param or edit_method)
Missing method listing	Modifying a class without first getting method="*" to understand the API	Blind modifications
Wrong action sequence	e.g., trying to activate before saving, or writing without reading first	Procedural error
Ignored BTP constraints	Attempting on-prem-only operations on BTP system	Known limitation, should check SAPManage features first

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Phase 2: Assess What Worked

2a. Successful Strategies

Identify and highlight things that worked well:

• Efficient tool selection — chose the right tool on the first try
• Good parameter usage — used format=structured, include=elements, method="*", etc.
• Effective error recovery — followed error hints, switched strategies after failures
• Context-first approach — used SAPContext/SAPSearch before diving into reads
• Batch operations — used batch_create or batch activate where appropriate
• Method surgery — used edit_method instead of full class rewrite

2b. What Partially Worked

Identify approaches that eventually succeeded but were inefficient:

• How many extra calls were needed?
• What was the corrective action?
• Could the first attempt have succeeded with different parameters?

2c. What Failed

Identify approaches that never worked:

• Was the information actually available through a different path?
• Was it a system limitation (BTP, permissions, missing feature)?
• Was it a tool limitation that should be reported?

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Phase 3: Generate Improvement Suggestions

3a. For the LLM (Prompt/Approach Improvements)

Suggestions the user can feed back into system prompts or skill instructions:

• Strategy improvements — "Start with SAPSearch before SAPRead when object type is uncertain"
• Parameter optimizations — "Use format=structured for class reads to reduce call count"
• Sequence improvements — "Read method listing before attempting method surgery"
• Recovery patterns — "When SAPContext fails for FUNC, pass the group= parameter explicitly"

3b. For ARC-1 (Server/Tool Improvements)

Suggestions that should become GitHub issues for the ARC-1 project:

Category	Example
Missing operation	"No way to read message class text by message number"
Poor error message	"404 on FUNC doesn't suggest using SAPSearch to find the function group"
Missing parameter	"SAPRead for DDLS should support reading specific annotations"
Performance	"SAPContext depth=2 timed out for large dependency graphs"
BTP gap	"Cannot read service binding details on BTP"
Documentation	"Tool description doesn't mention that method='*' lists all methods"
Safety false positive	"Legitimate read operation blocked by safety check"
Caching opportunity	"Same object read 4 times — server should hint about SAPContext caching"

3c. For the User (Workflow Improvements)

Suggestions for the user's workflow:

• "Provide the object type for live usage lookups to avoid an extra resolution call"
• "Use the explain-abap-code skill first to understand objects before modifying them"
• "Provide object type when possible to avoid search round-trips"

3d. Delivery Triage (Quick Wins vs Planned Work)

For every recommendation, classify delivery scope explicitly:

Bucket	Definition
Quick Win (This Session)	Low-risk prompt/skill/doc tweak that can be implemented immediately without new backend primitives
Needs Plan + Implementation Phase	Requires code changes, new ADT integration, schema/tool contract updates, or live SAP regression testing

Output two lists:

1. Quick wins to execute now (ordered by leverage)
2. Planned items (grouped into the minimum number of implementation plans)

This triage section is mandatory in issue and internal outputs.

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Phase 4: Produce the Report

Format: issue (Default — GitHub Issue Ready)

Generate a report structured for creating a GitHub issue. Use this template:

## Session Analysis: <brief goal description>

### Goal
<1-2 sentences: what the user was trying to accomplish>

### Approach Summary
<3-5 bullet points: high-level steps taken>

### Tool Call Statistics
- Total calls: N
- Successful: N (N%)
- Failed: N (N%)
- Redundant/unnecessary: N (N%)
- Tools used: SAPRead (N), SAPSearch (N), SAPWrite (N), ...

### What Worked Well
- <bullet points of effective strategies>

### What Didn't Work
- <bullet points with error classification>
  - **Error**: <redacted error summary>
  - **Root cause**: <analysis>
  - **Better approach**: <suggestion>

### Suggested ARC-1 Improvements

#### <Improvement 1 Title>
**Category**: Missing operation | Poor error message | Missing parameter | ...
**Severity**: blocker | major | minor | enhancement
**Description**: <what should change and why>
**Reproduction**: <generic steps, NO sensitive data>

#### <Improvement 2 Title>
...

### Delivery Triage
- **Quick Wins (this chat):**
  - <item>
- **Needs Plan + Implementation Phase:**
  - <item>
  - <item>

### Prompt Strategy Recommendations
- <numbered list of approach improvements for future sessions>

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*Generated by analyze-chat-session skill. **Review for sensitive data before sharing.***

Format: internal (Detailed Internal Review)

Same as issue but includes:

• Full tool call catalog table (with redacted parameters)
• Token usage estimates per call
• Sequence diagram of the call flow
• Detailed anti-pattern analysis with specific turn references

Format: summary (Brief)

A single paragraph summarizing: goal, outcome, top 2 things that worked, top 2 things that didn't, and the single most impactful improvement suggestion.

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Phase 5: Offer Next Steps

After presenting the report, offer:

1. "Create GitHub issues?" — For each suggested ARC-1 improvement, offer to draft a GitHub issue body (still requires user review before posting)
2. "Refine a specific finding?" — Dive deeper into one particular issue
3. "Retry the failed approach?" — If the analysis reveals a better strategy, offer to try it now
4. "Save as skill feedback?" — If the analysis reveals patterns that should be encoded in a skill, offer to update the relevant .claude/commands/*.md file

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Reference: ARC-1 Tool Capabilities Quick Reference

Use this reference to assess whether the LLM used the right tools. This is a concise mapping of common goals to optimal tool usage.

Read Operations (SAPRead)

Object Type	Key Parameters	Notes
CLAS	format=structured (metadata+all includes), method="*" (list methods), method="name" (single method source), include=testclasses|definitions|implementations|macros	Always prefer format=structured for initial read
DDLS	include=elements (field listing)	Field list is essential for understanding CDS views
FUNC	group=<FUGR> (required if not cached), expand_includes=true (full group source)	Group resolution is a common failure point
SRVB	Returns parsed binding info	Not raw DDL
TABLE_CONTENTS	maxRows, sqlFilter	Data preview — subject to blockData safety

Search (SAPSearch)

• searchType=object — by name/wildcard
• searchType=source_code — full-text (may be unavailable on some systems)

Write Sequence

1. Read first (understand current state)
2. SAPWrite action=create or action=update or action=edit_method
3. SAPActivate (single or batch with objects[])
4. SAPDiagnose action=syntax or action=unittest to verify

Context & Navigation

• SAPContext action=deps — dependency graph (preferred over manual reads)
• SAPContext action=usages — live reverse dependencies from SAP
• SAPNavigate action=definition — go-to-definition (needs URI + line/col + source)
• SAPNavigate action=references — where-used

Common Error Recovery Paths

Error	Recovery
404 Not Found	→ SAPSearch to verify name/type
FUNC without group	→ SAPSearch for function name, extract group from result
BTP type not supported	→ Check BTP_HINTS in error, use equivalent cloud type
Source search unavailable	→ Use SAPQuery or object search instead
SAPContext usages fails	→ Use SAPNavigate references as fallback
Activation fails	→ Read activation error, fix source, retry
Lock conflict	→ Object locked by another user, wait or use different object

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Error Handling

Situation	How to Handle
Very short session (< 3 tool calls)	Produce a brief summary, note that limited data makes analysis less useful
No tool calls in session	Report that no MCP tool usage was detected; suggest this skill is meant for sessions with SAP operations
All calls succeeded	Focus the report on efficiency and optimization opportunities
All calls failed	Focus on root cause analysis and whether the goal was achievable with current capabilities
Mixed results	Full analysis with emphasis on the failure→recovery patterns
Session used non-ARC-1 tools	Catalog those separately; focus ARC-1 analysis on the SAP-related calls

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Notes

When to Use This Skill

• After a complex debugging or development session
• When a new tool pattern was discovered through trial and error
• When multiple approaches failed and you want to document what was learned
• When preparing feedback for ARC-1 improvement
• When onboarding and wanting to capture learning patterns

What This Skill Does NOT Do

• No automated issue creation — it drafts issue content, but the user must review and create
• No system access — this skill only reads the chat history, it makes no MCP tool calls
• No cross-session analysis — it analyzes one conversation at a time
• No real-time monitoring — it's a post-hoc analysis, not a live dashboard