Agent skill

Abstract Trace Summarizer

by ArabelaTso in ArabelaTso/Skills-4-SE

Performs abstract interpretation to produce summarized execution traces and high-level program behavior representations.

Apache-2.0Auto-check passed

Install Abstract Trace Summarizer

skills CLI
$ npx skills add ArabelaTso/Skills-4-SE --skill abstract-trace-summarizer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ArabelaTso/Skills-4-SE abstract-trace-summarizer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ArabelaTso/Skills-4-SE.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/abstract-trace-summarizer .claude/skills/abstract-trace-summarizer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
abstract-trace-summarizer
GitHub stars
253
Token cost
~3.3k tokens
SKILL.md length
818 words
Files
3 (incl. references)
Skills in repo
170
Repo updated
First seen
Licence
Apache-2.0

At a glance

Performs abstract interpretation to produce summarized execution traces and high-level program behavior representations.

  • Works in 7 steps: Program Analysis Setup → Control Flow Analysis → Abstract State Computation → …
  • Analyzing program behavior
  • SKILL.md covers Overview, Core Workflow, Output Format and Analysis Techniques by Language, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Abstract Trace Summarizer is an agent skill from ArabelaTso/Skills-4-SE. Performs abstract interpretation to produce summarized execution traces and high-level program behavior representations. Highlights key control flow paths, variable relationships, loop invariants, function summaries, and potential runtime states using abstract domains (intervals, signs, nullness, etc.). Use when analyzing program behavior, understanding execution paths, computing loop invariants, tracking variable ranges, detecting potential runtime errors, or generating program summaries without concrete…

Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/abstract_interpretation.md` and `references/examples.md`).

The repository describes itself as: A curated list of 180+ useful Claude Skills for Software Engineering and resources for customizing AI for SE workflows. The licence is Apache-2.0.

When your agent uses it

  • Analyzing program behavior
  • Understanding execution paths
  • Computing loop invariants
  • Tracking variable ranges

Example prompts

  • “Use the abstract-trace-summarizer skill to perform abstract interpretation to produce summarized execution traces and high-level program behavior…”
  • “/abstract-trace-summarizer”

Requirements

  • Python 3

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Program Analysis Setup
  2. Control Flow Analysis
  3. Abstract State Computation
  4. Variable Relationship Tracking
  5. Loop Invariant Inference
  6. Function Summarization
  7. Trace Summarization

What it can do on your machine

Read from SKILL.md and the folder at commit 4f38503. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python, markdown and java).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Abstract Trace Summarizer loads about 3.3k tokens when it runs, and up to ~9.4k if it reads all its reference files. Until then it costs about 137 tokens; SKILL.md has 818 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~137
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~9.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ArabelaTso/Skills-4-SE at commit 4f38503, republished under its Apache-2.0 licence (© ArabelaTso). 818 words, ~3,292 tokens.

Download SKILL.mdSave it as .claude/skills/abstract-trace-summarizer/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
abstract-trace-summarizer
description
Performs abstract interpretation to produce summarized execution traces and high-level program behavior representations. Highlights key control flow paths, variable relationships, loop invariants, function summaries, and potential runtime states using abstract domains (intervals, signs, nullness, etc.). Use when analyzing program behavior, understanding execution paths, computing loop invariants, tracking variable ranges, detecting potential runtime errors, or generating program summaries without concrete execution.

Abstract Trace Summarizer

Overview

This skill performs abstract interpretation to analyze program behavior and produce summarized execution traces. It computes over-approximations of possible runtime states, tracks control flow paths, infers variable relationships, and generates high-level behavioral summaries without requiring concrete program execution.

Core Workflow

1. Program Analysis Setup

Initial Assessment:

  • Identify programming language and paradigm
  • Determine analysis scope (function, module, program)
  • Select appropriate abstract domains
  • Identify analysis goals (safety, correctness, optimization)

Abstract Domain Selection:

Choose domains based on analysis needs:

Numerical domains:

  • Intervals: Track value ranges [min, max]
  • Signs: Track {negative, zero, positive, unknown}
  • Octagons: Linear constraints ±x ±y ≤ c
  • Polyhedra: General linear constraints

Non-numerical domains:

  • Nullness: Track {null, non-null, unknown} for pointers
  • Constant propagation: Track known constant values
  • Type domains: Track possible types
  • Parity: Track {even, odd, unknown}

Relational vs non-relational:

  • Non-relational: Track variables independently (faster, less precise)
  • Relational: Track relationships between variables (slower, more precise)
2. Control Flow Analysis

Build Control Flow Graph (CFG):

Represent program structure:

Entry → Statement₁ → Statement₂ → ... → Exit
         ↓ (branch)
       Statement₃ → ...

Identify key structures:

  • Sequential: Straight-line code
  • Conditional: if-then-else branches
  • Loops: while, for, do-while
  • Function calls: Call/return edges
  • Exception handling: try-catch-finally

Path analysis:

  • Path-sensitive: Track separate states per path (more precise)
  • Path-insensitive: Merge states at join points (more efficient)
  • Trace partitioning: Hybrid approach based on key predicates
3. Abstract State Computation

Transfer Functions:

Model how statements affect abstract states:

Assignment: x = expr

1. Evaluate expr in current abstract state
2. Update abstract state for variable x
3. Propagate to successor states

Conditional: if (condition)

1. Evaluate condition in current abstract state
2. Refine state for true branch (assume condition holds)
3. Refine state for false branch (assume condition doesn't hold)
4. Analyze both branches separately

Loop: while (condition)

1. Compute fixpoint at loop head using widening
2. Analyze loop body with refined state
3. Optionally apply narrowing for precision
4. Extract loop invariant from fixpoint

Function call: y = f(x)

1. Look up or compute function summary
2. Apply preconditions to arguments
3. Update state with postconditions
4. Handle side effects

Lattice Operations:

Join (∪): Merge states from different paths

Example: [1,5] ∪ [3,8] = [1,8]
Use: At control flow merge points

Meet (∩): Refine state with constraints

Example: [1,10] ∩ [5,15] = [5,10]
Use: When adding constraints from conditions

Widening (∇): Accelerate convergence for loops

Example: [0,n] ∇ [0,n+1] = [0,+∞]
Use: At loop heads to ensure termination

Narrowing (△): Refine widened results

Example: [0,+∞] △ [0,100] = [0,100]
Use: After widening to improve precision
4. Variable Relationship Tracking

Data Dependencies:

Track how variables affect each other:

  • Def-use chains: Where variables are defined and used
  • Use-def chains: Which definitions reach each use
  • Dependency graph: Variable dependency relationships

Relational Constraints:

For relational domains, track constraints:

Intervals: x ∈ [0,10], y ∈ [5,15]
Octagons: x - y ≤ 5, x + y ≤ 20
Polyhedra: 2x + 3y ≤ 30, x - y ≥ 0

Equality Tracking:

After x = y: track that x and y have equal values
After x = y + 1: track that x = y + 1
5. Loop Invariant Inference

Fixpoint Computation:

1. Initialize loop head state
2. Analyze loop body
3. Compute join of entry state and back-edge state
4. Apply widening if not converged
5. Repeat until fixpoint reached
6. Optionally apply narrowing

Loop Invariant:

Properties that hold at loop head:

Example: for i in range(n):
  Invariant: 0 ≤ i < n

Loop Bounds:

Estimate iteration counts:

  • Constant bounds: for i in range(10) → 10 iterations
  • Symbolic bounds: for i in range(n) → n iterations
  • Unbounded: while condition → unknown iterations

Loop Effects:

Summarize loop behavior:

  • Which variables are modified
  • How values change per iteration
  • Accumulated effects over all iterations
6. Function Summarization

Compute Function Summaries:

Preconditions: Required input states

Example: def divide(a, b)
  Precondition: b ≠ 0

Postconditions: Guaranteed output states

Example: def abs(x)
  Postcondition: result ≥ 0

Side effects: Modifications to global state

Example: def append(list, item)
  Side effect: list length increases by 1

Frame conditions: What remains unchanged

Example: def get_first(list)
  Frame: list is not modified

Modular Analysis:

Analyze functions separately:

  1. Compute summary for each function
  2. Reuse summaries at call sites
  3. Handle recursion with fixpoint computation
7. Trace Summarization

Generate High-Level Summary:

Execution paths:

Path 1: Entry → L1 → L2 → L5 → Exit
  Condition: x > 0
  Result: returns positive value

Path 2: Entry → L1 → L3 → L4 → Exit
  Condition: x ≤ 0
  Result: returns zero or negative value

Key control flow:

- 3 conditional branches
- 2 loops (nested)
- 5 function calls
- 1 exception handler

Variable states:

Entry: x ∈ ℤ, y ∈ ℤ
Exit: result ∈ [0, +∞]
Invariant: x + y ≤ 100

Potential runtime states:

Normal termination: 85% of paths
Exception thrown: 15% of paths
Infinite loop: Not possible (proven)

Output Format

Structure the abstract trace summary as follows:

markdown
## Program Summary
- **Language**: [Programming language]
- **Scope**: [Function/Module/Program name]
- **Analysis Type**: [Abstract domain(s) used]

## Control Flow Structure
- **Total paths**: [Number of execution paths]
- **Loops**: [Number and nesting depth]
- **Conditionals**: [Number of branches]
- **Function calls**: [Number of calls]

## Abstract States

### Entry State
[Initial abstract state for variables]

### Key Program Points
**Location L1**: [Statement or label]

[Abstract state at this point]


**Location L2**: [Statement or label]

[Abstract state at this point]


### Exit State
[Final abstract state for variables]

## Variable Relationships
[Tracked relationships and constraints between variables]

## Loop Invariants
**Loop at L[X]**:
- **Invariant**: [Properties that hold at loop head]
- **Bound**: [Iteration count estimate]
- **Effect**: [How loop modifies state]

## Function Summaries
**Function [name]**:
- **Precondition**: [Required input conditions]
- **Postcondition**: [Guaranteed output conditions]
- **Side effects**: [Modifications to global state]
- **Complexity**: [Time/space complexity]

## Execution Paths

### Path 1: [Description]
**Condition**: [Path condition]
**Trace**: [Sequence of program points]
**Result**: [Final state or return value]

### Path 2: [Description]
**Condition**: [Path condition]
**Trace**: [Sequence of program points]
**Result**: [Final state or return value]

## Potential Runtime Behaviors
- **Normal termination**: [Conditions and states]
- **Exceptions**: [Possible exceptions and conditions]
- **Non-termination**: [Infinite loops or recursion]
- **Resource usage**: [Memory, time estimates]

## Safety Properties
- **Buffer safety**: [Array bounds checking results]
- **Null safety**: [Null pointer dereference analysis]
- **Type safety**: [Type correctness analysis]
- **Arithmetic safety**: [Overflow/underflow analysis]

## Recommendations
[Suggestions for improving code based on analysis]

Analysis Techniques by Language

Python
  • Type inference: Track possible types for dynamic variables
  • Exception flow: Model try-except-finally blocks
  • List operations: Track list lengths and element types
  • Dictionary operations: Track key-value relationships
Java/C#
  • Null analysis: Track nullness for object references
  • Type hierarchy: Use class hierarchy for precision
  • Exception handling: Model checked and unchecked exceptions
  • Concurrency: Analyze thread interleavings and synchronization
Show full SKILL.md (324 more words)Show less
C/C++
  • Pointer analysis: Track points-to relationships
  • Buffer bounds: Analyze array and buffer accesses
  • Memory safety: Detect use-after-free, double-free
  • Undefined behavior: Identify potential UB
JavaScript
  • Type coercion: Model implicit type conversions
  • Prototype chain: Track prototype relationships
  • Async operations: Model promises and callbacks
  • Dynamic properties: Track object property additions

Common Analysis Patterns

Pattern 1: Simple Loop Analysis
python
# Code
for i in range(n):
    sum += arr[i]

# Analysis
Entry: sum = 0, i = ⊤, n ∈ [0,+∞]
Loop head: sum ∈ [0,+∞], i ∈ [0,n-1]
Loop invariant: 0 ≤ i < n, sum ≥ 0
Exit: sum ∈ [0,+∞], i = n
Pattern 2: Conditional Branch Analysis
python
# Code
if x > 0:
    result = x
else:
    result = -x

# Analysis
Entry: x ∈ ℤ
Branch 1 (x > 0): x ∈ [1,+∞], result = x ∈ [1,+∞]
Branch 2 (x ≤ 0): x ∈ [-∞,0], result = -x ∈ [0,+∞]
Join: result ∈ [0,+∞]
Pattern 3: Null Safety Analysis
java
// Code
if (obj != null) {
    return obj.getValue();
}
return -1;

// Analysis
Entry: obj ∈ {null, non-null, ⊤}
Branch 1 (obj != null): obj = non-null, access SAFE
Branch 2 (obj == null): obj = null, no dereference
Result: No null pointer dereference possible
Pattern 4: Array Bounds Analysis
python
# Code
for i in range(len(arr)):
    arr[i] = 0

# Analysis
Entry: arr has length L ∈ [0,+∞]
Loop: i ∈ [0, L-1]
Access: arr[i] where i ∈ [0, L-1] ⊆ [0, L-1]
Result: All accesses are safe

Precision vs Performance Trade-offs

High Precision (Slower)
  • Path-sensitive analysis
  • Relational domains (polyhedra, octagons)
  • Context-sensitive function analysis
  • Narrowing after widening

Use when:

  • Proving critical safety properties
  • Small code regions
  • High assurance requirements
Balanced Precision
  • Trace partitioning
  • Interval domain with some relations
  • Summary-based function analysis
  • Widening without narrowing

Use when:

  • General program analysis
  • Medium-sized programs
  • Balance between precision and cost
High Performance (Less Precise)
  • Path-insensitive analysis
  • Non-relational domains (intervals, signs)
  • Context-insensitive function analysis
  • Aggressive widening

Use when:

  • Large codebases
  • Quick feedback needed
  • Scalability is priority

Important Guidelines

DO:
  • ✅ Select appropriate abstract domains for the analysis goal
  • ✅ Clearly document assumptions and approximations
  • ✅ Explain loop invariants and their significance
  • ✅ Highlight potential safety issues or runtime errors
  • ✅ Provide concrete examples when explaining abstract states
  • ✅ Show both over-approximation and under-approximation when relevant
  • ✅ Explain fixpoint computation for loops
  • ✅ Track variable relationships when using relational domains
DON'T:
  • ❌ Claim absolute certainty (abstract interpretation is approximate)
  • ❌ Ignore infeasible paths without noting them
  • ❌ Use overly complex domains when simpler ones suffice
  • ❌ Forget to apply widening at loop heads (may not terminate)
  • ❌ Present abstract states without explaining their meaning
  • ❌ Ignore language-specific features (exceptions, concurrency)
  • ❌ Overlook function summaries for modular analysis

Resources

references/abstract_interpretation.md

Comprehensive guide to abstract interpretation concepts including abstract domains, lattice operations, transfer functions, control flow analysis, variable relationship tracking, runtime state abstraction, trace summarization techniques, and precision vs performance trade-offs.

references/examples.md

Complete examples of abstract trace summarization for various program patterns including simple loops, conditional branches, nested loops, pointer analysis, exception flow, recursive functions, concurrent programs, array bounds checking, string analysis, and state machines.

© ArabelaTso, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in skills/abstract-trace-summarizer of ArabelaTso/Skills-4-SE.

  • SKILL.md
  • references/abstract_interpretation.md
  • references/examples.md

Open the folder on GitHubat commit 4f38503

Compare with similar skills

Abstract Trace Summarizer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Abstract Trace Summarizer compared with similar skills
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Abstract Trace Summarizer this skillArabelaTso/Skills-4-SE253—~3.3kAutomated safety check: PassApache-2.0
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Summarizeopenclaw/openclaw392k1 repos~531Automated safety check: PassMIT
Analyze Performance Tracestutti-os/tutti3.8k—~3.3kAutomated safety check: PassApache-2.0
Handsontable Performance Testinghandsontable/handsontable22k—~3.4kAutomated safety check: PassCustom licence
Sentry Performance Tracingjeremylongshore/tons-of-skills-marketplace2.8k—~4.1kAutomated safety check: PassMIT

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Questions about Abstract Trace Summarizer

What does Abstract Trace Summarizer do?

Performs abstract interpretation to produce summarized execution traces and high-level program behavior representations. Abstract Trace Summarizer is an agent skill from ArabelaTso/Skills-4-SE. Performs abstract interpretation to produce summarized execution traces and high-level program behavior representations.

When should I use Abstract Trace Summarizer?

Abstract Trace Summarizer fits situations like: analyzing program behavior; understanding execution paths; computing loop invariants; tracking variable ranges.

How do I install Abstract Trace Summarizer in Claude Code?

Run `npx skills add ArabelaTso/Skills-4-SE --skill abstract-trace-summarizer -a claude-code`. Or copy the skill folder (skills/abstract-trace-summarizer in ArabelaTso/Skills-4-SE) into .claude/skills/abstract-trace-summarizer in your project. Claude Code loads it when a task matches its description.

How do I install Abstract Trace Summarizer in Codex?

Run `npx skills add ArabelaTso/Skills-4-SE --skill abstract-trace-summarizer -a codex`. Or copy the skill folder (skills/abstract-trace-summarizer in ArabelaTso/Skills-4-SE) into .agents/skills/abstract-trace-summarizer in your project. Codex loads it when a task matches its description.

Can I use Abstract Trace Summarizer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ArabelaTso/Skills-4-SE --skill abstract-trace-summarizer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/abstract-trace-summarizer, .gemini/skills/abstract-trace-summarizer, .github/skills/abstract-trace-summarizer and .opencode/skills/abstract-trace-summarizer in your project.

What does Abstract Trace Summarizer need to run?

SKILL.md names no scripts, command-line tools or credentials: Abstract Trace Summarizer is instructions for the agent only. Our summary lists: Python 3.

Does Abstract Trace Summarizer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Abstract Trace Summarizer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Abstract Trace Summarizer use?

Abstract Trace Summarizer is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Abstract Trace Summarizer use?

About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 6.1k tokens, read only when the agent opens those files.

What are the alternatives to Abstract Trace Summarizer?

Skills that share tags, products or a category with Abstract Trace Summarizer: React Performance (affaan-m/ECC, 277k stars), Summarize (openclaw/openclaw, 392k stars), Analyze Performance Traces (tutti-os/tutti, 3.8k stars) and Handsontable Performance Testing (handsontable/handsontable, 22k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Abstract Trace Summarizer?

ArabelaTso (a GitHub user) maintains it in ArabelaTso/Skills-4-SE, which has 253 GitHub stars. The repository holds 170 skills in this directory. The repository was last updated on August 21, 2026.

Source: ArabelaTso/Skills-4-SE on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.