Fory Release
apache/fory
Prepare an Apache Fory release candidate from a clean release branch, including the version bump, RC tag, JVM staging, ASF source artifacts, SVN upload, and vote email.
Automatically infer loop invariants for code verification and correctness proofs.
$ npx skills add ArabelaTso/Skills-4-SE --skill invariant-inference -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ArabelaTso/Skills-4-SE invariant-inference --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/ArabelaTso/Skills-4-SE.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/invariant-inference .claude/skills/invariant-inference && rm -rf skills-srcUse ~/.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/
Install the "invariant-inference" agent skill from https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/invariant-inference into .claude/skills/invariant-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "invariant-inference", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/invariant-inferenceType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add ArabelaTso/Skills-4-SE --skill invariant-inference -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ArabelaTso/Skills-4-SE invariant-inference --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ArabelaTso/Skills-4-SE.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/invariant-inference .agents/skills/invariant-inference && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "invariant-inference" agent skill from https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/invariant-inference into .agents/skills/invariant-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "invariant-inference", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ArabelaTso/Skills-4-SE --skill invariant-inference -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ArabelaTso/Skills-4-SE invariant-inference --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ArabelaTso/Skills-4-SE.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/invariant-inference .cursor/skills/invariant-inference && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "invariant-inference" agent skill from https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/invariant-inference into .cursor/skills/invariant-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "invariant-inference", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/ArabelaTso/Skills-4-SE.git --path skills/invariant-inference--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add ArabelaTso/Skills-4-SE --skill invariant-inference -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ArabelaTso/Skills-4-SE invariant-inference --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ArabelaTso/Skills-4-SE.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/invariant-inference .gemini/skills/invariant-inference && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "invariant-inference" agent skill from https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/invariant-inference into .gemini/skills/invariant-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "invariant-inference", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install ArabelaTso/Skills-4-SE invariant-inferenceInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add ArabelaTso/Skills-4-SE --skill invariant-inference -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ArabelaTso/Skills-4-SE.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/invariant-inference .github/skills/invariant-inference && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "invariant-inference" agent skill from https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/invariant-inference into .github/skills/invariant-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "invariant-inference", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ArabelaTso/Skills-4-SE --skill invariant-inference -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install ArabelaTso/Skills-4-SE invariant-inference --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ArabelaTso/Skills-4-SE.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/invariant-inference .opencode/skills/invariant-inference && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "invariant-inference" agent skill from https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/invariant-inference into .opencode/skills/invariant-inference/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "invariant-inference", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
invariant-inferenceAutomatically infer loop invariants for code verification and correctness proofs.
Invariant Inference is an agent skill from ArabelaTso/Skills-4-SE. Automatically infer loop invariants for code verification and correctness proofs. Use when analyzing loops to identify properties that hold throughout execution, generating assertions for verification, proving loop correctness, or documenting loop behavior. Supports Python, Java, C/C++, and language-agnostic analysis. Generates invariants as code assertions (assert statements). Triggers when users ask to infer invariants, find loop properties, generate loop assertions, prove loop correctness, or verify loop…
Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/invariant-patterns.md`).
It works with C++, Java and Python. 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.
6 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 4f38503. It shows what the files ask for, not the result of running them.
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.
No scripts in the folder and no shell commands in SKILL.md (its code samples are python, java and c).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Invariant Inference loads about 3.1k tokens when it runs, and up to ~4.6k if it reads all its reference files. Until then it costs about 136 tokens; SKILL.md has 466 words of instructions outside code blocks.
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.
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.
The full file from ArabelaTso/Skills-4-SE at commit 4f38503, republished under its Apache-2.0 licence (© ArabelaTso). 466 words, ~3,096 tokens.
.claude/skills/invariant-inference/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.Analyze loops and automatically infer invariants—properties that remain true throughout loop execution. Generate these as code assertions for verification and correctness proofs.
First, locate and understand the loop to analyze:
Loop types to recognize:
for loops with index variableswhile loops with conditionsdo-while loopsExtract key information:
Understand what the loop does:
Categorize the loop:
Identify patterns:
Generate invariants for each applicable category. See invariant-patterns.md for comprehensive patterns.
Properties about variable ranges:
# Loop: for i in range(n)
assert 0 <= i < n
# Loop: while i < len(arr)
assert 0 <= i <= len(arr)
# Loop: two pointers
while left < right:
assert 0 <= left <= right < len(arr)Properties relating variables:
Sum/Accumulation:
total = 0
for i in range(len(arr)):
assert total == sum(arr[0:i]) # Invariant before update
total += arr[i]
assert total == sum(arr) # Post-conditionMax/Min:
max_val = arr[0]
for i in range(1, len(arr)):
assert max_val == max(arr[0:i])
if arr[i] > max_val:
max_val = arr[i]Product:
product = 1
for i in range(len(arr)):
assert product == arr[0] * arr[1] * ... * arr[i-1]
product *= arr[i]Properties showing termination:
# Decreasing to zero
while n > 0:
assert n > 0 # Still positive
n -= 1
assert n >= 0 # Non-negative after decrement
# Increasing to limit
i = 0
while i < n:
assert i < n # Not yet at limit
i += 1
assert i <= n # At most nProperties about structure integrity:
Sorted sublists:
# Insertion sort
for i in range(1, len(arr)):
assert is_sorted(arr[0:i]) # Prefix is sorted
# ... insert arr[i] into sorted positionPartition property:
# Partitioning around pivot
while left < right:
assert all(arr[j] <= pivot for j in range(0, left))
assert all(arr[j] >= pivot for j in range(right, len(arr)))
# ... move pointersSize invariants:
result = []
for i in range(len(items)):
assert len(result) == i # Processed i items so far
if condition(items[i]):
result.append(items[i])Convert inferred invariants into code assertions:
def find_maximum(arr):
"""Find maximum element in array."""
assert len(arr) > 0, "Array must not be empty" # Pre-condition
max_val = arr[0]
for i in range(1, len(arr)):
# Loop invariants
assert 0 < i < len(arr), "Index in valid range"
assert max_val == max(arr[0:i]), "max_val is maximum so far"
assert max_val in arr[0:i], "max_val is from processed elements"
if arr[i] > max_val:
max_val = arr[i]
assert max_val == max(arr), "max_val is maximum of entire array" # Post-condition
return max_valpublic int findMaximum(int[] arr) {
assert arr.length > 0 : "Array must not be empty";
int maxVal = arr[0];
for (int i = 1; i < arr.length; i++) {
assert i > 0 && i < arr.length : "Index in valid range";
assert maxVal == max(arr, 0, i) : "maxVal is maximum so far";
if (arr[i] > maxVal) {
maxVal = arr[i];
}
}
assert maxVal == max(arr, 0, arr.length) : "maxVal is maximum";
return maxVal;
}int find_maximum(int arr[], int n) {
assert(n > 0); // Pre-condition
int max_val = arr[0];
for (int i = 1; i < n; i++) {
assert(i >= 1 && i < n); // Bounds
assert(max_val >= arr[0]); // max_val is at least first element
// Note: Can't easily express "max of subarray" in C without helper
if (arr[i] > max_val) {
max_val = arr[i];
}
}
return max_val;
}Check that inferred invariants are correct:
Invariant must be true before the loop starts:
# Loop: total = 0; for i in range(n): total += arr[i]
# Invariant: total == sum(arr[0:i])
# Check: Before loop, i=0, total=0, sum(arr[0:0])=0 ✓Invariant remains true after each iteration:
# Assume invariant true at start of iteration i
# Show it's true at start of iteration i+1
# Before: total == sum(arr[0:i])
# Execute: total += arr[i]
# After: total == sum(arr[0:i]) + arr[i] == sum(arr[0:i+1]) ✓Invariant + termination condition proves post-condition:
# After loop: i == n (termination) and total == sum(arr[0:i]) (invariant)
# Therefore: total == sum(arr[0:n]) == sum(arr) ✓Generate invariants for each nesting level:
# Matrix sum
total = 0
for i in range(rows):
assert total == sum(matrix[0:i, :]) # Outer invariant
for j in range(cols):
assert total == sum(matrix[0:i, :]) + sum(matrix[i, 0:j]) # Inner
total += matrix[i][j]Track relationships between variables:
# Two pointers
left, right = 0, len(arr) - 1
while left < right:
assert 0 <= left <= right < len(arr)
assert left + right == len(arr) - 1 # Relationship invariant
# (only true if they move symmetrically)
left += 1
right -= 1Handle break/return statements:
# Search with early exit
found = False
for i in range(len(arr)):
assert not found or arr[i-1] == target # If found, previous element was it
if arr[i] == target:
found = True
break
assert not found or target in arr[0:i+1] # Post-conditionTrack effects on external state:
# Modifying a list
for i in range(len(arr)):
assert len(arr) remains constant # Size doesn't change
assert arr[0:i] are processed correctly
arr[i] = transform(arr[i])User request:
"Infer invariants for this sum loop"
def array_sum(arr):
total = 0
for i in range(len(arr)):
total += arr[i]
return totalResponse:
def array_sum(arr):
assert len(arr) >= 0 # Pre-condition: valid array
total = 0
for i in range(len(arr)):
# Loop invariants
assert 0 <= i < len(arr), "Index in valid range"
assert total == sum(arr[0:i]), "total is sum of elements so far"
total += arr[i]
assert total == sum(arr), "total is sum of entire array" # Post-condition
return totalExplanation:
0 <= i < len(arr)total == sum(arr[0:i])i increases each iterationUser request:
"What are the invariants for binary search?"
def binary_search(arr, target):
left, right = 0, len(arr) - 1
while left <= right:
mid = (left + right) // 2
if arr[mid] == target:
return mid
elif arr[mid] < target:
left = mid + 1
else:
right = mid - 1
return -1Response:
def binary_search(arr, target):
assert is_sorted(arr), "Array must be sorted" # Pre-condition
left, right = 0, len(arr) - 1
while left <= right:
# Loop invariants
assert 0 <= left <= len(arr), "left in valid range"
assert -1 <= right < len(arr), "right in valid range"
assert left <= right + 1, "Valid search range"
# Critical invariant: if target exists, it's in [left, right]
assert (target not in arr or
any(arr[i] == target for i in range(left, right + 1))), \
"Target in current range if it exists"
mid = (left + right) // 2
if arr[mid] == target:
return mid
elif arr[mid] < target:
assert arr[mid] < target, "All elements left of mid < target"
left = mid + 1
else:
assert arr[mid] > target, "All elements right of mid > target"
right = mid - 1
assert left > right, "Search range exhausted"
assert target not in arr, "Target not found implies not in array"
return -1User request:
"Generate invariants for insertion sort"
def insertion_sort(arr):
for i in range(1, len(arr)):
key = arr[i]
j = i - 1
while j >= 0 and arr[j] > key:
arr[j + 1] = arr[j]
j -= 1
arr[j + 1] = keyResponse:
def insertion_sort(arr):
for i in range(1, len(arr)):
# Outer loop invariant
assert is_sorted(arr[0:i]), "arr[0:i] is sorted"
assert set(arr) == set(original_arr), "Elements unchanged (only reordered)"
key = arr[i]
j = i - 1
while j >= 0 and arr[j] > key:
# Inner loop invariants
assert -1 <= j < i, "j in valid range"
assert arr[j] > key, "Current element larger than key"
assert is_sorted(arr[0:j]), "Left part still sorted"
assert arr[j+2:i+1] are shifted right and sorted
arr[j + 1] = arr[j]
j -= 1
arr[j + 1] = key
assert is_sorted(arr[0:i+1]), "arr[0:i+1] is now sorted"
assert is_sorted(arr), "Entire array is sorted"Start with obvious properties:
Look for accumulation patterns:
Identify preservation properties:
Think about the loop's purpose:
Verify your invariants:
Be specific:
i >= 00 <= i < len(arr)0 <= i < len(arr) and sum_val == sum(arr[0:i])Use helper predicates for clarity:
def is_sorted(arr):
return all(arr[i] <= arr[i+1] for i in range(len(arr)-1))
def is_partition(arr, pivot, left, right):
return (all(arr[i] <= pivot for i in range(left)) and
all(arr[i] >= pivot for i in range(right, len(arr))))For comprehensive invariant patterns across different loop types and languages, see invariant-patterns.md.
© 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
SKILL.md and 1 other file (references) in skills/invariant-inference of ArabelaTso/Skills-4-SE.
Open the folder on GitHubat commit 4f38503
Invariant Inference 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Invariant Inference this skillArabelaTso/Skills-4-SE | 253 | — | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| Fory Releaseapache/fory | 4.6k | — | ~2.9k | Automated safety check: Pass | Apache-2.0 | |
| CodeQL Security Scantrailofbits/skills | 7.4k | — | ~4.6k | Automated safety check: Notes | CC-BY-SA-4.0 | |
| Fory Version Bumpapache/fory | 4.6k | — | ~1.1k | Automated safety check: Pass | Apache-2.0 | |
| Fory Performance Optimizationapache/fory | 4.6k | — | ~2.2k | Automated safety check: Pass | Apache-2.0 | |
| MCP Debuggerdebugmcp/mcp-debugger | 172 | — | ~3.8k | Automated safety check: Pass | MIT |
apache/fory
Prepare an Apache Fory release candidate from a clean release branch, including the version bump, RC tag, JVM staging, ASF source artifacts, SVN upload, and vote email.
trailofbits/skills
Scans a codebase for vulnerabilities with CodeQL's data flow and taint tracking in run-all or important-only modes, including data extensions for project-specific sources and sinks.
apache/fory
Bump Apache Fory release or post-release development versions across Java, Kotlin, Scala, Python, Rust, Go, C++, C, Dart, JavaScript, Swift, integration tests, examples, and source docs.
apache/fory
Run profile-driven bottleneck optimization across Apache Fory implementations (Java, C++, Python/Cython, Go, Rust, Swift, C, JavaScript/TypeScript, Dart, Kotlin, Scala).
debugmcp/mcp-debugger
A skill your agent uses when investigating a bug, failing test, or unexpected runtime behavior and the mcp-debugger MCP server is available — drives real step-through debuggers (breakpoints, stack…
theodo-group/debug-that
Debug applications using the dbg CLI debugger. An agent skill from theodo-group/debug-that.
ArabelaTso/Skills-4-SE
Generate prioritized CVE watchlists and actionable security recommendations for repositories.
ArabelaTso/Skills-4-SE
Automatically migrate Python web applications between frameworks (Flask → FastAPI, Django → FastAPI).
ArabelaTso/Skills-4-SE
Generate test cases using metamorphic testing by applying transformations based on metamorphic properties.
ArabelaTso/Skills-4-SE
Instruments programs to capture execution traces specifically for reproducing reported bugs, enabling consistent replay and diagnosis of failures.
ArabelaTso/Skills-4-SE
Automatically migrate Spring MVC applications to Spring Boot.
ArabelaTso/Skills-4-SE
Instrument programs (Python, C/C++, Java) to capture snapshots of key program states at runtime, including variables, memory, and call stacks.
Automatically infer loop invariants for code verification and correctness proofs. Invariant Inference is an agent skill from ArabelaTso/Skills-4-SE. Automatically infer loop invariants for code verification and correctness proofs.
Invariant Inference fits situations like: analyzing loops to identify properties that hold throughout execution; generating assertions for verification; proving loop correctness; documenting loop behavior.
Run `npx skills add ArabelaTso/Skills-4-SE --skill invariant-inference -a claude-code`. Or copy the skill folder (skills/invariant-inference in ArabelaTso/Skills-4-SE) into .claude/skills/invariant-inference in your project. Claude Code loads it when a task matches its description.
Run `npx skills add ArabelaTso/Skills-4-SE --skill invariant-inference -a codex`. Or copy the skill folder (skills/invariant-inference in ArabelaTso/Skills-4-SE) into .agents/skills/invariant-inference in your project. Codex loads it when a task matches its description.
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 invariant-inference -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/invariant-inference, .gemini/skills/invariant-inference, .github/skills/invariant-inference and .opencode/skills/invariant-inference in your project.
SKILL.md names no scripts, command-line tools or credentials: Invariant Inference is instructions for the agent only. Our summary lists: Python 3.
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.
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.
Invariant Inference 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.
About 3.1k tokens (SKILL.md is roughly 12k 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 1.5k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Invariant Inference: Fory Release (apache/fory, 4.6k stars), CodeQL Security Scan (trailofbits/skills, 7.4k stars), Fory Version Bump (apache/fory, 4.6k stars) and Fory Performance Optimization (apache/fory, 4.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
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.