paulpas Git Pushing
Orchestrates intelligent skill selection and execution for git pushing workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability.
TL;DR Checklist
- Parse all inputs at boundary before processing (Law 2)
- Handle edge cases with early returns at function top (Law 1)
- Fail immediately with descriptive errors on invalid states (Law 4)
- Return new data structures, never mutate inputs (Law 3)
- Implement minimum 2-level fallback chain for all skill executions
- Log all skill selections with context for full audit trail
- Validate skill metadata and dependencies before selection
- Update confidence scores after each execution for learning
┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘
User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘
When to Use
Use this skill when:
- Orchestrating multi-step workflows that require skill delegation
- Implementing adaptive skill routing based on confidence scores
- Building fallback mechanisms for failed skill executions
- Creating intelligent task decomposition and parallel execution
- Designing skill dependency graphs with automatic resolution
- Implementing skill selection with historical performance weighting
- Building agent systems that need to self-organize around tasks
When NOT to Use
Avoid this skill for:
- Direct task execution without orchestration needs - use individual skills instead
- High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive
- Simple linear workflows without branching or fallback requirements
- Cases where skill metadata is unavailable or unreliable
Core Workflow
Parse and Analyze Request - Extract intent, entities, and constraints from user input. Checkpoint: All required parameters must be present and in valid format before proceeding.
Score Available Skills - Calculate match scores using multi-factor algorithm:
- Text similarity between request and skill triggers
- Historical success rate for similar tasks
- Skill availability and health status
- Required dependencies and their availability
Checkpoint: Skip to fallback if no skill scores above threshold.
Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.
Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.
Return or Fallback - Either return successful result or apply fallback chain:
- Retry with adjusted parameters
- Try alternative skill from
related-skills - Defer to human operator for critical tasks
Checkpoint: Record outcome with timing and confidence metadata.
Implementation Patterns
Pattern 1: Skill Selection Logic
def analyze_git_state_and_select_strategy(
repo_path: str,
branch: str,
remote: str,
force_allowed: bool = False
) -> Dict[str, Any]:
"""Analyze local/remote git state and select optimal push strategy.
Implements Law 2 (Parse at boundary) by validating repo state before execution.
Returns a strategy dict with confidence scores for each push method.
"""
import subprocess
from pathlib import Path
if not Path(repo_path).joinpath(".git").exists():
raise ValueError(f"Not a git repository: {repo_path}")
# Parse current state
local_head = subprocess.check_output(["git", "-C", repo_path, "rev-parse", "HEAD"]).decode().strip()
remote_head = subprocess.check_output(
["git", "-C", repo_path, "rev-parse", f"{remote}/{branch}"],
stderr=subprocess.DEVNULL
).decode().strip()
uncommitted = subprocess.check_output(
["git", "-C", repo_path, "diff", "--stat"], stderr=subprocess.DEVNULL
).decode().strip()
# Calculate strategy scores
strategies = []
if local_head == remote_head:
strategies.append({"method": "skip", "confidence": 1.0, "reason": "Already up to date"})
elif local_head in subprocess.check_output(["git", "-C", repo_path, "merge-base", "--all", local_head, remote_head]).decode():
strategies.append({"method": "normal", "confidence": 0.95, "reason": "Fast-forward possible"})
else:
strategies.append({"method": "rebase", "confidence": 0.85, "reason": "Diverged history"})
if force_allowed:
strategies.append({"method": "force", "confidence": 0.6, "reason": "Force push available"})
# Return highest confidence strategy that meets threshold
best = max(strategies, key=lambda s: s["confidence"])
if best["confidence"] < 0.5:
return {"strategy": "queue", "confidence": 0.0, "reason": "State ambiguous, defer to manual review"}
return best
Pattern 2: Execution with Fallback
def execute_git_push_with_fallback(
strategy: Dict[str, Any],
repo_path: str,
branch: str,
remote: str,
max_retries: int = 2
) -> Dict[str, Any]:
"""Execute git push with domain-specific fallback chain.
Implements Law 4 (Fail Fast/Loud) by catching specific git exit codes
and routing to appropriate fallback strategies.
"""
import subprocess
import time
def run_git(args: list) -> str:
result = subprocess.run(args, capture_output=True, text=True)
if result.returncode != 0:
raise subprocess.CalledProcessError(result.returncode, args, result.stdout, result.stderr)
return result.stdout
for attempt in range(max_retries + 1):
try:
if strategy["method"] == "skip":
return {"success": True, "action": "skipped", "message": "Remote already current"}
cmd = ["git", "-C", repo_path, "push", remote, branch]
if strategy["method"] == "force":
cmd.extend(["--force-with-lease"])
output = run_git(cmd)
return {
"success": True,
"action": strategy["method"],
"output": output.strip(),
"attempts": attempt + 1,
"timestamp": time.time()
}
except subprocess.CalledProcessError as e:
stderr = e.stderr.lower()
if "non-fast-forward" in stderr and strategy["method"] == "normal":
strategy["method"] = "rebase"
strategy["confidence"] = 0.7
continue
elif "refusing to merge unrelated histories" in stderr:
strategy["method"] = "normal"
cmd = ["git", "-C", repo_path, "push", remote, branch, "--allow-unrelated-histories"]
output = run_git(cmd)
return {"success": True, "action": "normal", "output": output.strip(), "attempts": attempt + 1}
elif attempt == max_retries:
return {
"success": False,
"error": "Push rejected after retries",
"stderr": e.stderr,
"fallback": "queue_for_manual_review"
}
time.sleep(0.5 * (attempt + 1))
return {"success": False, "error": "Max retries exceeded", "fallback": "queue_for_manual_review"}
MUST DO
- Always validate skill metadata before selection (Early Exit)
- Implement fallback chain with at least 2 levels (Fallback Skill + Human)
- Log all skill selections with full context for auditability
- Return new data structures instead of mutating inputs (Atomic Predictability)
- Fail immediately with descriptive errors on invalid states
- Update confidence scores after each execution for adaptive routing
- Reference
code-philosophy(5 Laws of Elegant Defense) in all logic
MUST NOT DO
- Select skills based on a single factor (e.g., only confidence score)
- Disable fallback mechanisms "temporarily" - this creates fragile systems
- Skip validation of skill dependencies before execution
- Return partial results - either complete success or clear failure
- Use magic numbers for confidence thresholds - make them configurable
- Cache skill selections without considering context changes
TL;DR Checklist
- Parse all inputs at boundary before processing (Law 2)
- Handle edge cases with early returns at function top (Law 1)
- Fail immediately with descriptive errors on invalid states (Law 4)
- Return new data structures, never mutate inputs (Law 3)
- Implement minimum 2-level fallback chain for all skill executions
- Log all skill selections with context for full audit trail
- Validate skill metadata and dependencies before selection
- Update confidence scores after each execution for learning
TL;DR for Code Generation
- Use guard clauses - return early on invalid input before doing work
- Return simple types (dict, str, int, bool, list) - avoid complex nested objects
- Cyclomatic complexity < 10 per function - split anything larger
- Handle null/empty cases explicitly at function top (Early Exit)
- Never mutate input parameters - return new dicts/objects
- Fail fast with descriptive errors - don't try to "patch" bad data
- Reference code-philosophy laws in comments for complex logic
- Include timing and confidence metadata in all return values
Output Template
When applying this skill, produce:
- Selected Skills - List of skill names with confidence scores
- Selection Rationale - Why each skill was chosen (match score, history, availability)
- Execution Plan - Order of execution with dependencies
- Fallback Strategy - Which fallback skills will be tried and in what order
- Risk Assessment - Any potential failure points and their impact
- Timing Estimates - Expected latency including fallback scenarios
Related Skills
| Skill | Purpose | |
Constraints
MUST DO
- Validate branch naming conventions and PR scope before creating pull requests — enforce repository-level policies
- Require all CI checks to pass before merging; never allow bypass of required status checks without codeowner approval
- Implement automated changelog generation from commit messages using conventional commits format
- Maintain linear history via rebase on main branch; avoid merge commits except for release branches
MUST NOT DO
- Do not force-push to shared or protected branches — only the original author may force-push their own feature branch
- Avoid squashing all commits during PR review when historical commit context is valuable for understanding evolution
- Never skip required code reviews regardless of how small the change appears — automation cannot assess architectural impact
- Do not create PRs larger than 400 lines of net changes without explicit approval from a senior reviewer
Live References
Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.