loop-pipeline-router

loop-pipeline-router is a skill for Claude Code, Codex from AnthonyAlcaraz/agentic-graph-rag-skills. It costs 199 tokens per session (1,750 once invoked), scanned A, original, MIT.

A routing rule for validation workflows that decides whether a result should proceed, be refined, stop, or use a fallback strategy.

In plain words
What is it for?
Use it in automated pipelines that validate results and may need bounded self-correction.
Why use it?
It handles correctable and fundamental errors separately and uses a retry limit, preventing endless loops.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it in automated pipelines that validate results and may need bounded self-correction.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/anthonyalcaraz/agentic-graph-rag-skills/loop-pipeline-router
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add AnthonyAlcaraz/agentic-graph-rag-skills --skill loop-pipeline-router
Clone the repo
git clone --depth 1 https://github.com/AnthonyAlcaraz/agentic-graph-rag-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for loop-pipeline-router

README.md
[![agentmods](https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/loop-pipeline-router/github.svg)](https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/loop-pipeline-router)
Your own site
<a href="https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/loop-pipeline-router"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/loop-pipeline-router/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for loop-pipeline-router

Your own site · 80×15
<a href="https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/loop-pipeline-router"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/loop-pipeline-router.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 199 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,750 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00199 $0.01750
Opus 5 $0.00100 $0.00875
Sonnet 5 $0.00040 $0.00350
Haiku 4.5 $0.00020 $0.00175

Measured 11d ago against content hash da1c8e3ee553, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

loop-pipeline-router scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (cli.py, lib.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/reasoning-planning/loop-pipeline-router/SKILL.md · 139 lines

How it starts

The opening of the file, as written. The whole thing — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Loop Pipeline Router

Overview

Loop pipelines introduce feedback for self-correction when perfect first-attempt reasoning is unrealistic. The decision that makes a loop a loop is the conditional edge after the validate node. Naively this is "valid -> proceed, else -> retry" (Example 5-6), but production needs the nuance of Example 5-9: distinguish correctable errors (refine) from fundamental ones (stop), and handle the retries-exhausted case (fall back to an alternative strategy) so the loop neither terminates prematurely nor spins forever.

The unified decision table:

valid                            -> proceed
correctable + retries remaining  -> refine (retry_count += 1, loop back)
correctable + retries exhausted  -> fallback_strategy
fundamental                      -> terminate_with_partial

The finite retry budget (Example 5-6's retry_count < 3 / recursion_limit) is the explicit termination guarantee. An invalid result with no error diagnostic is treated as fundamental — you cannot safely refine what you cannot diagnose.

In the DevOps latency investigation (account 123456789012), document verification on a related claim returns "incomplete" — the operative report lacks anesthesia-time records. That is a correctable error: refine (request the specific missing documentation, re-verify), bounded to three request cycles before escalating. A schema-level contradiction in the plan ("synchronous AND event-driven for the same operation") is fundamental: terminate with partial results rather than burn the retry budget on an unfixable error.

When to Use

  • First-attempt success is unrealistic and a validator can flag correctable errors
  • Documentation-gap re-requests, plan refinement, transient-failure recovery
  • You need a guaranteed-terminating self-correction loop with an audit trace

Phrases: "loop pipeline", "refine and re-validate", "retry with feedback", "correctable vs fundamental error", "fallback planner", "recursion limit".

Read the full file on GitHub · 139 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 139 lines · 199 tokens per session scan A da1c8e3ee553

Subscribe to this mod's changes

loop-pipeline-router is a skill published in the GitHub repository AnthonyAlcaraz/agentic-graph-rag-skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 199 tokens to every session and 1,750 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

graphify

Use for any question about a codebase, its architecture, file relationships, or project content — especially when graphify-out/ exists, where the question should be treated as a graphify query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community…

Graphify-Labs/graphify · 76 tokens

lemmalog

Externalize working memory and logical state into the lemmalog Datalog engine (MCP). Use for ANY multi-step task where state should outlive one context window or span agents: long investigations, debugging sessions, audits, multi-agent searches, systematic explorations, planning with many interdependent constraints…

JordyZomer/lemmalog · 105 tokens

jurisd-research

Expert Australian/NZ legal research and AGLC4 citation using the jurisd MCP server. Use when finding cases or legislation (AustLII), looking up a provision offline, formatting or resolving citations, building a pinpoint, tracing who-cites-what, or producing an AGLC4 bibliography. Triggers on case law, legislation…

russellbrenner/jurisd · 133 tokens

repo_search

Search repository text through a deterministic first-class fak command.

anthony-chaudhary/fak · 14 tokens

container-manager-kg-ingestion

Snapshot a host's Docker/Podman/Swarm inventory into the epistemic-graph knowledge graph as typed OWL nodes via the container-manager-mcp MCP server — containers, images, volumes, networks, swarm services and nodes, with their :usesImage / :runsOn / :builtFrom links. Use when the agent must record live container state…

Knuckles-Team/container-manager-mcp · 116 tokens

cortex-design

Use this skill to generate well-branded interfaces and assets for Cortex, either for production or throwaway prototypes/mocks/etc. Contains essential design guidelines, colors, type, fonts, assets, and UI kit components for prototyping.

mocaOS/cortex-app · 50 tokens