orchestrate-input-tracing

orchestrate-input-tracing is a skill for Claude Code, Codex from NITISH-R-G/hackerrank-orchestrate-skills. It costs 86 tokens per session (676 once invoked), scanned A, original, MIT.

A step-by-step method for tracing one input through an agent pipeline, from loading and context building to model calls, validation, retries, and fallback handling.

In plain words
What is it for?
Use it after building a pipeline or while debugging to verify what each stage receives, changes, and passes onward.
Why use it?
It reveals where the running system differs from the intended design and helps locate errors between processing stages.

Skill for Claude CodeCodex

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

Good fit Use it after building a pipeline or while debugging to verify what each stage receives, changes, and passes onward.

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Install with agentmods
npx agentmods add skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-input-tracing
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 NITISH-R-G/hackerrank-orchestrate-skills --skill orchestrate-input-tracing
Clone the repo
git clone --depth 1 https://github.com/NITISH-R-G/hackerrank-orchestrate-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 orchestrate-input-tracing

README.md
[![agentmods](https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-input-tracing/github.svg)](https://agentmods.dev/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-input-tracing)
Your own site
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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 orchestrate-input-tracing

Your own site · 80×15
<a href="https://agentmods.dev/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-input-tracing"><img src="https://agentmods.dev/badge/skills/nitish-r-g/hackerrank-orchestrate-skills/orchestrate-input-tracing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 86 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 676 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 $0.00086 $0.00676
Opus 5 $0.00043 $0.00338
Sonnet 5 $0.00017 $0.00135
Haiku 4.5 $0.00009 $0.00068

Measured 10d ago against content hash 36c17114d3af, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

orchestrate-input-tracing 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 10d ago.

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/orchestrate-input-tracing/SKILL.md · 33 lines

How it starts

The opening of the file, as written. The whole thing — 33 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Orchestrate: Input Tracing

Source: The Engineer's Notebook, "Getting better at HackerRank Orchestrate" (Shloka Shah) — the article's recommended self-check framework for verifying a layered agent architecture actually works as designed: pick one input and trace it through the entire system, checking where each stage occurs.

Why tracing beats reading your own architecture diagram

The article's recommended architecture separates concerns explicitly — input loading & normalization, context building, LLM invocation, response parsing & validation, schema enforcement, retry/error handling, fallback/escalation. It's easy to draw that separation and never actually confirm each stage exists as a distinct, checkable step in the running code. Tracing forces the confirmation: pick one real ticket/claim, and for each stage, answer "what did this stage receive, what did it do, what did it hand to the next stage" — concretely, not from memory of what you intended to build.

The practice

  1. Pick one representative input — not a trivial one, one with some real complexity (references multiple corpus documents, or has ambiguous phrasing).
  2. At input loading: confirm exactly what fields were extracted and normalized. Print or log the parsed representation.
  3. At context building: confirm exactly which corpus documents/images were retrieved for this input, and why — not just "context was built," but the actual retrieved content.
  4. At LLM invocation: confirm the actual prompt sent (not the template — the fully rendered prompt with this input's data substituted in).
  5. At response parsing: confirm what the raw model response looked like, and what your parser extracted from it.
  6. At validation: confirm which checks ran, and whether they passed or triggered a retry/fallback.
  7. At fallback/escalation (if triggered): confirm the exact condition that caused it.

If any of these steps is hard to isolate and inspect — if "context building" and "LLM invocation" are tangled into one function you can't separately observe — that's the architecture gap this trace just found, before an interviewer finds it for you.

Read the full file on GitHub · 33 lines

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. 10d ago First seen · 33 lines · 86 tokens per session scan A 36c17114d3af

Subscribe to this mod's changes

orchestrate-input-tracing is a skill published in the GitHub repository NITISH-R-G/hackerrank-orchestrate-skills (3 stars, last pushed 28d ago), licensed MIT. It adds 86 tokens to every session and 676 once invoked, about $0.0004 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.

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