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adobe-rate-limits

Implement service-specific Adobe admission control, Retry-After handling, bounded retries, and spend-aware queues

Install / Use

npx skills add jeremylongshore/tons-of-skills-marketplace --skill adobe-rate-limits

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

80/100

Category

Marketing

Supported Platforms

Claude Code

Our assessment of adobe-rate-limits

adobe-rate-limits scores 80/100 on our quality scale, 338th of 426 Marketing skills we index.

Its SKILL.md is 3.9 KB long, well organised into 13 sections and no code examples: a solid amount of guidance for an agent.

With 2,785 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
26/30
Structure
13/20
Description
12/15
Adoption
15/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 6 days ago, so adobe-rate-limits is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

adobe-rate-limits compared with similar skills

All 4 of these similar skills score higher than adobe-rate-limits; compare them before choosing.

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Frequently asked questions

How do I install adobe-rate-limits?
Run npx skills add jeremylongshore/tons-of-skills-marketplace --skill adobe-rate-limits. The install tabs above show the steps for each supported agent.
Which AI agents does adobe-rate-limits work with?
It is written for Claude Code, as a SKILL.md file. Other agents that read the same format can often use it too.
Is adobe-rate-limits safe to use?
It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is adobe-rate-limits still maintained?
The repository was last updated 6 days ago, so adobe-rate-limits is actively maintained.

name: adobe-rate-limits description: >- Implement service-specific Adobe admission control, Retry-After handling, bounded retries, and spend-aware queues. Use when scaling workloads or handling 429 responses. Trigger with "Adobe rate limit", "Adobe 429", or "Adobe backoff". allowed-tools: Read,Glob,Grep,Write,Edit argument-hint: "<service> <workload> <recovery-objective>" version: 1.8.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io tags: [saas, adobe, throttling] model: inherit effort: high compatibility: "Designed for Claude Code; live Adobe actions require network access, appropriate entitlement and authentication, and explicit approval"

Adobe Throttling and Backpressure

Overview

Implement service-specific Adobe admission control, Retry-After handling, bounded retries, and spend-aware queues. This workflow produces a reviewable artifact and evidence before any live side effect.

Prerequisites

  • Current first-party Adobe documentation for every selected service, API version, auth flow, limit, and lifecycle.
  • Named product, identity, security, data, budget, release, and operations owners appropriate to the scope.
  • Synthetic or approved non-production fixtures with secret and content canaries.

Current Contract

Adobe limits differ by service, operation, entitlement, and contract. Recheck first-party limits at execution. A valid Retry-After response is authoritative; absence requires conservative capped exponential backoff with jitter, not a remembered universal rate. Recheck the dated evidence map before relying on mutable product behavior.

Authentication

Partition control by approved credential, organization, service, operation, and tenant without exposing tokens or multiplying credentials to evade policy.

Instructions

  1. Inventory traffic, operations, concurrency, queues, retry chains, job durations, transactions, and downstream acknowledgements.
  2. Read current service limits and capture observed 429 headers and error bodies from sanitized evidence.
  3. Set conservative token buckets, concurrency, queue size, and spend ceilings below verified constraints.
  4. Retry only classified transient, idempotent work; honor valid server delay and cap attempts plus elapsed time.
  5. Quarantine ambiguous writes/jobs and propagate backpressure rather than recursively retrying.
  6. Load-test with synthetic data, publish the safe envelope, and assign an emergency reduction switch.

Tool Discipline

Use Read, Glob, and Grep to inspect current documentation, configuration, code, fixtures, and evidence. Use Write and Edit only for approved repository artifacts. Skill invocation alone does not authorize network access, credentials, Adobe content, consent, uploads, generation, spend, deployment, registration changes, replay, cancellation, or deletion.

Approval Boundaries

Workload, budget, and sandbox owners approve load tests and capacity changes. Do not add organizations, users, keys, or projects to circumvent limits.

Error Handling

  • Fixed pack-wide RPM tables are forbidden.
  • Do not retry 401, 403, content-policy, validation, or terminal job failures blindly.
  • Stop before the retry budget becomes a spend amplifier.

Output

Return cited current constraints, observed headers, control settings, retry matrix, queue policy, load curve, spend guard, and rollback. Mark assumptions, observed environment behavior, owners, evidence dates, and unresolved gaps explicitly.

Examples

  • Recover from a 429 with Retry-After.
  • Demonstrate queue shedding when the elapsed retry budget expires.

Validation

Exercise and record expected and observed results for:

  • burst
  • missing delay
  • valid delay
  • ambiguous job
  • queue saturation
  • vendor outage

Resources

  • Current first-party evidence map — recheck dated Adobe sources before execution.
  • Treat observed tenant or product behavior as environment-specific evidence, never a universal Adobe guarantee.

Related Skills

View on GitHub
GitHub Stars2.8k
CategoryMarketing
Updated6d ago
Forks404

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions