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.
npx skills add rlacombe/switchback-running --skill adjustgit clone --depth 1 https://github.com/rlacombe/switchback-runningWrote 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.
[](https://agentmods.dev/skills/rlacombe/switchback-running/adjust)<a href="https://agentmods.dev/skills/rlacombe/switchback-running/adjust"><img src="https://agentmods.dev/badge/skills/rlacombe/switchback-running/adjust/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.
<a href="https://agentmods.dev/skills/rlacombe/switchback-running/adjust"><img src="https://agentmods.dev/badge/skills/rlacombe/switchback-running/adjust.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00013 | $0.00333 |
| Opus 5.5 | $0.00005 | $0.00133 |
| Sonnet 5.5 | $0.00003 | $0.00067 |
| Haiku 4.5 | $0.00001 | $0.00033 |
Grade A, and why
adjust 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 1mo 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.
What it actually says
Adjust Training
Trigger
Use for a proposed change to one or more upcoming workouts.
Minimum evidence
Read the profile and relevant memory. Retrieve the affected calendar, recent completed training, and current wellness. Add zones and weather when they affect execution. For a schedule-only move, inspect adjacent hard sessions and recovery.
Use get_events for the affected window, get_activities for recent training,
and get_wellness and get_fitness when recovery or load matters. Use
get_weather with profile coordinates when conditions affect execution. Fetch the
complete event with get_event before changing it.
Knowledge
Retrieve only chapters implicated by the evidence: injury prevention for pain; recovery for fatigue or illness; periodization for schedule changes; volume progression for added load.
Decision
Apply the contract's health triage. Pain does not automatically require medical referral: classify the evidence, ask only decision-changing questions, and choose the least restrictive safe option. Preserve the workout's purpose where possible. Show observations, hypotheses, decision, and reassessment trigger.
Action
Present exact before-and-after dates, duration, intensity, and rationale. An explicit request to apply a fully specified change authorizes the write; otherwise ask once. Use Intervals.icu for writes, then read back each changed event. With Strava alone, provide portable text. Record a durable health or scheduling lesson only when it should affect future decisions.
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.
- 1mo ago Changed · +7 lines · +2 tokens per session 74c87b07a147
- 1mo ago First seen · 31 lines · 11 tokens per session scan A 2444cd95f891
adjust is a skill published in the GitHub repository rlacombe/switchback-running (10 stars, last pushed 1mo ago), licensed MIT. It adds 13 tokens to every session and 333 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-30.
Other skills, from other repositories
platform-api
Queries the Genesys Cloud Platform API schema for endpoint details, permissions, parameters, and response formats. Use when user asks about Genesys Cloud API endpoints (e.g., "/api/v2/assistants/queues"), API documentation, API permissions, schemas, capabilities, or when implementing Genesys Cloud operations.
google-adk-python
Build production-ready AI agents in Python using Google Agent Development Kit (ADK). Use when creating agentic Python applications with LlmAgent, workflow agents (SequentialAgent, ParallelAgent, LoopAgent), multi-agent orchestration, tool development (function tools, MCP, OpenAPI), session and state management…
google-adk-typescript
Build production-ready AI agents in TypeScript using Google Agent Development Kit (ADK). Use when creating agentic TypeScript/JavaScript applications with LlmAgent, workflow agents (SequentialAgent, ParallelAgent, LoopAgent), custom agents (BaseAgent with runAsyncImpl), FunctionTool with Zod schemas, MCP and OpenAPI…
neo4j-aura-agent-skill
Manages Neo4j Aura Agents via the v2beta1 REST API — create, list, get, update, delete, and invoke Aura agents backed by an AuraDB instance. Use when configuring Aura Agent tools (CypherTemplate, SimilaritySearch, Text2Cypher), setting system prompts, deploying agents to REST or MCP endpoints, invoking agents with…
neo4j-document-import-skill
Ingests unstructured and semi-structured documents into Neo4j as a knowledge graph. Use when chunking PDFs, HTML, plain text, or Markdown; extracting entities and relationships from text with an LLM (SimpleKGPipeline, neo4j-graphrag); loading JSON via apoc.load.json; building Document→Chunk→Entity graph structures; or…
neo4j-driver-dotnet-skill
Neo4j .NET Driver v6 — IDriver lifecycle, DI registration (singleton), ExecutableQuery fluent API, ExecuteReadAsync/ExecuteWriteAsync managed transactions, IResultCursor (FetchAsync/ ToListAsync), record value access (.Get /As ), null safety, UNWIND batching, temporal types, await using, EagerResult, object mapping…