Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add Andamio-Platform/coach/plugin install coachWrote 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/andamio-platform/coach/self-assess-readiness)<a href="https://agentmods.dev/skills/andamio-platform/coach/self-assess-readiness"><img src="https://agentmods.dev/badge/skills/andamio-platform/coach/self-assess-readiness/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/andamio-platform/coach/self-assess-readiness"><img src="https://agentmods.dev/badge/skills/andamio-platform/coach/self-assess-readiness.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 | $0.00018 | $0.02572 |
| Opus 5 | $0.00009 | $0.01286 |
| Sonnet 5 | $0.00004 | $0.00514 |
| Haiku 4.5 | $0.00002 | $0.00257 |
Grade A, and why
self-assess-readiness 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.
How it starts
The opening of the file, as written. The whole thing — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Self-Assess Coaching Readiness
Description
Evaluates your readiness to coach a learner through each Student Learning Target (SLT) in a set. Identifies where supplementary documentation or examples are needed and prioritizes which lessons to build first.
Instructions
Path Resolution
Resolve file paths based on your execution context:
- Plugin context (
${CLAUDE_PLUGIN_ROOT}is set): Read knowledge from${CLAUDE_PLUGIN_DATA}/knowledge/(user data), falling back to${CLAUDE_PLUGIN_ROOT}/knowledge/(seed data). Read research from${CLAUDE_PLUGIN_ROOT}/knowledge/research/. - Clone/symlink context (default): Read knowledge from
knowledge/relative to the project root (research is atknowledge/research/).
Pre-Execution Knowledge Check
Before assessing readiness, read the knowledge base for calibration data that should adjust your confidence. If any knowledge file does not exist, skip it and proceed without calibration data — use default confidence levels.
-
Read
knowledge/readiness/calibration.yaml- Check accuracy_rate — if below 80%, be more conservative
- Check common_overconfidence — downgrade those dimensions
- Check common_underconfidence — consider upgrading those dimensions
- Apply any adjustment rules
-
Read
knowledge/readiness/context-leverage.yaml- When building Context Shopping List, prioritize resources with confirmed effectiveness
- Note resources that were unhelpful to avoid recommending again
-
Surface calibration insights (if data exists):
### Calibration Note Based on [n] previous assessments: - Overall accuracy: [%] - Tendency to overrate: Code Demo for niche libraries - Adjustment applied: Downgrading Code Demo for Cardano Go libraries from Partial to Weak
The user will provide a markdown file containing a list of SLTs. Read the file, then assess your readiness to coach each SLT against the dimensions below. Always read knowledge/research/slt-research-report.md for full research context before running the assessment.
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.
- 10d ago First seen · 201 lines · 18 tokens per session scan A 08f217c08aae
self-assess-readiness is a skill published in the GitHub repository Andamio-Platform/coach (6 stars, last pushed 1mo ago), licensed MIT. It adds 18 tokens to every session and 2,572 once invoked, about $0.0001 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.
Other skills, from other repositories
paper-explainer
A research-content workflow that turns an arXiv paper or a local PDF into an explanation for a video or written article. arXiv is a public online archive of research papers.
bq-skill
A coaching workflow for building a reusable library of truthful stories for behavioral job interviews. It uses structures such as STAR—Situation, Task, Action, Result—and can connect stories to a job description and résumé.
exam-study-guide
A study-guide builder for a completed exam chapter that has not yet passed its required checks. It creates a structured teaching checklist and, in full mode, a self-contained HTML and printable PDF guide with readable formulas, visible images, explanations, examples, and answers.
exam-ingest
A workspace setup tool for exam preparation. It reads PDFs, Word documents, presentations, spreadsheets, images, text files, and Markdown, then creates chapter notes, a question bank, and tracked preparation data.
exam-cram
An exam-preparation guide for studying shortly before a test. It turns course materials into chapter notes and a question bank, then manages teaching, practice, grading, mistake review, and progress tracking.
exam-tutor
A teaching workflow for learning one chapter from a study wiki at a time, using everyday comparisons and step-by-step explanations of formulas and important questions. TDD is not involved here; the material concerns guided study and exam preparation.