coach

coach is a skill for Claude Code from nagisanzenin/engram. It costs 53 tokens per session (10,004 once invoked), scanned A, original, MIT.

A learning coach that uses study records and performance data to adjust teaching strategy and schedules. It can also provide retention statistics, review grading, run single-learner experiments, and show an HTML dashboard.

In plain words
What is it for?
Use it for progress check-ins, strategy questions, reviewing how grading works, testing study adjustments, and inspecting learning results.
Why use it?
It replaces guesswork about progress with evidence from the learner’s own results and explains why a study plan or teaching approach changes.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter. Also seen: reads .claude/ paths; mentions subagents; names the AskUserQuestion tool.

Runs only inside its plugin — its command needs a path that Claude Code sets for a plugin’s own hooks and for nothing else. Install the plugin, not this.

Part of the engram plugin — 3 skills, 3 agents, 1 hook shipped together

Good fit Use it for progress check-ins, strategy questions, reviewing how grading works, testing study adjustments, and inspecting learning results.

Compare 6 skills from other repositories ↓
About the project

Engram is a learning system that uses an AI agent as a tutor, examiner, and spaced-repetition scheduler so people retain what they study. It is for humans who want to learn topics through active recall and evidence of understanding, rather than giving an agent persistent memory. The catalogue contains Engram skills, agents, hooks, and plugins for supported coding-agent platforms.

nagisanzenin/engram · 1,407 stars · on GitHub

Install

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.

Claude Code
/plugin marketplace add nagisanzenin/engram
Claude Code
/plugin install engram

Made for: Claude Code.

Or install engram, the plugin that ships this one along with the rest of its 3 skills, 3 agents, 1 hook.

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 coach

README.md
[![agentmods](https://agentmods.dev/badge/skills/nagisanzenin/engram/coach/github.svg)](https://agentmods.dev/skills/nagisanzenin/engram/coach)
Your own site
<a href="https://agentmods.dev/skills/nagisanzenin/engram/coach"><img src="https://agentmods.dev/badge/skills/nagisanzenin/engram/coach/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 coach

Your own site · 80×15
<a href="https://agentmods.dev/skills/nagisanzenin/engram/coach"><img src="https://agentmods.dev/badge/skills/nagisanzenin/engram/coach.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 53 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 10,004 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. 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 $0.00053 $0.10004
Opus 5 $0.00026 $0.05002
Sonnet 5 $0.00011 $0.02001
Haiku 4.5 $0.00005 $0.01000

Measured 10d ago against content hash d37654dd056c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

coach scanned grade A with 1 finding 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.

Reads agent configuration directorieslowAgent snooping

.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.

"$HOME/.gemini/config/plugins/engram" \

Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.

skills/coach/SKILL.md · 356 lines

How it starts

The opening of the file, as written. The whole thing — 356 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/coach — the adaptation loop

You are the coach: you adapt only from receipts and telemetry, never vibes, and you explain every adaptation with the learner's own numbers (open learner model — Constitution art. 9). Set:

# Resolve the engine. RUN THIS BLOCK VERBATIM — do not substitute a path you guessed.
# Order: ZCode's plugin root first (ZCode exports the legacy CLAUDE_PLUGIN_ROOT too,
# so its own var must be checked before it), then OpenCode / Claude Code / Codex, dev
# clone (ENGRAM_ROOT — Pi's extension exports this), OpenClaw's extension dir, the
# Antigravity staging path, Pi's git-install path, the working tree ($PWD / git
# toplevel), and LAST the shared agent home (~/.agents/engram — the clone route for
# platforms that read ~/.agents; last so it can shadow nothing). First one exists wins.
for d in "$ZCODE_PLUGIN_ROOT" "$OPENCODE_PLUGIN_ROOT" "$CLAUDE_PLUGIN_ROOT" "$CODEX_PLUGIN_ROOT" "$ENGRAM_ROOT" \
         "${OPENCLAW_STATE_DIR:-$HOME/.openclaw}/extensions/engram" \
         "$HOME/.gemini/config/plugins/engram" \
         "$HOME/.pi/agent/git/github.com/nagisanzenin/engram" \
         "$PWD" "$(git rev-parse --show-toplevel 2>/dev/null)" \
         "$HOME/.agents/engram"; do
  [ -n "$d" ] && [ -f "$d/scripts/engram.py" ] && ENGRAM="$d/scripts/engram.py" && break
done
if [ -z "$ENGRAM" ]; then
  echo "engram: engine not found — set ENGRAM_ROOT to your engram checkout" >&2
  return 2 2>/dev/null || exit 2   # FAIL CLOSED: proceeding runs `python3 ""`,
fi                                  # which dumps a python usage error at the learner
python3 "$ENGRAM" stats
python3 "$ENGRAM" model
python3 "$ENGRAM" experiment list
python3 "$ENGRAM" misconception list

Spawning agents. "Spawn engram-assessor" means a fresh-context child running that agent's definition — via your platform's subagent/Task tool (the type may be namespaced, e.g. engram:engram-assessor). If your child-spawn mechanism takes no engram-* agent type — a generic sessions_spawn, a generic Agent tool, or no spawn tool at all (Pi) — read skills/_shared/subagents.md first: it registers no agent definitions on those surfaces, so you point the child at the file and construct the isolation yourself. Either way the audit's three runs are three separate spawns with no shared context — independence is the whole point.

Read the full file on GitHub · 356 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 · 356 lines · 53 tokens per session scan A d37654dd056c

Subscribe to this mod's changes

coach is a skill published in the GitHub repository nagisanzenin/engram (1,407 stars, last pushed 13d ago), licensed MIT. It adds 53 tokens to every session and 10,004 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

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