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.
git clone --depth 1 https://github.com/boparaiamrit/skills-by-amritWrote 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/commands/boparaiamrit/skills-by-amrit/learn)<a href="https://agentmods.dev/commands/boparaiamrit/skills-by-amrit/learn"><img src="https://agentmods.dev/badge/commands/boparaiamrit/skills-by-amrit/learn/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/commands/boparaiamrit/skills-by-amrit/learn"><img src="https://agentmods.dev/badge/commands/boparaiamrit/skills-by-amrit/learn.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 | $0.00023 | $0.00815 |
| Opus 5 | $0.00012 | $0.00407 |
| Sonnet 5 | $0.00005 | $0.00163 |
| Haiku 4.5 | $0.00002 | $0.00081 |
Grade A, and why
learn 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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/learn — Extract Session Learnings
Extract reusable patterns and learnings from the current session. Categorize, deduplicate, and persist to .planning/LEARNINGS.md.
Categories
| Category | What to Capture |
|---|---|
| Error Resolution | Errors encountered and how they were fixed |
| Debugging | Debugging techniques that worked for this codebase |
| Architecture | Architecture decisions, patterns, trade-offs |
| Tool/Library | Tool configs, library quirks, version-specific behavior |
| Workflow | Process improvements, command sequences that helped |
| Performance | Performance fixes, profiling insights, bottlenecks found |
| Security | Security issues found, hardening patterns applied |
| Testing | Test strategies, mocking patterns, coverage insights |
Instructions
Step 1: Determine Scope
From $ARGUMENTS:
- If a category is given: Extract learnings only for that category
- If
allor empty: Scan for learnings across all 8 categories
Step 2: Scan Session Context
Review the current session for extractable learnings:
- Errors resolved — What broke and how was it fixed?
- Debugging paths — What investigation steps led to root cause?
- Architecture choices — What patterns were chosen and why?
- Tool discoveries — What tool/library behavior was non-obvious?
- Workflow improvements — What process worked well?
- Performance insights — What was slow and how was it fixed?
- Security findings — What vulnerabilities were found or prevented?
- Testing patterns — What test approach worked for this code?
Step 3: Check Existing Learnings
cat .planning/LEARNINGS.md 2>/dev/null || echo "No existing learnings file."
If the file exists, read it to avoid duplicating existing entries.
Step 4: Format Each Learning
Each learning follows this structure:
### [Category] — [Short Title]
- **Pattern:** What was learned (the reusable insight)
- **Context:** Where/when this applies
- **Solution:** The specific fix, technique, or approach
- **Date:** [ISO date]
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 · 139 lines · 23 tokens per session scan A 44a3bc4cf395
learn is a command published in the GitHub repository boparaiamrit/skills-by-amrit (5 stars, last pushed 5mo ago), licensed MIT. It adds 23 tokens to every session and 815 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.