capture-learning

A command for recording the full story of solving a problem, from the first assumption through troubleshooting to the final lesson.

In plain words
What is it for?
Use it to document problems, assumptions, findings, troubleshooting steps, solutions, and takeaways for later documentation or process updates.
Why use it?
It prevents useful reasoning and discoveries from being lost after a work session.

Command

Install

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.

agentmods
npx agentmods add commands/danielmiessler/paiplugin/capture-learning
Clone the repo
git clone --depth 1 https://github.com/danielmiessler/PAIPlugin
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 660 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00000 $0.00660
Opus 5 $0.00000 $0.00330
Sonnet 5 $0.00000 $0.00132
Haiku 4.5 $0.00000 $0.00066

Measured 2d ago against content hash 7e029c9a3b36, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

capture-learning 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 2d 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.

commands/capture-learning.md · 101 lines

How it starts

The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.

capture-learning Command

Capture comprehensive problem-solving narratives from our work sessions, documenting not just the solution but the entire journey of discovery.

Purpose

This command captures the full narrative of problem-solving: what we thought was true, what we discovered was actually true, the troubleshooting journey, and the lessons learned. This narrative format makes it easy to harvest insights for updating documentation, commands, or pipelines.

Usage

# Interactive mode - guides you through the narrative
bun ${PAI_DIR}/commands/capture-learning.ts

# Direct mode with full narrative (all 6 arguments)
bun ${PAI_DIR}/commands/capture-learning.ts "problem" "initial assumption" "actual reality" "troubleshooting steps" "solution" "key takeaway"

Trigger Phrases

When you say any of these, I'll capture our learning:

  • "Great job, log this"
  • "Nice work, make a record"
  • "Document this"
  • "Capture this learning"
  • "Save this for later"
  • "That worked!"

The Narrative Structure

The command captures a complete story with these elements:

  1. The Problem - What issue did we encounter?
  2. Initial Assumption - "We thought this was true..."
  3. Actual Reality - "What we realized was actually true..."
  4. Troubleshooting Journey - Steps we took to discover the truth
  5. The Solution - What finally worked
  6. The Takeaway - "So now we know..." or "So now we do it this way..."

Why This Format?

This narrative approach helps us:

  • Remember the journey, not just the destination
  • Understand WHY the solution works
  • Recognize similar patterns in future problems
  • Update our mental models and documentation
  • Share knowledge effectively with others

Output

Files are saved to: ${PAI_DIR}/context/learnings/

Named with format: YYYY-MM-DD-problem-description.md

Each file contains:

  • Full narrative story
  • Before/after comparison
  • Technical details and commands used
  • Actionable takeaways for future reference

Example Output

Read the full file on GitHub · 101 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. 2d ago First seen · 101 lines · 0 tokens per session scan A 7e029c9a3b36

Subscribe to this mod's changes

capture-learning is a command published in the GitHub repository danielmiessler/PAIPlugin (56 stars, last pushed 9mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 660 tokens. 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.