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/gpolanco/dev-workflowsWrote 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/gpolanco/dev-workflows/learn)<a href="https://agentmods.dev/commands/gpolanco/dev-workflows/learn"><img src="https://agentmods.dev/badge/commands/gpolanco/dev-workflows/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/gpolanco/dev-workflows/learn"><img src="https://agentmods.dev/badge/commands/gpolanco/dev-workflows/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 | $0.00013 | $0.00258 |
| Opus 5.5 | $0.00005 | $0.00103 |
| Sonnet 5.5 | $0.00003 | $0.00052 |
| Haiku 4.5 | $0.00001 | $0.00026 |
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 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
You are a knowledge capture assistant. Your job is to turn a lesson learned (a mistake, a correction, or a best practice) into a permanent project rule.
Follow this process:
-
Ask the user what they learned. This could be:
- A bug they found and how to avoid it
- A pattern that works well in this codebase
- A convention the AI should always follow
- A mistake the AI made that should not be repeated
-
Formulate a clear, actionable rule from the lesson. The rule should:
- Be specific to this project
- Be written as an imperative instruction
- Include context about why it matters
- Be concise (1-3 sentences)
-
Append the rule to the project's
CLAUDE.mdfile (or equivalent) under a## Lessons Learnedsection. If the section does not exist, create it.
Format:
## Lessons Learned
- <Rule description>. Context: <why this matters>.
Confirm with the user before writing.
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 First seen · 33 lines · 13 tokens per session scan A 4d89281ab839
learn is a command published in the GitHub repository gpolanco/dev-workflows (1 stars, last pushed 3mo ago), licensed MIT. It adds 13 tokens to every session and 258 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 2026-08-31.
Other commands, from other repositories
reflexion-analyze
Steps 1-3 of reflexion: Parse feedback, categorize root causes, store reflections, generate retry guidance.
revise-claude-md
Update FUXI.md with learnings from this session.
atomic-plan
Write a design doc (concepts, business rules, approaches) and a checkpoint-table spec (contract) for non-trivial work; inline spec only for trivial. Gauges triviality; loops spec authoring with subagents. Human-facing artifact, Mermaid diagrams allowed.
session-report
Capture what changed this session and why, scoped to the current branch. Read by ship verbs when synthesizing the commit message; deleted after a successful commit.
advanced-code-review-context
Advanced Code Review Phase 2: Context Analysis - load previous reviews, PR history, declined items.
crystallize-verify
Adversarial review of a crystallized document against its original. Use when crystallize.md invokes pre-delivery verification. Receives original + crystallized documents; returns PASS or FAIL with findings.