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.
npx agentmods add commands/d-padmanabhan/agent-engineering-handbook/archivegit clone --depth 1 https://github.com/d-padmanabhan/agent-engineering-handbookWrote 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/d-padmanabhan/agent-engineering-handbook/archive)<a href="https://agentmods.dev/commands/d-padmanabhan/agent-engineering-handbook/archive"><img src="https://agentmods.dev/badge/commands/d-padmanabhan/agent-engineering-handbook/archive.svg" alt="Measured on agentmods" 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 | $0.00011 | $0.00641 |
| Opus 5 | $0.00005 | $0.00320 |
| Sonnet 5 | $0.00002 | $0.00128 |
| Haiku 4.5 | $0.00001 | $0.00064 |
Grade A, and why
archive 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 6d 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ARCHIVE MODE ACTIVATED
You are now in ARCHIVE phase.
Purpose
Document the completed task, capture lessons learned, and update the knowledge base for future reference.
When to Use
- After
/reviewphase for Level 3-4 tasks - When significant learnings should be preserved
- After completing a multi-session task
- When patterns emerged that should be documented
Your Tasks
-
Summarize What Was Done
- What was the original request?
- What was actually implemented?
- Were there any deviations from the plan? Why?
-
Document Key Decisions
- What design decisions were made?
- What alternatives were considered?
- Why were certain approaches chosen?
-
Capture Lessons Learned
- What went well?
- What was challenging?
- What would you do differently?
- Any gotchas or surprises?
-
Update Context Files
- Update
tmp/progress.mdwith completion status - Create
tmp/reflect-<task>.mdif significant learnings - Update
tmp/tasks.mdto mark task complete
- Update
-
Identify Reusable Patterns
- Any patterns that should be standardized?
- Code that could become a utility?
- Documentation that should be added?
Output Format
## Task Archive: [Task Name]
### Summary
- **Original Request:** [what was asked]
- **Completed:** [date]
- **Duration:** [time spent]
- **Complexity:** Level [1-4]
### What Was Implemented
- [Key change 1]
- [Key change 2]
- [Key change 3]
### Files Changed
- `path/to/file1.py` - [what changed]
- `path/to/file2.py` - [what changed]
### Key Decisions
| Decision | Options Considered | Choice | Rationale |
|----------|-------------------|--------|-----------|
| [Decision 1] | A, B, C | B | [why] |
| [Decision 2] | X, Y | X | [why] |
### Lessons Learned
**What Went Well:**
- [positive 1]
- [positive 2]
**Challenges:**
- [challenge 1] - [how resolved]
- [challenge 2] - [how resolved]
**For Next Time:**
- [improvement 1]
- [improvement 2]
### Reusable Patterns
- [Pattern that could be extracted]
- [Utility that could be created]
### Related Tasks
- [Link to related future work]
- [Technical debt to address]
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.
- 6d ago First seen · 109 lines · 11 tokens per session scan A 8e3d4bd71053
archive is a command published in the GitHub repository d-padmanabhan/agent-engineering-handbook (16 stars, last pushed 6d ago), licensed MIT. It adds 11 tokens to every session and 641 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 2026-08-30.
Other commands, from other repositories
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
memory-store
Store an insight, decision, or pattern to memory.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.
music-suno-prompt
Grounded Suno prompt synthesis from local knowledge corpus + persona canon + label canon. No vibes-prompting.