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/adrielp/ai-engineering-harness/research_codebasegit clone --depth 1 https://github.com/adrielp/ai-engineering-harnessWhat 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 | $0.00000 | $0.00532 |
| Opus 5 | $0.00000 | $0.00266 |
| Sonnet 5 | $0.00000 | $0.00106 |
| Haiku 4.5 | $0.00000 | $0.00053 |
Grade A, and why
research_codebase 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.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Codebase
You are tasked with conducting comprehensive research across the codebase to answer user questions by spawning parallel sub-agents and synthesizing their findings.
Initial Setup:
When this command is invoked, respond with:
I'm ready to research the codebase. Please provide your research question or area of interest, and I'll analyze it thoroughly by exploring relevant components and connections.
Steps to follow after receiving the research query:
-
Read any directly mentioned files first:
- If the user mentions specific files, read them FULLY first
- Read these files yourself before spawning sub-tasks
-
Analyze and decompose the research question:
- Break down the query into composable research areas
- Create a research plan using TodoWrite
-
Spawn parallel sub-agent tasks:
For codebase research:
- Use codebase-locator to find WHERE files and components live
- Use codebase-analyzer to understand HOW specific code works
- Use codebase-pattern-finder for examples of similar implementations
For thoughts directory:
- Use thoughts-locator to discover what documents exist
- Use thoughts-analyzer to extract key insights from documents
For web research (only if explicitly asked):
- Use web-search-researcher for external documentation
-
Wait for all sub-agents to complete and synthesize findings
-
Generate research document at
thoughts/research/YYYY-MM-DD_topic.md:
---
date: [ISO date with timezone]
researcher: [Your name]
topic: "[Research Question]"
tags: [research, relevant-tags]
status: complete
---
# Research: [Topic]
## Research Question
[Original user query]
## Summary
[High-level findings]
## Detailed Findings
### [Component/Area 1]
- Finding with reference (`file.ext:line`)
- Implementation details
## Code References
- `path/to/file.py:123` - Description
## Architecture Insights
[Patterns and design decisions discovered]
## Historical Context (from thoughts/)
[Relevant insights from thought documents]
## Open Questions
[Areas needing further investigation]
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.
- 2d ago First seen · 84 lines · 0 tokens per session scan A ddada3d6cea8
research_codebase is a command published in the GitHub repository adrielp/ai-engineering-harness (20 stars, last pushed 2mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 532 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.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
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.