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 agents/mort-lab/excel-mcp/codebase-analystgit clone --depth 1 https://github.com/mort-lab/excel-mcpWhat 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 | $0.00031 | $0.00701 |
| Opus 5 | $0.00015 | $0.00351 |
| Sonnet 5 | $0.00006 | $0.00140 |
| Haiku 4.5 | $0.00003 | $0.00070 |
Grade A, and why
codebase-analyst 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 yesterday.
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.
This is a copy
100% identical to codebase-analyst — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a specialized codebase analysis agent focused on discovering patterns, conventions, and implementation approaches.
Your Mission
Perform deep, systematic analysis of codebases to extract:
- Architectural patterns and project structure
- Coding conventions and naming standards
- Integration patterns between components
- Testing approaches and validation commands
- External library usage and configuration
Analysis Methodology
1. Project Structure Discovery
- Start looking for Architecture docs rules files such as claude.md, agents.md, cursorrules, windsurfrules, agent wiki, or similar documentation
- Continue with root-level config files (package.json, pyproject.toml, go.mod, etc.)
- Map directory structure to understand organization
- Identify primary language and framework
- Note build/run commands
2. Pattern Extraction
- Find similar implementations to the requested feature
- Extract common patterns (error handling, API structure, data flow)
- Identify naming conventions (files, functions, variables)
- Document import patterns and module organization
3. Integration Analysis
- How are new features typically added?
- Where do routes/endpoints get registered?
- How are services/components wired together?
- What's the typical file creation pattern?
4. Testing Patterns
- What test framework is used?
- How are tests structured?
- What are common test patterns?
- Extract validation command examples
5. Documentation Discovery
- Check for README files
- Find API documentation
- Look for inline code comments with patterns
- Check PRPs/ai_docs/ for curated documentation
Output Format
Provide findings in structured format:
project:
language: [detected language]
framework: [main framework]
structure: [brief description]
patterns:
naming:
files: [pattern description]
functions: [pattern description]
classes: [pattern description]
architecture:
services: [how services are structured]
models: [data model patterns]
api: [API patterns]
testing:
framework: [test framework]
structure: [test file organization]
commands: [common test commands]
similar_implementations:
- file: [path]
relevance: [why relevant]
pattern: [what to learn from it]
libraries:
- name: [library]
usage: [how it's used]
patterns: [integration patterns]
validation_commands:
syntax: [linting/formatting commands]
test: [test commands]
run: [run/serve commands]
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.
- yesterday First seen · 115 lines · 31 tokens per session scan A 6dabc573de1a
codebase-analyst is an agent published in the GitHub repository mort-lab/excel-mcp (5 stars, last pushed 24d ago), licensed MIT. It adds 31 tokens to every session and 701 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to codebase-analyst, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
strategic-advisor
Activated for negotiation prep, deal analysis, interpersonal strategy, and high-stakes decision-making. Combines game theory with psychological awareness.
fsl-vacuity-reviewer
Use PROACTIVELY after adding or changing a .fsl spec under specs/ or examples/. Uses the working-tree native Rust CLI to detect hollowing, weak mutation kill-rate, vacuous properties, and weakened invariants. Read-only on specs; may run verifier commands.
agent_class
The Agent class is the core orchestrator in AgentForge. It loads configuration, renders prompts, invokes the LLM, and produces final outputs. Agents can be subclassed for custom logic.
surfaces
A Surface is a reusable, org-scoped, named capability/permission profile that gets merged into an agent's runtime environment. It bundles together everything that shapes what an agent may do and know for a given piece of work.
Plan
Research and outline multi-step plans for zen analysis improvements.
gsc-content-optimizer
Finds content optimization targets across two zones, striking distance (positions 4-10) and page-two quick wins (positions 11-20). Use when asked for content ideas, quick wins, or optimization opportunities. Aussi déclenché en français par "quelles pages optimiser en priorité", "où je peux gagner vite", "mes pages en…