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 skills add markmhendrickson/neotoma --skill remember-codebasegit clone --depth 1 https://github.com/markmhendrickson/neotomaWrote 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/skills/markmhendrickson/neotoma/remember-codebase)<a href="https://agentmods.dev/skills/markmhendrickson/neotoma/remember-codebase"><img src="https://agentmods.dev/badge/skills/markmhendrickson/neotoma/remember-codebase/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/skills/markmhendrickson/neotoma/remember-codebase"><img src="https://agentmods.dev/badge/skills/markmhendrickson/neotoma/remember-codebase.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00031 | $0.00739 |
| Opus 5 | $0.00015 | $0.00369 |
| Sonnet 5 | $0.00006 | $0.00148 |
| Haiku 4.5 | $0.00003 | $0.00074 |
Grade A, and why
remember-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 7d 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Remember Codebase
Build a persistent inventory of your development context — repositories, architecture decisions, key dependencies, and team knowledge — in Neotoma memory.
When to use
When a developer wants their agent to persistently understand their codebase context across sessions, without re-prompting project structure, conventions, or architectural decisions each time.
Prerequisites
Run the ensure-neotoma skill first if Neotoma is not yet installed or configured in your current harness.
Workflow
Phase 0: Verify Neotoma
Confirm Neotoma MCP is connected (call get_session_identity).
Phase 1: Inventory the current repo
- Read project metadata:
package.json/pyproject.toml/Cargo.toml(name, version, dependencies)README.md(project description, purpose)- Git remote URL and branch structure
- Scan for architectural signals:
- Directory structure (src/, lib/, test/, docs/)
- Configuration files (.env.example, docker-compose.yml, CI configs)
- Framework and language markers
- Present the inventory: project name, language, framework, key directories, dependency count.
- Ask the user to confirm and add any context the scan missed.
Phase 2: Extract entities
- Repository: create a
repositoryentity with name, remote URL, language, framework, description. - Architectural decisions: if the repo has ADR files (docs/adr/) or architecture docs, extract each as a
decisionentity. - Dependencies: create entities for key dependencies that the user wants to track (not all — ask which matter).
- Team context: if the user provides team member info, store as
contactentities linked to the repo. - Conventions: if the repo has coding conventions docs, extract key rules as
noteentities.
Phase 3: Store with provenance
Store the repository entity and related entities with provenance:
- Set
source_filefor file-derived entities (README, package.json, ADR files). - Use the combined store path for any files worth preserving as source.
- Link all entities to the repository entity via REFERS_TO.
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.
- 7d ago First seen · 79 lines · 31 tokens per session scan A 2d2d9451a329
remember-codebase is a skill published in the GitHub repository markmhendrickson/neotoma (32 stars, last pushed yesterday), licensed MIT. It adds 31 tokens to every session and 739 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
remember
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akf
Trust metadata for files, memories, and skills — check before you trust, stamp what you verify. A stamp costs 15 tokens; re-verifying costs 15,000.
akf
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aoa-memo
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soul-archive
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mk:wiki
Capture, gate, query, and render long-term project knowledge through the gated mewkit wiki subsystem. Use to create a wiki, propose/approve candidates (scanner-gated), hand off a skill's terminal artifact as a scanned candidate, recall context, search the FTS index, or list pages. Agents may only PROPOSE candidates…