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 instructions/veronchenko/inkwell-memory/claude-mdgit clone --depth 1 https://github.com/veronchenko/inkwell-memoryWrote 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/instructions/veronchenko/inkwell-memory/claude-md)<a href="https://agentmods.dev/instructions/veronchenko/inkwell-memory/claude-md"><img src="https://agentmods.dev/badge/instructions/veronchenko/inkwell-memory/claude-md.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 | $0.02378 | $0.02378 |
| Opus 5 | $0.01189 | $0.01189 |
| Sonnet 5 | $0.00476 | $0.00476 |
| Haiku 4.5 | $0.00238 | $0.00238 |
Grade A, and why
inkwell-memory CLAUDE.md scanned grade A with 1 finding 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 5d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- `src/cli.py` — `inkwell` console script (installed via `pyproject.toml`'s `[project.scripts]`, `pip install -e .` in the Dockerfile): thin `urllib`-based HTTP client to `admin_api` for `add-team`/`list-teams`/`revoke-t How it starts
The opening of the file, as written. The whole thing — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md — inkwell_memory
MCP server providing persistent memory for AI agents. Markdown files are the source of truth; a hybrid SQLite FTS5 + semantic embedding index (fused via Reciprocal Rank Fusion) provides search, plus graph-style relations (kb://uuid#type links).
Layout
src/server.py— MCP tool definitions (remember,recall,search,list,tags,forget,rebuild,doctor)src/schema.json+src/schema.py— the entry taxonomy as data: per-typerequired/body/membership/usage_boost/digest_on_recall. Loader resolves<data-path>/schema.json→ packaged default, full replacement, never a merge; builds theentry_typeenum injected intoserver.py's module globals before tool registration (PEP 563 annotations resolve there), so an undeclared type is rejected client-side. Read once at startup → editing it requires a restart.src/doctor.py— schema-driven integrity pass (run_doctor), shared by thedoctortool,rebuild's warnings, andremember's conformance warnings viacheck_entry. Reads the Markdown files, not the index.src/database.py— entry storage: Markdown + YAML frontmatter CRUD, UUID assignmentsrc/search_backend.py—SQLiteBackend(index,unindex,search,rebuild,get_relations,get_all_relations) +kb://relation extraction.searchfuses BM25 (FTS5) with cosine similarity over local Model2Vec embeddings (_bm25_search+_vector_search+_rrf_fuse); embeddings are stored as a BLOB column onentriesand computed lazily/in batch by the same code path that writes the row.src/dashboard/— web dashboard: FastAPI app (app.py,create_app(kb)) exposing REST CRUD +/api/graphover the sameKnowledgeBase/SQLiteBackendthe MCP tools use (no protocol duplication), a single staticindex.html(vanilla JS force-directed canvas graph, no build step/CDN) served at/, and__main__.pyas its ownuvicornentry point (INKWELL_DASHBOARD_HOST/INKWELL_DASHBOARD_PORT, default port 8193). Runs as a second process in the same container viadocker-entrypoint.sh, alongsideserver.py's MCP process. Single-KnowledgeBaseonly — not yet multi-tenant-aware (see below).src/team_admin.py—TeamAdminStore: SQLite registry (<data_path>/admin.db) mapping a team name to its data folder (<data_path>/teams/<name>/) and a SHA-256-hashed API key. Only touched in multi-tenant mode.src/admin_api/— loopback-only FastAPI app (app.py,create_app(store, api_key)) foradd-team/list-teams/revoke-team, guarded byINKWELL_ADMIN_API_KEY;__main__.pyis its ownuvicornentry point (INKWELL_ADMIN_API_HOST/INKWELL_ADMIN_API_PORT, default port 8194, not proxied/exposed). Third process in the container, started only whenINKWELL_MULTI_TENANT=1.src/cli.py—inkwellconsole script (installed viapyproject.toml's[project.scripts],pip install -e .in the Dockerfile): thinurllib-based HTTP client toadmin_apiforadd-team/list-teams/revoke-team, plus abackupsubcommand (on-demand tar.gz ofteams//admin.dbto a given--dest, no rotation/schedule built in). Run viadocker exec <container> inkwell <command>; readsINKWELL_ADMIN_API_URL/INKWELL_ADMIN_API_KEYfrom the same container environment the admin API itself uses.tests/— mirrorssrc/(test_server.py,test_database.py,test_backends.py,test_dashboard.py)
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.
- 5d ago First seen · 46 lines · 2,378 tokens per session scan A 2a5225049b1b
inkwell-memory CLAUDE.md is an instructions file published in the GitHub repository veronchenko/inkwell-memory (2 stars, last pushed 22d ago), licensed MIT. It adds 2,378 tokens to every session, about $0.0119 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other instructions, from other repositories
obsidian-mcp-server AGENTS.md
AGENTS.md instructions for cyanheads/obsidian-mcp-server, covering agent protocol, what's next?, core rules, patterns and tool — obsidianlisttags.
second-brain AGENTS.md
AGENTS.md instructions for LindaHaviv/second-brain, covering instructions for ai coding agents, what to read first, the rules (non-negotiable), common tasks, the sanctioned way and enforcement, not just instructions.
kb AGENTS.md
AGENTS.md instructions for hraness/kb, a project described as: A knowledge base for coding agents, built from Markdown, backlinks, semantic search, and Git context.
context CLAUDE.md
Claude Code instructions for Supa-Media/context, covering agent instructions — context, non-negotiables, architecture, the gateway (apps/mcp) and vocabulary.
grounded-knowledge-engine copilot-instructions.md
Copilot instructions for dimosgit/grounded-knowledge-engine: Follow AGENTS.md as the canonical repository contract.
context-keeper CLAUDE.md
Claude Code instructions for jarmstrong158/context-keeper, covering context keeper mcp server, project resolution, capture loop, when to record and record a decision when.