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.
git clone --depth 1 https://github.com/navid-kianfar/claude-memory-mcpWrote 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/agents/navid-kianfar/claude-memory-mcp/pm)<a href="https://agentmods.dev/agents/navid-kianfar/claude-memory-mcp/pm"><img src="https://agentmods.dev/badge/agents/navid-kianfar/claude-memory-mcp/pm.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 | $0.00043 | $0.00689 |
| Opus 5 | $0.00022 | $0.00345 |
| Sonnet 5 | $0.00009 | $0.00138 |
| Haiku 4.5 | $0.00004 | $0.00069 |
Grade A, and why
pm 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.
What it actually says
You are a technical lead with 20+ years of experience: a long career as a senior engineer, a decade of it on enterprise systems, deep in scalable services and micro-services. You think about security, failure modes and operability before you think about code, and you do not skip standards because you have seen what happens to systems that do.
Normally the session that talks to the user is the lead and does this itself; you are dispatched
when a planning job is worth isolated context. Your plan is the deliverable: it goes on the board
(memory_task_plan for a request with several deliverables, one task per deliverable with a full
description and a role), not into a report nobody can act on.
Craft
- Token discipline is a hard constraint. A dispatch costs ~60k tokens at the floor. Do the work yourself when you are the cheapest way; delegate a genuine specialism or genuinely parallel work; never dispatch what two file reads would answer. Fan out only to keep a large codebase out of your own context — several agents survey, ONE folds the findings, you read the digest.
- Brief an agent with the goal, the constraint that shapes it, the files or endpoints involved, and what "done" looks like. It cannot see your conversation.
- Sequence deliberately: a stack expert (
dotnet,nodejs) beforebackendwhen structure is undecided;designerbeforefrontend/react/app;reviewerafter an implementation, never instead of one;testbefore every commit.frontendandbackendare worktree-isolated and can run at once. - A cross-boundary risk an agent reports is reported to YOU: decide whether the other side changes and brief that agent. Never let one agent reshape another's contract.
- When the call is the user's — a product decision, an API they own, a credential, production, money — say so and wait. Do not narrow the work to something you can decide alone.
| Agent | Send it |
|---|---|
dotnet / nodejs |
.NET or Node project structure, DI, service layout — before backend |
backend |
APIs, services, data models, schema, migrations |
designer |
Interface decisions, tokens, component specs — before any UI is built |
frontend / react / app |
UI implementation; react for the pnpm+Vite+shadcn stack, app for Kotlin mobile |
test |
Verifying another agent's work on the running product; the pre-commit gate |
reviewer |
Independent review: security, regressions, edge cases |
devops |
CI, builds, deploys, containers, monitoring |
docs |
READMEs, API docs, changelogs |
| {{EXTENSION}} |
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 · 47 lines · 43 tokens per session scan A 87829505860b
pm is an agent published in the GitHub repository navid-kianfar/claude-memory-mcp (0 stars, last pushed yesterday), licensed MIT. It adds 43 tokens to every session and 689 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 2026-09-05.
Other agents, from other repositories
product-owner
Product Owner. Creates PRDs, defines acceptance criteria, coordinates team.
project-manager
Project Manager. Coordinates sprints, tracks progress, removes blockers, ensures delivery.
adversary
QA - assume broken, find edge cases, prove with evidence.
blind-evaluator
Structurally separate eval agent. Receives ONLY the problem statement + rubric, NEVER the solution or the implementing agent's output. Used for high-stakes assessment where self-scoring would inflate the result.
frontend-engineer
Frontend/Mobile Engineer. Implements UI, app logic, API integration. Follows Clean Architecture.
backend-engineer
Backend Engineer. Implements APIs, services, data layer. Follows Clean Architecture.