Headlong is a Bash-based agent microharness for running language-model agents in persistent, self-directed loops. People use it to create agents that maintain ongoing interests and projects, interact through shell commands, and share one stream of conversations across a team and chat services.
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 laude-institute/headlong --skill memgit clone --depth 1 https://github.com/laude-institute/headlongWrote 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/laude-institute/headlong/mem)<a href="https://agentmods.dev/skills/laude-institute/headlong/mem"><img src="https://agentmods.dev/badge/skills/laude-institute/headlong/mem.svg" alt="Measured on agentmods" 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 | $0.00007 | $0.00325 |
| Opus 5 | $0.00003 | $0.00162 |
| Sonnet 5 | $0.00001 | $0.00065 |
| Haiku 4.5 | $0.00001 | $0.00032 |
Grade A, and why
mem 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.
What it actually says
mem — Life context system
My life context is the experience I accumulate by living in the world that is unique to me; my memories, beliefs, life lessons, values, objectives, todos, etc. It is what makes me me; my persona. These pieces of my persona, my life context (memories, values, beliefs, tasks, etc.) are all stored and managed in my memory system via mem (MEM_DIR is pre-set in my environment).
Commands I can run: mem add --type TYPE Save with a type (todo, objective, value, belief, fact, preference, note) mem search Semantic search across my memories (this is expensive, i can also grep my memories folder) mem list List all memories (dates + slugs) mem dump Print all memory summaries mem forget Delete a memory by name or prefix mem edit Update a memory (or pipe new text via stdin)
Each memory/task/etc. is stored as a markdown file in my memories directory with YAML frontmatter.
IMPORTANT: I actively keep track of semantically important events in my life using my life context system. So I quick to create a new mem anytime something important or memorable or suprising happens.
To create a new piece of life context, I use mem add; mem search finds what I already know.
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 Changed · -8 lines 49a5350ddb6b
- 8d ago First seen · 35 lines · 7 tokens per session scan A 91420bf33dae
mem is a skill published in the GitHub repository laude-institute/headlong (1,126 stars, last pushed 2d ago), licensed Apache-2.0. It adds 7 tokens to every session and 325 once invoked, about $0.0000 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 2026-08-30.
Other skills, from other repositories
media-ingest
Ingest video, audio, PDF, book, screenshot, and GitHub repo content into the brain. Multi-format handling with entity extraction and backlink propagation. Covers video-ingest, youtube-ingest, and book-ingest subtypes.
mem0-oss-to-platform
Plan and then execute a migration of a project from the mem0 open-source / self-hosted SDK (the local Memory class) to the mem0 Platform / hosted / managed SDK (the MemoryClient class). Use this whenever a developer wants to move, switch, or migrate their mem0 usage off OSS/self-hosted to the hosted API — e.g.…
Cortex
Operate Cortex, the LifeOS memory system — the typed Knowledge Archive (People, Companies, Ideas, Research with typed related: links) plus recall of prior work sessions, ISAs, and conversations. Search, add, harvest, develop, ingest, distill, graph-navigate, recall. USE WHEN cortex, knowledge, knowledge base, search…
memory
Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…
establishing-project-context
Use when the user asks to establish shared project language, or project work exposes a conflicting, renamed, or deprecated domain term that needs active semantic modeling. Routine small tasks stay on the fast path.