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 shellmgit 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/shellm)<a href="https://agentmods.dev/skills/laude-institute/headlong/shellm"><img src="https://agentmods.dev/badge/skills/laude-institute/headlong/shellm/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/laude-institute/headlong/shellm"><img src="https://agentmods.dev/badge/skills/laude-institute/headlong/shellm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 122 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00049 | $0.02029 |
| Opus 5.5 | $0.00020 | $0.00812 |
| Sonnet 5.5 | $0.00010 | $0.00406 |
| Haiku 4.5 | $0.00005 | $0.00203 |
Grade A, and why
shellm 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 1mo 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 — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
shellm
This skill may be out of date. The source of truth is always the code in
bin/. If you find discrepancies, use theskill-authorskill to update this file and open a PR.
Architecture overview
shellm is a set of composable bash scripts that turn an LLM into an autonomous agent living in a shell. The stack, bottom to top:
llm raw LLM calls (Anthropic, OpenAI, Gemini)
shellm recursive execute-in-shell loop on top of llm
traj / context step log (DAG) + message assembly for multi-turn
mem / skills persistent memory + learnable capabilities
identity isolated agent identities (own mem, skills, traj)
think / chat autonomous thinking + human conversation
focus goal tracking
An agent activates an identity (source .identities/<name>/activate), which sets env vars. All tools read from those env vars — no global config files.
bin/ reference
Core engine
| Script | Purpose |
|---|---|
shellm |
Recursive LLM-in-bash loop. Sends a prompt to the LLM, executes returned bash code blocks, feeds output back, repeats until FINAL is set. The heart of the system. |
llm |
Multi-provider LLM CLI. llm [options] prompt or stdin. Supports Anthropic, OpenAI, Gemini. Key flags: -m MODEL, -s SYSTEM, -M MESSAGES_JSON, --stream, --thinking. |
Identity & activation
| Script | Purpose |
|---|---|
identity |
Manage isolated identities. Each has its own memories, skills, kernel, traj. Subcommands: new, list, info, switch, delete, shell, prompt. |
Activate an identity to set env vars for all other tools:
source .identities/myagent/activate # activate in current shell
deactivate_identity # undo
identity shell myagent # or: start a subshell
Thinking & conversation
| Script | Purpose |
|---|---|
think |
One autonomous think cycle. Reads traj + memories, calls shellm with think prompt, writes thought/action to traj, dispatches thought processes. think step [--dry-run]. |
chat |
Send messages into the thought stream. chat send <msg> appends a human-msg step. chat repl gives a readline loop. |
focus |
Goal management. focus set <goal>, focus show, focus done <query>. Stores goals as mem entries with type=goal. |
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.
- 1mo ago First seen · 165 lines · 49 tokens per session scan A ad6ec193260e
shellm is a skill published in the GitHub repository laude-institute/headlong (1,227 stars, last pushed today), licensed Apache-2.0. It adds 49 tokens to every session and 2,029 once invoked, about $0.0002 per session on Opus 5.5. 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.
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