Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/XAIHT/Tlamatininpx agentmods add skills/xaiht/tlamatini/tlamatini_flow_from_objectiveWrote 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/xaiht/tlamatini/tlamatini_flow_from_objective)<a href="https://agentmods.dev/skills/xaiht/tlamatini/tlamatini_flow_from_objective"><img src="https://agentmods.dev/badge/skills/xaiht/tlamatini/tlamatini_flow_from_objective/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/xaiht/tlamatini/tlamatini_flow_from_objective"><img src="https://agentmods.dev/badge/skills/xaiht/tlamatini/tlamatini_flow_from_objective.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 | $0.00033 | $0.00932 |
| Opus 5 | $0.00016 | $0.00466 |
| Sonnet 5 | $0.00007 | $0.00186 |
| Haiku 4.5 | $0.00003 | $0.00093 |
Grade A, and why
tlamatini-flow-from-objective scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Flow from objective
Produce a canvas-loadable .flw for the user's stated objective.
Superseded by the
flow-makingskill. Preferflow-making: it drives the FlowCreator engine (full 83-agent catalog + connection contracts) and emits a validated, schemaVersion-2.flw. This skill is kept as an alias/entry point — do NOT hand-author the.flwJSON, because you do not carry the agent catalog in context and a hand-written flow hallucinates agent types and will not load.
Procedure (delegate)
- Invoke the
flow-makingskill with the same inputs:invoke_skill('flow-making', { "objective": "${input.objective}", "out_path": "${input.out_path}" }). - Return its result verbatim:
{ flw_path, agent_count, connection_count }.
If you must run it directly
Use the shipped driver — it copies the FlowCreator template to an isolated dir,
runs it, and writes the .flw:
python Tlamatini/agent/skills_pkg/flow_making/scripts/make_flow.py \
--objective "${input.objective}" --out "${input.out_path}"
The last stdout line is agent_count=<N> connection_count=<M> flw_path=<path>.
Correct .flw shape (schemaVersion 2)
If you ever emit .flw JSON by hand, it MUST match the loader contract
(acp-file-io.js::loadDiagram / flow_spec.py) — NOT a {version, agents, connections:[{from,to,kind}]} shape (that is obsolete and will not load):
{
"schemaVersion": 2,
"nodes": [
{"id": "starter-1", "text": "Starter", "left": "50px", "top": "50px",
"agentPurpose": "", "configData": {"target_agents": ["monitor_log_1"]}}
],
"connections": [
{"sourceIndex": 0, "targetIndex": 1, "inputSlot": 0, "outputSlot": 0}
],
"artifacts": {}
}
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.
- 10d ago First seen · 92 lines · 33 tokens per session scan A c660c20d59a7
tlamatini-flow-from-objective is a skill published in the GitHub repository XAIHT/Tlamatini (16 stars, last pushed yesterday), licensed MIT. It adds 33 tokens to every session and 932 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 2026-08-30.
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