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/flow_makingWrote 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/flow_making)<a href="https://agentmods.dev/skills/xaiht/tlamatini/flow_making"><img src="https://agentmods.dev/badge/skills/xaiht/tlamatini/flow_making/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/flow_making"><img src="https://agentmods.dev/badge/skills/xaiht/tlamatini/flow_making.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Excessive Agency · line 105 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00038 | $0.02294 |
| Opus 5.5 | $0.00015 | $0.00918 |
| Sonnet 5.5 | $0.00008 | $0.00459 |
| Haiku 4.5 | $0.00004 | $0.00229 |
Grade A, and why
flow-making 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 today.
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.
Flow-Making
Current release: v1.90.0. Dated records below retain their original evidence dates.
HEADLESS IS FORBIDDEN. VISIBLE FOREGROUND EXECUTION ONLY. Every command, automated test, diagnostic, script, build, agent and browser must run in a visible foreground window on Angela's real desktop. Use forked CMD/PowerShell windows,
execute_forked_window: true, andheadless: false. Monitor live; if visibility cannot be confirmed, do not run. Read the mandatory execution policy.
Related (2026-07-22): FlowCreator is now ALSO a plain wrapped chat-agent,
chat_agent_flowcreator— a Multi-Turn tool (no ACPX needed) that takesprompt=+flow_filename=and writes the.flwitself. This skill is the heavier, ACPX/invoke_skill-surface path that shells out toscripts/make_flow.py; the wrapped tool is the lighter one-call path. Both convertflow_result.json→.flwwith the SAMEresult_to_flw.pylogic — this skill'sscripts/result_to_flw.pyand the copy vendored atagent/agents/flowcreator/result_to_flw.py(so the pool subprocess can carry it) must be kept in sync. For a simple "make me a .flw from this sentence" ask, preferchat_agent_flowcreator; use this skill when you need the scripted pipeline.
Produce a canvas-loadable .flw from ${input.objective} by wrapping the
FlowCreator engine — which already encodes the full 86-agent catalog,
connection contracts, and design rules in agentic_skill.md. Do NOT hand-author
the .flw JSON yourself: you do not carry the agent catalog/config-key contracts
in context, so a hand-written flow hallucinates agent types and will not load.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- today Changed a17da38a5691
- 2d ago Changed ea69417f5f7c
- 3d ago Changed e8d194750ee2
- 4d ago Changed 4e5c94815c63
- 5d ago Changed · +3 lines e546aa2a8e3e
- 29d ago Changed · +2 lines fe2e21feb00b
- 1mo ago Changed · +13 lines e84fe61e428d
- 1mo ago Changed · +8 lines 007439ae68d3
- 1mo ago First seen · 139 lines · 38 tokens per session scan A 407c5f7e860a
flow-making is a skill published in the GitHub repository XAIHT/Tlamatini (15 stars, last pushed yesterday), licensed MIT. It adds 38 tokens to every session and 2,294 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.
Other skills, from other repositories
commit
Inspect git status, draft a commit message that matches the repo's style, and commit staged changes.
review
Review current code changes for bugs, regressions, test gaps, and project-guideline violations.
artifact-deploy
One-click deploy a user's pre-built app/artifact into their OWN AWS account and get a global public HTTPS link (Vercel-like), with a default TTL and promote-to-persistent. Use when the user says "deploy this", "ship this demo", "give me a public link", "share this externally", or "deploy to AWS".
explain-for
Explain a topic, code, error or design decision for one named audience (a 5-year-old, a manager, a designer, a grad student, a parent). Resolves who it is for, pins the ground truth first, then tunes vocabulary, analogy, tone and depth to that audience. Use when an audience is named.
papyrus-latex-suggestions
Propose inline replacement text (old -> new) as accept/reject suggestions, LaTeX 'suggesting mode' / track changes, rendered in the PDF. Use when the author wants concrete edit proposals to accept or reject. For remarks ABOUT the text rather than replacements, use papyrus-latex-comments.
pptagent
Create and revise editable PowerPoint presentations from a brief using HTML slide sources, rendering, and visual review. Use when the requested deliverable is a PPTX deck. The host authors the slides; text-only hosts can use an external visual reviewer.