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 bartoszarendt/agenticloop --skill setup-agenticloopgit clone --depth 1 https://github.com/bartoszarendt/agenticloopWrote 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/bartoszarendt/agenticloop/setup-agenticloop)<a href="https://agentmods.dev/skills/bartoszarendt/agenticloop/setup-agenticloop"><img src="https://agentmods.dev/badge/skills/bartoszarendt/agenticloop/setup-agenticloop/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/bartoszarendt/agenticloop/setup-agenticloop"><img src="https://agentmods.dev/badge/skills/bartoszarendt/agenticloop/setup-agenticloop.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00098 | $0.03247 |
| Opus 5.5 | $0.00039 | $0.01299 |
| Sonnet 5.5 | $0.00020 | $0.00649 |
| Haiku 4.5 | $0.00010 | $0.00325 |
Grade A, and why
setup-agenticloop 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 — 349 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Setup Agentic Loop
This is an interactive setup and human-controlled profile-update skill. It is maintainer-run or human-run. Run it when:
- setting up Agentic Loop in a target project for the first time,
- source document names are non-standard or ambiguous,
.agenticloop/project.mdissetup_status: unconfirmed,.agenticloop/project.mdneeds confirmed typed document selections.- a confirmed project needs its one-time development-stage migration or a human wants to confirm a later development-stage transition.
Do not use this skill for routine runtime document lookup. At runtime, agents read
.agenticloop/project.md for overrides and fall back to conventional names. This skill
performs one-time discovery so runtime agents do not need to scan the repository.
What This Skill Does
- Scan bounded candidate paths once.
- Propose detected documents by role, detected grouping profile, inferred task ID style, backend choice, development stage, and implementation-lane maximum.
- Ask the human to confirm before writing.
- Write confirmed project-map values and setup confirmation state to
.agenticloop/project.md. - Preserve existing
.agenticloop/project.mdcontent where possible.
After this skill runs, future runtime agents use convention-first lookup plus the confirmed overrides recorded here.
Setup is complete only after the maintainer or human confirms:
- document role selections,
- task ID pattern and regex,
- grouping profile,
- backend choice.
- one development stage:
greenfield,expansion,stabilization, ormaintenance.
Candidate Files to Detect
Use the canonical document-role registry in agenticloop/config.json. Scan only
these bounded candidates once:
rules
AGENTS.mdCLAUDE.mdGEMINI.md
plan
IMPLEMENTATION_PLAN.mdPLAN.mdROADMAP.mddocs/roadmap.md
overview
README.mddocs/overview.md
process
agenticloop/AGENTIC_LOOP.md
spec
SPEC.mdSPECS.mdPRD.mdREQUIREMENTS.mddocs/spec.mddocs/specs/docs/prd/
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 Changed · +1 lines 2d4a5ebea3c6
- 1mo ago First seen · 348 lines · 98 tokens per session scan A ad19a357a4e3
setup-agenticloop is a skill published in the GitHub repository bartoszarendt/agenticloop (2 stars, last pushed 1mo ago), licensed MIT. It adds 98 tokens to every session and 3,247 once invoked, about $0.0004 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 2026-08-31.
Other skills, from other repositories
ppt-generation
Use this skill when the user requests to generate, create, or make presentations (PPT/PPTX). Creates visually rich slides by generating images for each slide and composing them into a PowerPoint file.
image-generation
Use this skill when the user requests to generate, create, imagine, or visualize images including characters, scenes, products, or any visual content. Supports structured prompts and reference images for guided generation.
newsletter-generation
Use this skill when the user requests to generate, create, write, or draft a newsletter, email digest, weekly roundup, industry briefing, or curated content summary. Supports topic-based research, content curation from multiple sources, and professional formatting for email or web distribution. Trigger on requests…
data-analysis
Use this skill when the user uploads Excel (.xlsx/.xls) or CSV files and wants to perform data analysis, generate statistics, create summaries, pivot tables, SQL queries, or any form of structured data exploration. Supports multi-sheet Excel workbooks, aggregation, filtering, joins, and exporting results to…
podcast-generation
Use this skill when the user requests to generate, create, or produce podcasts from text content. Converts written content into a two-host conversational podcast audio format with natural dialogue.
claude-to-deerflow
Interact with DeerFlow AI agent platform via its HTTP API. Use this skill when the user wants to send messages or questions to DeerFlow for research/analysis, start a DeerFlow conversation thread, check DeerFlow status or health, list available models/skills/agents in DeerFlow, manage DeerFlow memory, upload files to…