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/qiuy-collab/AutoFlow.skillnpx agentmods add skills/qiuy-collab/autoflow.skill/upstreamWrote 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/qiuy-collab/autoflow.skill/upstream)<a href="https://agentmods.dev/skills/qiuy-collab/autoflow.skill/upstream"><img src="https://agentmods.dev/badge/skills/qiuy-collab/autoflow.skill/upstream/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/qiuy-collab/autoflow.skill/upstream"><img src="https://agentmods.dev/badge/skills/qiuy-collab/autoflow.skill/upstream.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 | $0.00124 | $0.03036 |
| Opus 5 | $0.00062 | $0.01518 |
| Sonnet 5 | $0.00025 | $0.00607 |
| Haiku 4.5 | $0.00012 | $0.00304 |
Grade A, and why
presentation-skill 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 yesterday.
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 — 393 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Presentation Skill
Create editable PowerPoint decks from source files. The model owns narrative, evidence, and design judgment; repository scripts own deterministic rendering, staging, and QA.
Backbone
- Treat
outline.json, planning files, local data, and figure scripts as the source of truth. - Build with repository scripts. Do not write one-off inline
python-pptxorpptxgenjsdeck code. - Fix source and rebuild. Do not patch generated
.pptxfiles when source is available. - Keep charts, tables, text, and layout objects editable where practical.
- Run QA and inspect rendered slides before delivering a deck.
- Never install dependencies during a deck task. Report a missing dependency.
- Scaffold a new topic from its own evidence and story. Do not clone another deck's slide sequence as a house style.
Start Here
Read only the context needed for the current phase:
DESIGN.mdfor the compact design contract.references/outline_schema.mdfor fields and supported variants.references/model_adaptive_workflow.mdfor execution-profile selection.
Then load one task-specific reference:
- Saved/rebuildable deck:
references/deck_workspace_mode.md - Existing PPTX edit:
references/editing.md - Data/figure workflow:
references/reproducible_workflow.md - Style inspiration or screenshot/template matching:
references/style_reference_catalog.md - Structural diversity or topic-to-grammar routing:
references/composition_grammar_catalog.md - PptxGenJS renderer changes:
references/pptxgenjs.md - Fresh-eyes rendered QA:
references/visual_qa_prompt.md
Do not preload the corpus, all preset descriptions, or every workflow reference. Search or open the selected item on demand.
Model-Adaptive Route
Use workload profiles as orchestration controls, not as different quality definitions:
quality-first(sol,frontier,pro): difficult/high-stakes decks, complex evidence, full rendered review, optional bounded scouts.balanced(terra,standard): default professional route, at most one useful scout, one focused render/repair loop.fast(luna,draft): short internal drafts, deterministic routing, render-free first pass, then one final render.auto: choose from the request; high-stakes or evidence-heavy work becomes quality-first, explicit rough drafts become fast, everything else balanced.
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.
- yesterday First seen · 393 lines · 124 tokens per session scan A c1916aaf9060
presentation-skill is a skill published in the GitHub repository qiuy-collab/AutoFlow.skill (2 stars, last pushed 2d ago), licensed MIT. It adds 124 tokens to every session and 3,036 once invoked, about $0.0006 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 2026-09-11.
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