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 autodesk-platform-services/skills --skill flow-ptr-appgit clone --depth 1 https://github.com/autodesk-platform-services/skillsWrote 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/autodesk-platform-services/skills/flow-ptr-app)<a href="https://agentmods.dev/skills/autodesk-platform-services/skills/flow-ptr-app"><img src="https://agentmods.dev/badge/skills/autodesk-platform-services/skills/flow-ptr-app/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/autodesk-platform-services/skills/flow-ptr-app"><img src="https://agentmods.dev/badge/skills/autodesk-platform-services/skills/flow-ptr-app.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 | $0.00083 | $0.02258 |
| Opus 5.5 | $0.00033 | $0.00903 |
| Sonnet 5.5 | $0.00017 | $0.00452 |
| Haiku 4.5 | $0.00008 | $0.00226 |
Grade A, and why
flow-ptr-app 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 9d 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create a Flow Production Tracking (FPTR) Toolkit App
This skill guides developers and pipeline engineers through building an FPTR app, following a spec-driven way — capture intent, check for reuse, write and validate a spec, plan, then implement, verify, release, and maintain against that spec. Tell the user which phase (below) you're starting before acting in it, so they can track progress and step in between phases.
Reference guide: Developing a Toolkit App Default app template repo: https://github.com/shotgunsoftware/tk-multi-starterapp
App spec-driven lifecycle
Each step below that has mechanical how-to links to a references/*.md guide — read it while
executing that step, not before; keep the skill itself light and pull in detail only when it's
actually needed. Phases 2, 7, and 9 have no dedicated guide — they're about the spec and process
rather than a tool/config action.
- Phase 1 — Requirements/Intent capture
- Step 1.1 — Ask for the business need, and capture it in a short spec (one paragraph or a few bullet points) — what the app should do, on which context and environment it will run, whether it needs a UI, whether it needs toolkit hooks, constraints and acceptance criteria.
- Step 1.2 — Find if an existing app / project setting / hook cover fully or partially the requirements, and confirm with the user whether later we will fork/extend from that tool or scaffold a new app from the starter template. Read references/existing-functionality.md while executing this step.
- Phase 2 — Specification — before cloning anything, write down what the app does:
- Step 2.1 — Clarify the goal, the app name, dependencies on Flow PTR frameworks/engines, the environment the tool runs in (e.g. Sequence/Shot/Episode-specific), the entities involved, the settings schema, which hooks the tool may need to expose and why, whether it needs a UI, and its acceptance criteria.
- Step 2.2 — Validate the spec before planning: check it for internal contradictions, missing edge cases (no-UI/headless path, permissions, multi-engine support), and security/data-privacy concerns (e.g. what PTR fields/entities the app reads or writes). Flag gaps to the user instead of assuming an answer; only move to Phase 3 once the spec is complete and consistent.
- Phase 3 — Plan/Design
- Step 3.1 — Locate the project's pipeline configuration and figure out how it's set up (centralized vs. distributed). Read references/locating-config.md while executing this step.
- Step 3.2 — Find or create a sandbox or a dev configuration if it doesn't exist. Read references/sandbox-dev-configuration.md while executing this step.
- Step 3.3 — Clarify where the app's source code should live, and how it will be installed into the target configuration (dev path vs. install_app + switch_app). Read references/cloning-template.md and references/install-app.md while executing this step.
- Step 3.4 — Break the spec into a technical plan: architecture decisions, file/module
breakdown, sequencing of work, identification of risks or open questions. Where practical,
decide to keep core logic separate from UI code (e.g.
app.py/a logic module stays UI-agnostic, withdialog.pycalling into it) — this is what lets Phase 6 exercise the tool as a headless command intk-shellbefore wiring up the dialog. Read references/implement-app.md while executing this step for how that split plays out in code. - Step 3.5 — Split the plan into discrete, independently verifiable tasks/tickets, each with clear inputs/outputs and acceptance criteria, so that the work can be parallelized and tracked.
- Phase 4 — Scaffold
- Step 4.1 — Clone the reference tool —
tk-multi-starterappby default, or whichever existing app Phase 1 found as a better fit. Read references/cloning-template.md while executing this step.
- Step 4.1 — Clone the reference tool —
- Phase 5 — Implement against spec
- Step 5.1 — Fill in
info.ymland implement code with main logic starting inapp.pyand UI starting inpython/app/dialog.py. Declare and implement hooks if applicable. Read references/app-manifest.md, references/app-hooks.md, and references/implement-app.md while executing this step.
- Step 5.1 — Fill in
- Phase 6 — Verify against spec
- Step 6.1 — Test and iterate using Toolkit's "Reload and Restart" menu item, checked against
acceptance criteria, not just "does it run". If possible and it has a dependency on UI, test
first in
tk-shellor a batch context, then in tk-desktop and then in the target DCC engine, if applicable. If app is a Menu Action Item, test it in the FPTR Desktop and then in the web UI. Read references/test-app.md while executing this step.
- Step 6.1 — Test and iterate using Toolkit's "Reload and Restart" menu item, checked against
acceptance criteria, not just "does it run". If possible and it has a dependency on UI, test
first in
- Phase 7 — Change management
- Step 7.1 — Commit in git, pushing changes only to the sandbox configuration, not production.
- Step 7.2 — Future feature changes update the spec first (Phase 2, re-validating it per Step 2.2), then cascade forward through Phases 3-6 — the spec stays the source of truth, not the code.
- Phase 8 — Release to production
- Step 8.1 — Once ready for release, warn the user about the implications of bringing to production the new changes. Analyse possible side effects or interruptions to workflow.
- Step 8.2 — Tag a release and push the sandbox config changes to the production pipeline configuration. Read references/release.md while executing this step.
- Phase 9 — Maintenance
- Step 9.1 — Treat post-release bug reports or new asks as spec changes, not code patches: update the spec first (back to Phase 2), validate it again, then re-enter Phase 3 and cascade forward through Phases 4-8 — the spec stays the source of truth for the life of the app, not just during initial development.
What ships with it
14 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.
- README.md 391 B
- references/app-hooks.md 1.3 KB
- references/app-manifest.md 2.0 KB
- references/centralized-config.md 2.0 KB
- references/cloning-template.md 2.5 KB
- references/distributed-config.md 2.8 KB
- references/existing-functionality.md 3.6 KB
- references/implement-app.md 3.2 KB
- references/install-app.md 3.4 KB
- references/locating-config.md 2.1 KB
- references/python-api-best-practices.md 2.3 KB
- references/release.md 2.1 KB
- references/sandbox-dev-configuration.md 2.3 KB
- references/test-app.md 1.1 KB
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.
- 9d ago Changed · +4 lines e59a0aedb17f
- 1mo ago First seen · 127 lines · 83 tokens per session scan A 0a5b37acd14f
flow-ptr-app is a skill published in the GitHub repository autodesk-platform-services/skills (65 stars, last pushed 2d ago), licensed MIT. It adds 83 tokens to every session and 2,258 once invoked, about $0.0003 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
feature-prioritisation
Aplica marcos de priorización (RICE, MoSCoW, Kano, ICE, Opportunity Scoring) para clasificar características e items del backlog. Úsalo cuando se te pida priorizar características, clasificar un backlog, decidir qué construir a continuación, o evaluar compensaciones entre ideas competidoras. Produce una lista de…
design-handoff-brief
Transform feature briefs into structured design briefs that give designers the context they need before opening Figma. Use when asked to write a design brief, create a design handoff, brief a designer on a new feature, or translate a PRD into design requirements. Produces a brief with user goal, emotional context…
contact-cache
Track all identified/contacted people across strategies. CSV-backed contact database with dedup by LinkedIn URL or email. Prevents duplicate outreach when running strategies on a recurring cadence.
stakeholder-communication
Communicate effectively with stakeholders across functions and seniority levels. Use this skill when writing status updates, preparing executive reviews, sharing technical decisions with non-technical audiences, managing up, communicating bad news, or designing the communication cadence for a project. Triggers on…
service-desk
Runs the IT service desk — intake, triage, prioritization, escalation, knowledge, and the metrics that improve service rather than distort it. Use this to set up or fix a service desk, design ticket priority and escalation, reduce repeat contacts, structure a knowledge base, or work out why a desk hitting its targets…
estimating-and-contingency
Produces a cost or effort estimate someone can defend — decomposing the work, choosing between analogous, parametric and bottom-up methods, documenting the basis and its assumptions, expressing confidence as a range, and sizing contingency and management reserve separately. Use this to build an estimate, challenge one…