Review an exported Adobe Workfront Planning + Fusion scenario blueprint (.json) for performance, speed, resource use, and API call volume, and produce a prioritized optimization self-review.
$ npx skills add adobe/skills --skill wf-planning-fusion-optimizer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install adobe/skills wf-planning-fusion-optimizer --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/adobe/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/workfront/skills/wf-planning-fusion-optimizer .claude/skills/wf-planning-fusion-optimizer && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "wf-planning-fusion-optimizer" agent skill from https://github.com/adobe/skills/tree/main/plugins/workfront/skills/wf-planning-fusion-optimizer into .claude/skills/wf-planning-fusion-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wf-planning-fusion-optimizer", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/adobe/skills/tree/main/plugins/workfront/skills/wf-planning-fusion-optimizerType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add adobe/skills --skill wf-planning-fusion-optimizer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install adobe/skills wf-planning-fusion-optimizer --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/adobe/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/workfront/skills/wf-planning-fusion-optimizer .agents/skills/wf-planning-fusion-optimizer && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "wf-planning-fusion-optimizer" agent skill from https://github.com/adobe/skills/tree/main/plugins/workfront/skills/wf-planning-fusion-optimizer into .agents/skills/wf-planning-fusion-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wf-planning-fusion-optimizer", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add adobe/skills --skill wf-planning-fusion-optimizer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install adobe/skills wf-planning-fusion-optimizer --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/adobe/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/workfront/skills/wf-planning-fusion-optimizer .cursor/skills/wf-planning-fusion-optimizer && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "wf-planning-fusion-optimizer" agent skill from https://github.com/adobe/skills/tree/main/plugins/workfront/skills/wf-planning-fusion-optimizer into .cursor/skills/wf-planning-fusion-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wf-planning-fusion-optimizer", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/adobe/skills.git --path plugins/workfront/skills/wf-planning-fusion-optimizer--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add adobe/skills --skill wf-planning-fusion-optimizer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install adobe/skills wf-planning-fusion-optimizer --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/adobe/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/workfront/skills/wf-planning-fusion-optimizer .gemini/skills/wf-planning-fusion-optimizer && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "wf-planning-fusion-optimizer" agent skill from https://github.com/adobe/skills/tree/main/plugins/workfront/skills/wf-planning-fusion-optimizer into .gemini/skills/wf-planning-fusion-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wf-planning-fusion-optimizer", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install adobe/skills wf-planning-fusion-optimizerInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add adobe/skills --skill wf-planning-fusion-optimizer -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/adobe/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/workfront/skills/wf-planning-fusion-optimizer .github/skills/wf-planning-fusion-optimizer && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "wf-planning-fusion-optimizer" agent skill from https://github.com/adobe/skills/tree/main/plugins/workfront/skills/wf-planning-fusion-optimizer into .github/skills/wf-planning-fusion-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wf-planning-fusion-optimizer", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add adobe/skills --skill wf-planning-fusion-optimizer -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install adobe/skills wf-planning-fusion-optimizer --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/adobe/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/workfront/skills/wf-planning-fusion-optimizer .opencode/skills/wf-planning-fusion-optimizer && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "wf-planning-fusion-optimizer" agent skill from https://github.com/adobe/skills/tree/main/plugins/workfront/skills/wf-planning-fusion-optimizer into .opencode/skills/wf-planning-fusion-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wf-planning-fusion-optimizer", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
wf-planning-fusion-optimizerReview an exported Adobe Workfront Planning + Fusion scenario blueprint (.json) for performance, speed, resource use, and API call volume, and produce a prioritized optimization self-review.
Wf Planning Fusion Optimizer is an agent skill from adobe/skills. Review an exported Adobe Workfront Planning + Fusion scenario blueprint (.json) for performance, speed, resource use, and API call volume, and produce a prioritized optimization self-review. Use this whenever someone shares a Fusion scenario that uses Workfront Planning (Maestro) and wants it faster, cheaper, or more reliable: slow runs, long execution time, scenario timeouts, high operations consumption, too many API calls, or rate-limit / 429 errors, especially when it works in test but breaks under load or at…
Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts and reference files (for example `README.md`, `evals/evals.json` and `references/findings.md`).
It sits in Mobile, covering Rate limiting, Mobile testing and debugging and Database administration. The repository describes itself as: Adobe Skills for Agents. The licence is Apache-2.0.
3 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit c8f44ec. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
pythonFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Wf Planning Fusion Optimizer loads about 1.6k tokens when it runs, and up to ~4.1k if it reads all its reference files. Until then it costs about 232 tokens; SKILL.md has 784 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
The full file from adobe/skills at commit c8f44ec, republished under its Apache-2.0 licence (© adobe). 784 words, ~1,593 tokens.
.claude/skills/wf-planning-fusion-optimizer/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.Takes one or more exported Fusion scenario blueprints (.json) that use the Workfront
Planning (Maestro) connector and produces a prioritized, read-only review of what is
hurting the scenario's performance: run time, operations consumed, and API call volume
(which is what drives rate-limit 429s). It reports; it never modifies the blueprint,
and it does not need live workspace access.
The goal is broader than rate limits. Every module execution in Fusion is an operation, operations cost run time and quota, and loop nesting multiplies them. So the same structural fixes usually improve speed, cost, and rate-limit headroom at once. Rate-limit 429s are one symptom of the same underlying inefficiency.
This reviewer understands the Workfront Planning connector (workfront-maestro
modules). It is not a general Fusion optimizer. If a blueprint has no Planning modules
(for example a Salesforce-to-Slack scenario), the analyzer detects that and returns a
clear "not applicable" result listing the connectors it did find, rather than pretending
to review it. Do not force it onto non-Planning scenarios; tell the user it is
Planning-specific and, if they want, offer general Fusion advice separately without
this skill.
Use it whenever a Workfront Planning Fusion scenario is the subject and the concern is speed, run time, timeouts, operations/quota, cost, reliability, or API limits, even if the person does not say "optimize" or "429". Replication, sync, and migration scenarios benefit most because they walk hierarchies and fan out calls, but it applies to any Planning scenario.
Get the exported blueprint file(s) (Fusion scenario blueprint export, .json).
Multiple can be reviewed together.
Run:
python scripts/analyze_blueprint.py <blueprint.json> [<blueprint2.json> ...]With an optional per-request call export (CSV with Resource, Method, Status Code, and optionally Duration columns) for measured context:
python scripts/analyze_blueprint.py <blueprint.json> --callmix <calls.csv>Add --json for a machine-readable result.
Present the Markdown report as the deliverable, findings in the order returned (highest impact first). Do not invent numbers the script did not produce.
Anchor everything in one model: call volume and operations are module count times how often each module runs, and iteration depth is the multiplier. A GET is cheap at the top of a scenario and expensive several loops deep. Frame each finding in terms of what it improves: calls, operations, run time, reliability, or throughput. The report labels each finding with its "Improves:" dimension for this reason.
State one thing early: there is no bulk-read endpoint. Search and single GET are the only ways to read; bulk exists for writes only. So raising a limit or batching writes does not fix a read-heavy scenario, only reading fewer and wider does. Lead with the read findings (F1, F2, F3) for that reason.
Finding codes, their rationale, and recommended fixes are in references/findings.md.
Read it when you need the full explanation for a code; each finding already carries a
one-line recommendation in the report.
The static analyzer is the base and needs no connection. But if a Workfront Planning MCP
is connected in the session (most customers running Planning have one), use it to make
the recommendations instance-specific instead of generic. It turns opaque ids in the
blueprint (Rt69e105..., F6a2010...) into real record-type and field names, and, more
importantly, confirms on the live schema that each read-collapse finding is actually
feasible: that the child record type has a filterable reference to its parent, and that
the fields a per-record GET fetches are already carried as lookups the search returns.
That is the check that otherwise needs a pasted sample.
Run python scripts/analyze_blueprint.py <blueprint.json> --ids to list the record-type
and field ids the blueprint references, then resolve them through the Planning MCP. The
full workflow, and one important caveat (the MCP verifies the schema and feasibility, not
the Fusion connector's output nesting), are in references/mcp-enrichment.md. Read it
when a Planning MCP is available. This step is strictly read-only, and if no MCP is
connected, skip it: the static report stands on its own.
--callmix, shares are usually sampled: treat them as proportions.© adobe, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 5 other files (scripts, references) in plugins/workfront/skills/wf-planning-fusion-optimizer of adobe/skills.
Open the folder on GitHubat commit c8f44ec
Wf Planning Fusion Optimizer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Wf Planning Fusion Optimizer this skilladobe/skills | 197 | — | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| Running Tests On Gradle Managed Devicesskydoves/android-testing-skills | 334 | — | ~3.6k | Automated safety check: Notes | Apache-2.0 | |
| Upstash Redisgithub/awesome-copilot | 40k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Discord Bot Architectdavila7/claude-code-templates | 33k | 7 repos | ~2k | Automated safety check: Pass | MIT | |
| Roblox Networkinggamedev-skills/awesome-gamedev-agent-skills | 1.4k | — | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| Phone HarnessShawnPana/phone-harness | 3.2k | — | ~4.3k | Automated safety check: Pass | MIT |
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Categories
Review an exported Adobe Workfront Planning + Fusion scenario blueprint (.json) for performance, speed, resource use, and API call volume, and produce a prioritized optimization self-review. Wf Planning Fusion Optimizer is an agent skill from adobe/skills.json) for performance, speed, resource use, and API call volume, and produce a prioritized optimization self-review.
Wf Planning Fusion Optimizer fits situations like: phrases like make my Fusion scenario faster; why is this Planning scenario so slow; reduce operations; our replication.
Run `npx skills add adobe/skills --skill wf-planning-fusion-optimizer -a claude-code`. Or copy the skill folder (plugins/workfront/skills/wf-planning-fusion-optimizer in adobe/skills) into .claude/skills/wf-planning-fusion-optimizer in your project. Claude Code loads it when a task matches its description.
Run `npx skills add adobe/skills --skill wf-planning-fusion-optimizer -a codex`. Or copy the skill folder (plugins/workfront/skills/wf-planning-fusion-optimizer in adobe/skills) into .agents/skills/wf-planning-fusion-optimizer in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add adobe/skills --skill wf-planning-fusion-optimizer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/wf-planning-fusion-optimizer, .gemini/skills/wf-planning-fusion-optimizer, .github/skills/wf-planning-fusion-optimizer and .opencode/skills/wf-planning-fusion-optimizer in your project.
Going by SKILL.md and its folder, Wf Planning Fusion Optimizer needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Wf Planning Fusion Optimizer is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 1.6k tokens (SKILL.md is roughly 6.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.5k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Wf Planning Fusion Optimizer: Running Tests On Gradle Managed Devices (skydoves/android-testing-skills, 334 stars), Upstash Redis (github/awesome-copilot, 40k stars), Discord Bot Architect (davila7/claude-code-templates, 33k stars) and Roblox Networking (gamedev-skills/awesome-gamedev-agent-skills, 1.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
adobe (a GitHub organization) maintains it in adobe/skills, which has 197 GitHub stars. The repository holds 66 skills in this directory. The repository was last updated on October 10, 2026.
Source: adobe/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.
