platform-sync
Skill2 starsChanged 3 months agoMITView on GitHub
Use when this repo may have fallen behind the AI coding platforms it targets — Claude Code, Codex CLI, Cursor, Gemini CLI, OpenCode. Triggers: 'what new features am I missing', 'am I up to date with the latest docs', 'is my plugin using current best practices', 'sync my plugin', 'audit every platform', '/platform-sync'. Also when a platform manifest or CHANGELOG is being bumped, a release is being cut, or more than a day has passed since the last check.
In this repository46 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- .cursor/rules/commit-conventions.mdcCursor rule
- .cursor/rules/cross-platform-handoff.mdcCursor rule
- .cursor/rules/dev-files-workspace.mdcCursor rule
- .cursor/rules/documentation-standards.mdcCursor rule
- .cursor/rules/gh-cli-preference.mdcCursor rule
- .cursor/rules/github-repository-policy.mdcCursor rule
- .cursor/rules/git-worktree-agent-workflow.mdcCursor rule
- .cursor/rules/hooks-guide.mdcCursor rule
- .cursor/rules/plugin-structure.mdcCursor rule
- .cursor/rules/plugin-version-bump.mdcCursor rule
- .cursor/rules/skill-frontmatter.mdcCursor rule
- .cursor/rules/skill-quality-standards.mdcCursor rule
- .cursor/rules/skills-guide.mdcCursor rule
- .cursor/rules/user-facing-script-standards.mdcCursor rule
- run-tamirs-superpowersSkill
- field-notebook-uiSkill
- diagnose-refusalSkill
- targeted-debugSkill
- decisionSkill
- deliver-devSkill
- diagnose-objectiveSkill
- orchestrate-devSkill
- plan-devSkill
- pr-devSkill
- start-devSkill
- switch-devSkill
- worker-devSkill
- changelog-reviewSkill
- dark-terminal-docSkill
- docs-reviewSkill
- mcp-builderSkill
- mcp-paginationSkill
- cleanupSkill
- github-policySkill
- multi-agent-repoSkill
- repo-scaffoldSkill
- repo-standardsSkill
- capture-configSkill
- find-skillSkill
- notify-setupSkill
- retroSkill
- session-reportSkill
- skill-creatorSkill
- usage-captureSkill
The file
We split this skill into the rules it gives an agent
9 rules. About 0 are common, 4 are distinctive, and 5 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
Distinctive · estimate
This author's own choices. Worth a look.
5
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 80
Hard constraint: never guess what's new. Every improvement step cites a URL that was actually fetched.
- Distinctive: line 83
Probe-informed, not probe-dependent: When a version probe has already found the delta between what this repo's docs claim and what is live …
- Very specific: line 100
Follow references/registry.md.
- Very specific: line 112
If nothing matches, emit the "No AI coding assistant usage detected" block from references/detection.md and stop.
- Very specific: line 117
Follow references/probe.md.
- Very specific: line 130
Run references/analysis-protocol.md once per detected target, using that target's reference file as its data, and the probe result from …
- Distinctive: line 194
Every step includes a concrete snippet.
- Very specific: line 197
Never propose a capability the registry marks unsupported or unknown — those go under "Documented gaps", labelled honestly as unsupported …
- Distinctive: line 199
Never emit a separate section for a runtime surface (Claude Desktop rides on Claude Code).
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
platform-sync
You are a multi-platform improvement planner, and you are the only orchestration engine in this family. Detect which AI coding assistant targets a repo actually uses (not only plugin manifests), fetch live docs for each, and synthesize a concrete numbered improvement plan.
The platform list is data, never a list in these instructions. It is resolved from the capability registry at run time. Adding a sixth or tenth target means adding registry data and one reference file — it must never mean editing this skill or creating another one.
Hard constraint: never guess what's new. Every improvement step cites a URL that was actually fetched. A fetch failure is reported, never smoothed over with training knowledge.
Probe-informed, not probe-dependent. When a version probe has already found the delta
between what this repo's docs claim and what is live upstream, this skill's real job shrinks
to judging that delta — is it relevant here, does it change a capability row, does it need a
docs/engineering/host-notes/<host>.md line — rather than finding it from a standing start via open-ended
research. Without a probe (or an unusable one), it still runs the full hand-research pass
unchanged; see references/probe.md.
Read before Step 1:
references/registry.md— how the target list and capability gaps are resolvedreferences/detection.md— cross-target detection rulesreferences/probe.md— consuming a version probe's output, and the fallback when there is nonereferences/analysis-protocol.md— the per-target analysis loop
Step 1 — Resolve the target list
Follow references/registry.md. Result: a set of target ids, each with a
references/platforms/<id>.md reference file, plus the capability data that governs what
may be recommended to each.
Report which registry source answered. If it was the fallback list, say so.
Step 2 — Detect which targets this repo uses
Glob and read the repo root (or the path passed as an argument). For each resolved target,
apply the "Detection signals" table in its reference file, under the cross-target rules in
references/detection.md. Record the signals that triggered each target and their strength.
If nothing matches, emit the "No AI coding assistant usage detected" block from
references/detection.md and stop.
Step 3 — Consult probe output, if available
Follow references/probe.md. Result: for each target detected in Step 2, either a parsed
pinned/current/reachable triple, or an explicit "no probe input" fallback signal (script
missing, run failed, or output unparseable).
This does not skip Step 4 — it determines how much of Step 4 is fetching versus judging. A target the probe confirms current still gets a (trivial) section; a target the probe cannot speak to gets the full hand-research pass exactly as before this step existed.
Report which path was taken (available, not available, or stale/malformed) next to the
registry source line in the final output.
Step 4 — Analyse each detected target
Run references/analysis-protocol.md once per detected target, using that target's
reference file as its data, and the probe result from Step 3 for that target (if any).
Parallelism is capability-gated. Where parallel_subagents is available, dispatch the
targets concurrently — one subagent per target, each returning only its finished section.
Where it is not, run them sequentially in the same session. The protocol, the constraints
and the output shape are identical either way; never let the two paths produce different
findings, and never claim a parallel run on a platform that cannot do one.
Collect every section before synthesizing.
Step 5 — Synthesize the unified improvement plan
Merge every target's section into one prioritized plan, sorted by:
- Critical — breaking changes, deprecated patterns, silent capability loss between targets
- Quick wins — high impact, minimal config change
- Medium — meaningful, moderate effort
- Low — optional
Merge duplicates: one step that applies to three targets is one step tagged with all three, never three steps.
Output format
# Platform Sync — Improvement Plan
**Repo:** <path>
**Registry source:** <core/capabilities/platforms.json | platform-targets.json | fallback>
**Probe:** <available | not available | stale/malformed>
**Targets resolved:** <all ids> **Detected:** <ids with signals>
**Date:** <today>
---
## Improvement steps
### 1. <Step title> [<Platform>]
**Why:** one sentence — the benefit or the risk
**Effort:** low | medium | high
**Change:**
```<lang>
<concrete config or code snippet>Source: <fetched URL>
Already well-used
<feature>: correctly implemented ✓ (<Platform>)
Documented gaps (not improvements)
<capability>on<Platform>: unsupported | unverified —<registry fallback>
Fetch errors
<Platform>:<url>—<error>(omit the section when there were none)
Summary table
| # | Platform | Feature | Effort |
### Rules
- Every step includes a concrete snippet. "Consider using X" is not a step.
- Tag steps with the target display names; merge cross-target steps rather than repeating.
- A target with no findings still gets a line: "No improvements found for <Platform> — config is current."
- Never propose a capability the registry marks `unsupported` or `unknown` — those go under
"Documented gaps", labelled honestly as unsupported or unverified.
- Never emit a separate section for a runtime surface (Claude Desktop rides on Claude Code).
- Report every fetch error. A partial audit stated plainly beats a complete-looking guess.
- Report the probe path honestly. A target audited via full hand research says so — never
imply a probe confirmed a result that hand research actually produced.Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

