Agent skill

V2 Perf Iteration

by mirage-project in mirage-project/mirage

Runtime-V2 performance-iteration workflow. An agent skill from mirage-project/mirage.

Apache-2.0Auto-check passedAgent Workflows

Install V2 Perf Iteration

skills CLI
$ npx skills add mirage-project/mirage --skill v2-perf-iteration -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mirage-project/mirage v2-perf-iteration --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mirage-project/mirage.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/v2-perf-iteration .claude/skills/v2-perf-iteration && rm -rf skills-src

Use ~/.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/

Facts

Skill name
v2-perf-iteration
GitHub stars
2.5k
Token cost
~4k tokens
SKILL.md length
1,591 words
Files
5 (incl. references)
Skills in repo
24
Repo updated
First seen
Licence
Apache-2.0

At a glance

Runtime-V2 performance-iteration workflow. An agent skill from mirage-project/mirage.

  • Running a perf-optimization campaign
  • SKILL.md covers Environment prerequisites…, The loop, Invariants (every iteration —… and v2 measurement quickstart (the…, plus 1 more section
  • Runs Python scripts from its folder; calls python
  • Iteration on the v2 runtime (--use-v

What it does

V2 Perf Iteration is an agent skill from mirage-project/mirage. Runtime-V2 performance-iteration workflow. Use when running a perf-optimization campaign or iteration on the v2 runtime (--use-v2) — measuring a baseline, ranking bottlenecks, planning levers, implementing, re-measuring, and landing/recording the verdict. Drives the loop MEASURE→ANALYZE→PLAN→NEXT-MOVE→REVIEW→IMPLEMENT→VALIDATE+RE-MEASURE→LOOP-OR-LAND→RECORD with the mpk- subagent roster, the v2 profiler/perfetto toolchain, and the TIER-1 TP8 verdict discipline.

Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `references/loop-agents.md`, `tools/perfetto_analyze.py` and `tools/perfetto_depgraph.py`).

It sits in Agent Workflows, covering Subagents and Performance optimization. The repository describes itself as: Mirage Persistent Kernel: Compiling LLMs into a MegaKernel. The licence is Apache-2.0.

When your agent uses it

  • Running a perf-optimization campaign
  • Iteration on the v2 runtime (--use-v
  • — measuring a baseline
  • Ranking bottlenecks

Example prompts

  • “/v2-perf-iteration”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit f9eb70c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    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.

  • Runs code

    Ships script files (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

V2 Perf Iteration loads about 4k tokens when it runs, and up to ~5.8k if it reads all its reference files. Until then it costs about 121 tokens; SKILL.md has 1,591 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~121
When it runs · the whole SKILL.md, loaded when a task matches
~4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.8k

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.

Safety

Auto-check passed

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from mirage-project/mirage at commit f9eb70c, republished under its Apache-2.0 licence (© mirage-project). 1,591 words, ~3,959 tokens.

Download SKILL.mdSave it as .claude/skills/v2-perf-iteration/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
v2-perf-iteration
description
Runtime-V2 performance-iteration workflow. Use when running a perf-optimization campaign or iteration on the v2 runtime (--use-v2) — measuring a baseline, ranking bottlenecks, planning levers, implementing, re-measuring, and landing/recording the verdict. Drives the loop MEASURE→ANALYZE→PLAN→NEXT-MOVE→REVIEW→IMPLEMENT→VALIDATE+RE-MEASURE→LOOP-OR-LAND→RECORD with the mpk-* subagent roster, the v2 profiler/perfetto toolchain, and the TIER-1 TP8 verdict discipline.

V2 Perf Iteration — the measurement-driven optimization loop

This is the perf-optimization loop of the v1 multi-agent campaign that ran for months (the old repo-root WORKFLOW.md is now a superseded stub pointing here), upgraded for Runtime-V2's measurement reality. Siblings: v2-model-support (bring-up; its Phase (d) is this skill), v2-kernel-writing (the per-KERNEL inner loop this skill dispatches into when a lever is kernel-body work). The main thread (or one lead orchestrator) runs the loop and does all edits/commits/box-ops; subagents measure-parse / analyze / plan / review / record. No subagent dispatches another subagent, and box operations never go inside a subagent (v2-model-support/references/box-orchestration.md).

Goal + verdict metric — pin ONE per campaign, then do not drift. The loop is metric-agnostic; what is non-negotiable is that a single PRODUCTION verdict config is declared up front and every lever's verdict-grade Δ is measured there. Worked example — the concluded 2026-06/07 DSv3 campaign's instance: e2e decode tpot, bs=1, TP8 EP2, MTP off, toward 8 ms/token (SGLang 7.99 on the same box proves it reachable); v2 clean baseline 2026-07-07: 12.069 ms/tok vs v1 ~9.787; TP<8/local runs are triage only. A new campaign (e.g. Qwen3-8B single-GPU bs=1024 throughput) writes its own goal line in this exact shape — metric, config, target, why-reachable — and restates it in EVERY dispatch prompt. Per-task numbers follow the TIER hierarchy (v2-kernel-writing/references/validation-debug.md §8): TIER-1 = in-MPK %globaltimer / per-position slowest-CTA at the production grid — the ONLY verdict tier; faithful harness corroborates (TIER-2); cudaEvent-wall / standalone-warm are diagnostic only.

⚠️ The .claude/agents/mpk-* defs still carry v1-era DSv3 framing (150µs/MoE-layer, TP4, per_position_grid.py, scratch/ helper scripts that are git-ignored/machine-local). Every dispatch prompt MUST restate the CURRENT campaign goal, the v2 toolchain commands from the quickstart below, and the artifact paths — otherwise the agent drifts to the stale v1 pipeline (each def now carries a "V2 / new-campaign note" saying exactly this).

Environment prerequisites (what must exist on the machine)

In-repo (travels with every clone): the parser python -m mirage.mpk.prof (python/mirage/mpk/prof.py, tracked), the demo --profiling plumbing, this skill's references/ + tools/. Machine-local / degradations:

  • scripts/v2_perfetto_export.py / perfetto_analyze.py / perfetto_depgraph.py are UNTRACKED on this branch — a fresh clone will not have them under scripts/. Archived copies travel in .claude/skills/v2-perf-iteration/tools/ — run them from there (or copy back to scripts/, which stays git-ignored for local files). mirage.mpk.prof summary/check/pagewait is the tracked no-dependency fallback for text-table analysis.
  • The TP8 box — only for multi-GPU verdict configs (v2-model-support/references/ box-orchestration.md; §1-2 there are site-specific). Single-GPU campaigns run the whole loop locally.
  • experiment_history/ — git-ignored ⇒ empty on a fresh clone; create INDEX.md + the journal at step 9 of the first iteration. The kernel-lever anti-loop that must survive clones is v2-kernel-writing/references/m1-decode-evidence.md (in-repo).
  • User-level agents (~/.claude/agents/: mpk-perf-analyzer, ablation-logic-reviewer, codex-task-dispatcher) — same-account only; the rest of the roster is in-repo at .claude/agents/. Codex MCP — machine-configured (.mcp.json is git-ignored); absent ⇒ reviews degrade to subagent-only (state it). Personal memory — optional context.
  • ~/ref_vllm_sglang.md (analyzer/planner per-kernel reference table) — machine-local; absent ⇒ rank gaps against the SGLang/vLLM numbers recorded in the campaign goal line and in-repo docs, and say the external table was unavailable.

The loop

(1) MEASURE ─▶ (2) ANALYZE ─▶ (3) PLAN ─▶ (4) NEXT-MOVE ─▶ (5) REVIEW-BEFORE-ACT
                                  ▲                                   │
                                  │ (new bottleneck / re-plan)        ▼
(9) RECORD ◀─ (8) LOOP-OR-LAND ◀─ (7) VALIDATE + RE-MEASURE ◀─ (6) IMPLEMENT
#StepWhoIn → Out
1MEASUREmain thread (box) + mpk-profiler disciplineprofiled --use-v2 run → per-task-type consumer-body table + per-position slowCTA + tpot
2ANALYZEmpk-perf-analyzer (Opus)trace/tables → ranked gaps vs refs, kernel-level vs system-level split
3PLANmpk-optimization-planner (Opus)report + history → µs-derived ranked batch plan, [MAIN|ENGINEER|FERRET|CODEX] tags, 3-round Codex convergence
4NEXT-MOVEmpk-iteratorplan + report → reflection + the single next move w/ falsifiable predicted Δ
5REVIEWablation-logic-reviewer + Codex MCPthe move/conclusion → first-principles audit (MANDATORY before acting)
6IMPLEMENTmain thread (route by tag)env-gated default-OFF change
7VALIDATEmpk-correctness-gate, then re-run (1)PASS/FAIL + the TIER-1 TP8 Δ
8LOOP-OR-LANDmain thread + mpk-commit-reviewercommit (WIN) / revert+INDEX (NULL/REGRESS) / re-plan
9RECORDmpk-memory-keeperjournal + INDEX row + personal-memory lesson

Full roster card (what each agent consumes/returns + key discipline): references/loop-agents.md.

1. MEASURE. The mpk-profiler pattern updated for v2: GPU-safety pre-flight → the canonical config → profiled run → parse → cleanup+zombie-guard → report. At TP8 the box session belongs to the MAIN THREAD (setup/poll split, retries, verify-STOPPED — follow v2-model-support/references/box-orchestration.md; do NOT re-derive box mechanics here): rsync → --use-v2 --profiling run (quickstart below) → retrieve the per-rank *_v2prof.npy → v2_perfetto_export.py + python -m mirage.mpk.prof summary/check. Dispatch mpk-profiler itself only for local-GPU triage runs or offline parsing of an already-retrieved buffer — never for box ops. Report = tpot (n-of-N) + the per-task-type consumer-body table (µs/instance × count × layers = ms and % of tpot) + per-position slowCTA

  • correctness precondition (coherent output, routed-MoE non-null).

2. ANALYZE (optional on small iterations, mandatory on a fresh baseline). Ranked TODO split kernel-level (body ≫ SOTA ref at M=1 shape) vs system-level (dep-wait, page-wait, role-coordination overhead, AR/skew — the v2 runtime-overhead axis that made v2 12.07 vs v1 9.79). It reads experiment_history/INDEX.md first.

3. PLAN. µs-derived ranked batch plan; every lever: target position + arithmetic + on-critical-path reasoning + correctness risk + dispatch tag. Anti-loop is MANDATORY: check experiment_history/INDEX.md AND v2-kernel-writing/references/m1-decode-evidence.md (the DEAD/WIN/UNTESTED map) — a dead lever is only re-proposable by naming what's different.

4. NEXT-MOVE. One concrete single-iteration move with a falsifiable predicted Δ ("tpot 12.07 → ~11.5 because attn consumer body 108 → ~99µs and attn is on the CP").

5. REVIEW-BEFORE-ACT (MANDATORY, user-locked). Every non-trivial conclusion — root-cause, ablation verdict, dead/alive, ceiling, perf claim — goes through ablation-logic-reviewer (first-principles re-derivation) AND a Codex MCP cross-check (mcp__codex__codex, DEFAULT params) BEFORE you act on it or report it settled. When stuck: detailed multi-turn Codex discussion BEFORE escalating to the user (escalate only when both agree there's no room).

6. IMPLEMENT (main thread routes by tag):

  • [ENGINEER] kernel-body / new-op / port work → the v2-kernel-writing skill (SPEC→IMPLEMENT→WIRE→VALIDATE→PERF→REVIEW; its Stage 2 dispatches v2-kernel-engineer, and ferret-kernel-agent/kda-kernel-agent are its beat-a-target engines).
  • [FERRET]/[KDA] standalone beat-the-SOTA kernel rewrite → ferret-kernel-agent (frozen-gate autonomous loop) or kda-kernel-agent (verdict-grade honest transfer); routing one-liners in references/loop-agents.md.
  • [CODEX] scoped investigation/experiment → codex-task-dispatcher.
  • [MAIN] builder / plan / scheduling / system change → main thread edits directly. Every lever lands env-gated default-OFF; default build byte-identical.

7. VALIDATE + RE-MEASURE. Math-changing → mpk-correctness-gate (test-mode + non-null MoE + the TP8-nondeterminism-aware gates: deterministic canary, poison-fill, coherence-in-envelope — see validation-debug.md §7). Math-neutral → token-identity on a deterministic config. Then re-run step (1); the verdict is the TIER-1 TP8 number, and the predicted Δ is confirmed or refuted — say which.

8. LOOP-OR-LAND decision rules:

  • WIN (predicted Δ held at TP8) → land: mpk-commit-reviewer gate (staged-path, default-OFF byte-identity, message mechanism+Δ+sign-off) then commit. BLOCK → fix, re-gate.
  • NULL/REGRESS → revert the lever (or leave default-OFF-dead), INDEX row WITH the why — recording the death is the deliverable, not a failure.
  • Bottleneck shifted / lever class exhausted / stalled → back to (3) re-plan. No stall-stop: a stall means the next idea isn't found yet (planner researches refs).
  • STOP only at goal, user halt, or hardware down.

9. RECORD. mpk-memory-keeper appends the journal entry + INDEX one-liner (esp. NULL/REGRESS) + folds structural lessons into personal memory. This closes the anti-loop: steps (2)-(4) read what (9) wrote.

Show full SKILL.md (514 more words)Show less

Invariants (every iteration — these encode the documented failures)

  • bs=1 ALWAYS; MTP off. Batching/MTP-amortization = goal-drift, not a lever.
  • TP8 = the verdict tier. Local/TP<8 = triage proxy only; TIER-1 in-MPK slowCTA per position, NEVER P50, NEVER per-kernel-type aggregate, NEVER cudaEvent-wall promotion.
  • 4-role-track averaging trap: a v2 task's body = its CONSUMER-group slice; loader/ launcher/storer tracks are mostly waits — averaging across role tracks produces garbage.
  • Correctness-first. No perf number on an unverified forward pass (routed-MoE non-null, num_active≈4; DECODE_LEAN ≠ correctness; coherent decode output for v2 e2e). A correctness bug is root-caused before any perf judgment (never-park-a-bug).
  • Default build byte-identical. Every lever env-gated default-OFF; a default-flip needs measured justification + commit-reviewer sign-off.
  • Every non-trivial conclusion → ablation-logic-reviewer + Codex double-check before it is acted on (the over-claim guard; this project's flip-flop history is why).
  • GPU-safety non-negotiable. Never crash-loop the megakernel (D-state zombies); test-mode first; box verify-STOPPED; one hung run → clean up + stop, don't retry blind.
  • experiment_history closes the loop. No experiment ends without its INDEX row; NULL/REGRESS rows are the most valuable.
  • Falsifiable moves. Each move pre-states its predicted Δ and CP-membership; off-CP wins ≈ 0 e2e.

v2 measurement quickstart (the exact commands)

Profiled run (add to the canonical demo invocation, per-rank under mpirun):

demo/deepseek_v3/demo.py ... --use-v2 --profiling --trace-name <tag> \
    [--profile-start-step N]     # profile steady-state, not warmup
  • --profiling compiles with -DMPK_ENABLE_PROFILING (persistent_kernel.py:504).
  • Buffer contract: v2 needs V2_PROF_BUF_ENTRIES = 120000*128 (15.36M) uint64 entries — demo.py auto-sizes this (V2_PROFILER_BUFFER_ENTRIES, demo.py:30) and HARD-RAISES if MPK_PROFILER_BUFFER_ENTRIES is set smaller (demo.py:64-75): a smaller buffer = silent device OOB (the v2 profiler writes tail accumulators at absolute end-of-buffer indices). Don't override it; don't "fix" a >256-worker abort by shrinking the buffer.
  • Only the LAST V2_PROF_WINDOW_ITERS = 25 decode steps are recorded; 8 tracks/SM (consumer/loader/launcher/storer/controller + 3 phase tracks: dep-wait/page-wait/>2µs).
  • Artifacts per rank: <tag>_rank<r>_v2prof.npy (raw buffer) + <tag>_rank<r>.perfetto-trace (v1 exporter output — garbage for v2 buffers, ignore it) + an auto text summary at run end (prof.print_run_summary).

Parse (usually rank0's npy, retrieved from the box):

python -m mirage.mpk.prof check    <npy>   # structural gate: needs "ALL CHECKS PASS",
                                           # dropped events MUST be 0 (else trace truncated)
python -m mirage.mpk.prof summary  <npy>   # per-task-type consumer table: n/SM/it, dep-wait,
                                           # suffix, body+disp, win-mean/p50 + busy ms/SM/step
python -m mirage.mpk.prof pagewait <npy>   # page-protocol serialization (dead prefetch)
python scripts/v2_perfetto_export.py <npy> <out.json> --last-steps 2 [--sm N]
                                           # Chrome-JSON for ui.perfetto.dev; NEVER --full
                                           # (full window OOMs the UI); deeper analysis:
                                           # scripts/perfetto_analyze.py / perfetto_depgraph.py
                                           # (fresh clone: these are untracked — run the
                                           #  archived copies in this skill's tools/ dir)

What number to quote: per-invocation task latency = ONE consumer slice; per-task-type body = the consumer-group windows (summary's table / the consumer track in perfetto). Never average the role tracks. Headline = e2e tpot + the per-task-type decomposition. Worked example (2026-07-09 profile): attn 108µs × 61 layers ≈ 58% of tpot; ffn (52+14)µs × 58 MoE layers ≈ 30%; AR ≈ 6% → the attn consumer body is the dominant axis, AR is not — that ranking IS the plan input.

Hang/crash during a profiled run: the historical profiled-only wedges were the v2 runtime races, ALL FIXED 2026-07-16 (689dadc5/7d271a01/025029a1/7b6ae2bb; former wedge windows pass post-fix — see v2-kernel-writing/references/validation-debug.md §5.1), so profiled measurement is first-class again; a hang on a ≥7b6ae2bb tree is a NEW bug. Watchdog -DMPK_V2_BREADCRUMB + MPK_V2_HANG_WATCHDOG_S=<s> names the hung task; crash → compute-sanitizer memcheck is ground truth (breadcrumb in-flight counts are base-rate artifacts). Full triage table: validation-debug.md §5. Remember: instrumentation changes tpot (breadcrumb cost ~5.3ms on full-61L) — never quote an instrumented run as the baseline.

References

DocContent
references/loop-agents.mdRoster card: every loop agent + the kernel-perf engines + routing
tools/Archived copies of the untracked v2 perfetto toolchain (v2_perfetto_export.py, perfetto_analyze.py, perfetto_depgraph.py) — the clone-safe way to run them
../v2-kernel-writing/references/validation-debug.mdTIER hierarchy §8, profiler contract §9, hang/crash triage §5
../v2-kernel-writing/references/m1-decode-evidence.mdThe DEAD/WIN/UNTESTED anti-loop map for kernel levers
../v2-model-support/references/box-orchestration.mdBox session playbook (TP8 runs live here)
experiment_history/README.md + INDEX.mdThe durable log contract + the anti-loop source

© mirage-project, 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

Files

SKILL.md and 4 other files (references) in .claude/skills/v2-perf-iteration of mirage-project/mirage.

  • SKILL.md
  • references/loop-agents.md
  • tools/perfetto_analyze.py
  • tools/perfetto_depgraph.py
  • tools/v2_perfetto_export.py

Open the folder on GitHubat commit f9eb70c

Compare with similar skills

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V2 Perf Iteration compared with similar skills
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Session Profilertamdogood/builder-essential-skills220—~1.5kAutomated safety check: PassMIT
Tracelens Analysis Orchestratoramd/skills406—~760Automated safety check: PassMIT
O2 Review Loopopenobserve/openobserve22k—~3.7kAutomated safety check: PassAGPL-3.0
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Questions about V2 Perf Iteration

What does V2 Perf Iteration do?

Runtime-V2 performance-iteration workflow. An agent skill from mirage-project/mirage. V2 Perf Iteration is an agent skill from mirage-project/mirage. Runtime-V2 performance-iteration workflow.

When should I use V2 Perf Iteration?

V2 Perf Iteration fits situations like: running a perf-optimization campaign; iteration on the v2 runtime (--use-v; — measuring a baseline; ranking bottlenecks.

How do I install V2 Perf Iteration in Claude Code?

Run `npx skills add mirage-project/mirage --skill v2-perf-iteration -a claude-code`. Or copy the skill folder (.claude/skills/v2-perf-iteration in mirage-project/mirage) into .claude/skills/v2-perf-iteration in your project. Claude Code loads it when a task matches its description.

How do I install V2 Perf Iteration in Codex?

Run `npx skills add mirage-project/mirage --skill v2-perf-iteration -a codex`. Or copy the skill folder (.claude/skills/v2-perf-iteration in mirage-project/mirage) into .agents/skills/v2-perf-iteration in your project. Codex loads it when a task matches its description.

Can I use V2 Perf Iteration in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mirage-project/mirage --skill v2-perf-iteration -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/v2-perf-iteration, .gemini/skills/v2-perf-iteration, .github/skills/v2-perf-iteration and .opencode/skills/v2-perf-iteration in your project.

What does V2 Perf Iteration need to run?

Going by SKILL.md and its folder, V2 Perf Iteration needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does V2 Perf Iteration access the network?

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.

Is V2 Perf Iteration safe to install?

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. Review the folder before installing.

What licence does V2 Perf Iteration use?

V2 Perf Iteration is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does V2 Perf Iteration use?

About 4k tokens (SKILL.md is roughly 16k 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 1.9k tokens, read only when the agent opens those files.

What are the alternatives to V2 Perf Iteration?

Skills that share tags, products or a category with V2 Perf Iteration: Perf Loop (ozontech/seq-db, 133 stars), Session Profiler (tamdogood/builder-essential-skills, 220 stars), Tracelens Analysis Orchestrator (amd/skills, 406 stars) and O2 Review Loop (openobserve/openobserve, 22k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains V2 Perf Iteration?

mirage-project (a GitHub organization) maintains it in mirage-project/mirage, which has 2,543 GitHub stars. The repository holds 24 skills in this directory. The repository was last updated on October 7, 2026.

Source: mirage-project/mirage on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.