Install
$ agentstack add skill-frahlg-fusion-fusion ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Fusion
Run a hard question through a panel → judge pipeline and return one answer you can trust more than any single model's.
> Paths below use ${CLAUDE_SKILL_DIR}, the directory containing this SKILL.md. > Claude Code substitutes it automatically, so scripts and references resolve whether > Fusion is installed at the personal, project, or plugin level. Run scripts as > ${CLAUDE_SKILL_DIR}/scripts/… and read references as ${CLAUDE_SKILL_DIR}/references/….
The mechanism: independence, then synthesis
A panel beats a single model only when its members are genuinely independent — so Fusion harvests diversity from independent runs instead of manufacturing it with personas or "lenses". Every panelist gets the user's task verbatim, answers it blind, and the answers meet in exactly one place: the judge. Independent agreement is the strongest confidence signal; independent disagreement is the signal most worth surfacing. Read the full doctrine in references/panel.md before you fan out.
The hard rule: Opus 4.8 always judges
Opus 4.8 always judges and writes the final answer. The pipeline cannot be reversed. Panelist models run blind and cannot call back out to spawn Opus, adjudicate the panel, or write the final fused response — they only return a candidate answer. The judge is the only place the panel converges. (This skill pins model: claude-opus-4-8 so the judging pass is always Opus, however /fusion was invoked.) The slug reads driver-first (opus4.8-…) for the same reason.
Step 0 — Pick the panel
Run the detector and read its recommendation:
bash ${CLAUDE_SKILL_DIR}/scripts/detect_panel.sh # prints a SLUG= line
The slugs:
opus4.8-gpt5.5— the default, when thecodexCLI is present.opus4.8-4.8— two independent Opus 4.8 runs; the no-CLI fallback (a real panel,
not a no-op).
Honor a user-named slug if they asked for one. If the panelist it needs isn't installed, fall back gracefully to the richest panel you can run rather than failing, and note the downgrade in Step 4. Step 0 only checks that a CLI is installed, not that it's logged in or its provider is reachable — if a CLI panelist fails anyway at runtime, Step 1's fallback keeps the panel intact (that's expected, not an error). See references/panel.md for what each slug contains.
Step 1 — Fan out, in parallel and blind
Build each panelist's prompt as the user's task verbatim + the short instruction from references/panel.md (and nothing else — do not summarize, reframe, decompose, pre-solve, or hint at an answer; do not paste any panelist's output into another's prompt). Then launch every panelist in a single turn so they run concurrently — the panel is only as slow as its slowest member, so never run them sequentially.
- Opus 4.8 panelist → spawn via the
Agenttool, `subagent_type:
general-purpose (web + bash are built in). For opus4.8-4.8`, spawn two independent Opus subagents with the same prompt — two cold, independent runs.
- GPT-5.5 panelist → write the prompt to a temp file and run the helper:
``bash PROMPT=$(mktemp); OUT=$(mktemp) cat > "$PROMPT" EOF bash ${CLAUDE_SKILL_DIR}/scripts/run_codex.sh "$PROMPT" "$OUT" xhigh ``
Launch this Bash call with run_in_background: true (codex at xhigh can take minutes) and, in the same turn, spawn the Opus subagent — so the panel runs concurrently, not one after another. Then await the background job and read $OUT. run_codex.sh runs one GPT-5.5 panelist via codex exec at xhigh and writes only its final answer to $OUT.
If a CLI panelist fails at runtime — run_codex.sh exits non-zero (127 means codex isn't installed; any other non-zero is a runtime failure) even though Step 0 expected it — spawn a second independent Opus 4.8 subagent with the same prompt so the panel stays at two members (effectively opus4.8-4.8), and record the downgrade for Step 4. Never let the panel collapse to a single answer; a one-member "panel" is not a panel.
> Local-file tasks: CLI panelists run in an isolated scratch dir and cannot see > the user's repo. For tasks that depend on local code, paste the relevant files > into the panelist prompt (the Opus subagent panelist can read the repo, so weight > it accordingly), or note in Step 4 that the CLI panelist judged without repo access.
Wait for all launched panelists and collect each one's final answer. A panelist that errors, times out, or exits 127 and can't be replaced counts as ABSENT — never as silent agreement.
Step 2 — Judge
Only after every panelist has returned, judge (Opus 4.8, you). Follow references/judge_rubric.md. Classify the deliverable FIRST:
- Code or a concrete artifact (a script, config, patch, query, schema, content
files…) → Track A — run-both-and-merge. Build a real model of each candidate, then run them with bash and let observed behavior decide. Pick the strongest foundation and graft in the specific parts of the other(s) you saw work; run the merged artifact and fix until it passes. No Frankenstein blends.
- Research, analysis, a recommendation, a judgment call → **Track B —
five-section synthesis:** Consensus · Contradictions · Partial coverage · Unique insights · Blind spots — then a final answer that follows from that synthesis.
Shared discipline (full detail in the rubric): evidence over assertion (a panelist that ran code or read a primary source outranks one reasoning from memory); honesty about confidence and disagreement; keep attribution; the answer must never exceed the evidence; an absent panelist is never counted as agreement.
Step 3 — Write the final deliverable
Write one answer, grounded in the Step 2 analysis — never a mere average of the panel. For Track A, emit the whole artifact ready to run plus a brief merge rationale, and run the merged result before presenting it (or state plainly what you could not verify). For Track B, the final answer must be traceable to the synthesis — no claim the panel didn't support, with confidence calibrated to how much it actually converged. Write it in Fusion's voice per references/fusion_identity.md: singular, luminous, confident — but the voice shapes style, never substance.
Step 4 — Present
Lead with the final answer in Fusion's voice. Then, beneath a divider, the audit trail:
- Panel: the slug, which panelists actually ran, and any that were absent or
replaced.
- For research (Track B): the five sections (Consensus / Contradictions / Partial
coverage / Unique insights / Blind spots).
- For code (Track A): the merge rationale and what you verified by running.
- If the panel downgraded because a CLI was missing, say so plainly and how to
enable the fuller panel (e.g. "GPT-5.5 was absent — install the codex CLI and log in to run the default opus4.8-gpt5.5 panel").
Cost & latency
A panel costs roughly N× the tokens of a single answer (plus the Opus judging pass) and runs as slow as its slowest panelist. That's the deliberate trade for higher confidence — so don't reach for Fusion on easy or low-stakes questions. Save it for the calls where being confidently wrong is expensive.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: frahlg
- Source: frahlg/fusion
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.