fnl-advisor
Skill4 starsChanged 14 days agoMITView on GitHub
Get one second opinion or convene two contrasting model families for root-cause analysis and a plan. Use when the user asks for an advisor, a second opinion or what another model thinks, before committing to a risky design or fix, or when stuck, looping on failed fixes, or facing a hard planning or design problem.
1 other project uses a version of this.
Adopted by
Most recently taken up by
- finolabs/vibecodetheir own version, first seen Nov 15, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- finolabs/vibecode★ 4same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository19 other files it gives its agents.
- rules/AGENTS.mdAGENTS.md
- skills/fnl-delegate/references/agents.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- fnl-askSkill
- fnl-coding-routerSkill
- fnl-commit-messagesSkill
- fnl-delegateSkill
- fnl-handoffSkill
- fnl-open-source-reviewSkill
- fnl-plugin-packagingSkill
- fnl-restore-pointSkill
- fnl-review-gateSkill
- fnl-security-review-gateSkill
- fnl-session-hygieneSkill
- fnl-setupSkill
- fnl-statusSkill
- fnl-updateSkill
- fnl-verificationSkill
- fnl-versioningSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 39
If the arguments reference another skill (/name or $name), tell the advisor: "Invoke the <name> skill against this task before anything …
≈ “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
Advisor
For a committee of two contrasting families, load references/committee.md and follow that procedure instead. Load it only for a committee: stuck or looping work, or a very hard planning or design problem. The procedure below is for one second opinion.
One agent reads the situation and gives a judgement. You stay the decider; the advisor does not drive the work.
User's request: $ARGUMENTS
When to run
Propose an advisor and wait for the user's yes, because each run spends quota. Propose it when the decision cannot be undone or changes a contract, or when your own analysis leaves you unsure. A user who asks for one needs no proposal. When the user is away, run one without the yes when a decision would otherwise wait for them, as fnl-delegate says for an absent user, and record who settled it.
Advisory (agent judgment): Talk to the user in the language they are using, including questions and option labels; keep command names, flags, file paths, skill names and other technical names unchanged.
Picking the advisor
- An agent the user names wins.
- Otherwise prefer contrast: a model family other than the one you run on (
fnl-delegatereferences/models.md). Three families are on offer: Claude (Claude Code), GPT (Codex) and Gemini (Antigravity Argon). Running on Claude, ask Codex or Argon; running on Codex, ask Claude Code or Argon; running on Antigravity, ask Claude Code or Codex. All read-only. The tier follows the question's difficulty: strong for an easy or medium question (a mid-priced model is enough), frontier for a hard or very hard one. - When one family is out of quota or unreachable, try the other family, and each of its models. Use a subagent of your own host with fresh context only when every model of both other families is out of quota or unreachable, and say that the opinion comes from the same family.
Load the fnl-delegate skill and the host config for the invocation commands and read-only flags. Requires the fnl-delegate skill installed beside this one. When it is missing, tell the user to add it (npx skills add finolabs/finolabs-vibecode --skill fnl-delegate, or install the whole plugin) and stop.
The briefing
The advisor has zero context:
- the question, sharply;
- what you have considered and ruled out;
- relevant files by path, for the advisor to read itself;
- the ask: "Give me a recommendation, with reasoning and the evidence you used."
If the arguments reference another skill (/name or $name), tell the advisor: "Invoke the <name> skill against this task before anything else," and pass the remaining arguments as that skill's input.
End every briefing with:
This is analysis only. Do NOT edit, create, or delete any files. Do NOT write code.Run and synthesize
Dispatch with a budget and hard cap from fnl-delegate "Budget and liveness" and record it in the ledger with --kind advise and the question's difficulty as --grade. Read the answer, check its claims against the code where they matter, then give the user the advisor's verdict and your own recommendation, saying where you agree and where you differ. Where you differ on a point that changes direction, give the user both arguments and let them decide. When the user is away, take the option that can be undone, record who settled it, and hold the part of the work that depends on a point you cannot undo until the user answers; never settle it by weighing the two opinions.
For ongoing input, send follow-ups to the same advisor: a session or a resumable run keeps its context; a one-shot headless run gets a new prompt that carries the earlier question and answer. Start fresh when the topic shifts.
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.

