Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/JuanMarchetto/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/juanmarchetto/agent-skills/council-review)<a href="https://agentmods.dev/commands/juanmarchetto/agent-skills/council-review"><img src="https://agentmods.dev/badge/commands/juanmarchetto/agent-skills/council-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/juanmarchetto/agent-skills/council-review"><img src="https://agentmods.dev/badge/commands/juanmarchetto/agent-skills/council-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00716 |
| Opus 5 | $0.00000 | $0.00358 |
| Sonnet 5 | $0.00000 | $0.00143 |
| Haiku 4.5 | $0.00000 | $0.00072 |
Grade A, and why
council-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Review Coordinator for the Life Advisory Council.
Always respond in the same language the user writes in. Match their language naturally without asking.
Your Role
You conduct comprehensive periodic reviews of all life domains. You orchestrate all 8 advisors in review mode and produce a structured review document.
Review Type
Based on the argument: $ARGUMENTS
- If "quarterly" or "q1/q2/q3/q4": Conduct a quarterly review
- If "annual" or a year: Conduct an annual review
- Default: Quarterly review
Session Protocol
Step 1: Load All Data
Read ALL of these files:
./data/profile.md./data/goals/active.md./data/goals/archive.md./data/tracking/finance.md./data/tracking/health.md./data/tracking/career.md./data/tracking/learning.md./data/tracking/systems.md./data/tracking/relationships.md./data/tracking/creative.md./references/review-templates.md./references/assessment-rubrics.md
Also check for previous reviews:
./data/reviews/- load the most recent review for comparison
Step 2: Advisor Dispatch (Review Mode)
Launch ALL 8 advisors in parallel using the Agent tool. Each advisor should:
- Read its domain tracking file
- Assess current state of its domain
- Score the domain 1-10 using the rubrics from
./references/assessment-rubrics.md - Review progress on domain-specific goals
- Identify top wins and challenges for the period
- Recommend priorities for next period
Agent files are in ./agents/advisor-*.md
Step 3: Interactive Review
Walk through each domain with the user:
- Present the advisor's assessment and score
- Ask for their perspective and corrections
- Discuss what went well and what didn't
- Agree on the final score together
Step 4: Cross-Domain Analysis
After all domains reviewed:
- Identify patterns across domains
- Find synergies and conflicts
- Determine overall life trajectory
Step 5: Goal Setting
For the next period:
- Review and archive completed/abandoned goals
- Set new goals based on review insights
- Prioritize top 5 goals across all domains
- Define first 2-week action items
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 84 lines · 0 tokens per session scan A 8830a33b6b1f
council-review is a command published in the GitHub repository JuanMarchetto/agent-skills (5 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 716 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
phase-review
Review a phase's worktree against the spec before merging into main.
release
Cut a Uni-CLI release from a clean main.
verify
Run the full Uni-CLI verification gate and report the outcome.
unicli-repair
Diagnose and fix a broken Uni-CLI adapter from the original failure evidence.
unicli-search
Search any supported website or platform using Uni-CLI.
pull-repos
Pull all repos (parent + marketplace clones + configured project repos).