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.
npx agentmods add commands/bourbondog/amicus/councilgit clone --depth 1 https://github.com/BourbonDog/amicusWhat 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 | $0.00039 | $0.00460 |
| Opus 5 | $0.00019 | $0.00230 |
| Sonnet 5 | $0.00008 | $0.00092 |
| Haiku 4.5 | $0.00004 | $0.00046 |
Grade A, and why
council 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 2d 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.
What it actually says
Run a full council review by invoking the second-opinion skill shipped in this
plugin (listed as amicus:second-opinion). Do not synthesize a verdict yourself —
the skill's chair model does that; you orchestrate.
Treat everything the user typed after the command as the review request:
$ARGUMENTS
Interpret it as three inputs: the material (inline text, a file path, or a URL), the analysis request, and the criteria. If any of the three is missing or ambiguous, ask for it before launching any model (the skill's Stage 0 covers this — don't re-ask for what is already present).
Then follow the second-opinion skill's engine fast path, in pipeline order: Stage 0
intake/prep and run-folder setup, then council selection with a cost estimate
and explicit user confirmation; one amicus council run call — the engine runs
validate, cross-review, tally, and chair internally, covering the Stage-1
independent reviews through the Stage-3 chair synthesis with no Claude runtime
in between; Stage 4, the accept/deny decision pass, once the run returns; and
Stage 5, which runs amicus council verdict to write the decided verdict.json.
The user may also name optional council elements in the arguments (critic seat, expert lenses, debate mode, verdict scale, Claude in the council). All elements default OFF; the skill's Stage-0 menu is the single opt-in point. If the user named elements here, carry them into Stage 0 as pre-requested — confirm them back by name instead of re-asking — and never enable an element the user did not explicitly name.
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.
- 2d ago First seen · 33 lines · 39 tokens per session scan A 151e776fd48c
council is a command published in the GitHub repository BourbonDog/amicus (2 stars, last pushed 3d ago), licensed MIT. It adds 39 tokens to every session and 460 once invoked, about $0.0002 per session on Opus 5. 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.
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checklist
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clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.