council-assess

council-assess is a command for Claude Code from SteveGJones/ai-first-sdlc-practices. It costs 28 tokens per session (1,307 once invoked), scanned A, original, MIT.

A command that gives the same set of test problems to several AI models, scores their answers, and creates a comparison of their strengths.

In plain words
What is it for?
Use it to audition models, compare them across chosen dimensions, and build a roster for later multi-model work.
Why use it?
It helps you make an initial, budget-limited choice among models before assigning one an important role.

Command for Claude Code

Written for Claude Code: argument-hint in frontmatter. Also seen: positional $N argument; mentions OpenCode.

Runs only inside its plugin — its command needs a path that Claude Code sets for a plugin’s own hooks and for nothing else. Install the plugin, not this.

Part of the sdlc-model-council plugin — 1 skill, 7 commands, 2 agents shipped together

Good fit Use it to audition models, compare them across chosen dimensions, and build a roster for later multi-model work.

Compare 6 commands from other repositories ↓
Install

Getting it into your agent

This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.

Claude Code
/plugin marketplace add SteveGJones/ai-first-sdlc-practices
Claude Code
/plugin install sdlc-model-council

Made for: Claude Code.

Or install sdlc-model-council, the plugin that ships this one along with the rest of its 1 skill, 7 commands, 2 agents.

Wrote 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.

agentmods badge for council-assess

README.md
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Your own site
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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.

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Your own site · 80×15
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Per session 28 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,307 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00028 $0.01307
Opus 5 $0.00014 $0.00654
Sonnet 5 $0.00006 $0.00261
Haiku 4.5 $0.00003 $0.00131

Measured 10d ago against content hash bfa4b485b29a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-13, from the pricing page.

Security

Grade A, and why

council-assess 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 10d 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.

plugins/sdlc-model-council/commands/council-assess.md · 95 lines

How it starts

The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Run (or resume) a council assessment: the shipped problem stack goes to each named model, scored, and turned into a roster card + diversity map. This wraps assess.sh — the choreography, not a new engine.

This is the shallow, broad instrument — one of two. The v1 item stack run here is cheap and ranks models quickly, which makes it right for auditioning a newly-released model or taking a first cut. It does not tell you where a model breaks on sustained multi-stage work. For that, the second instrument is the poker-capstone capability ladder (research/poker-capstone/): P1-P10 in difficulty order over one substantial engineering problem, run fail-fast so a model's assessment stops at its first genuine failure. Use this command to narrow the field; use the ladder before granting a model a seat that matters. Results to date, and what each phase measures, are tabulated in that directory's README.

Arguments: $ARGUMENTS — parse models= (comma-separated model addresses, required), dims= (comma-separated dimensions, required), optional budget-usd= (a dollar cap — see step 2), --estimate (stop after the estimate, spend nothing), --resume run-dir=DIR (continue an earlier run, re-running only missing/skipped:budget pairs).

Do this:

  1. It always estimates first, token-free. Run:

    ${CLAUDE_PLUGIN_ROOT}/scripts/council/assess.sh \
      --stack ${CLAUDE_PLUGIN_ROOT}/assessment/stack/v1/stack.json \
      --priors-dir ${CLAUDE_PLUGIN_ROOT}/scripts/council/priors \
      --pricing ${CLAUDE_PLUGIN_ROOT}/scripts/council/pricing.json \
      --models <models> --dims <dims> \
      [--budget-usd <budget-usd>] [--estimate] \
      [--resume --run-dir <run-dir>]
    

    assess.sh prints the per-model estimate table before touching any adapter. Echo that table to the user before anything paid happens — this is the never-surprise-spend contract, not optional politeness.

  2. --estimate stops there. If the caller passed --estimate, the script exits after the estimate table with no tokens spent — report the table and total, and stop; do not proceed to the steps below.

  3. A live run over any paid model requires budget-usd=. If the fleet named in models= includes anything outside the free family (opencode-free-tier) and no budget-usd= was given, assess.sh itself refuses (fleet contains paid models; a live run requires --budget-usd) — don't try to work around that by inventing a number; tell the user the command needs budget-usd= and stop. Free-only fleets run at $0 with no budget flag needed.

  4. Free calibration runs first, automatically. Wave-0 dispatches only free models over the full item set (cost $0) to validate items/scorers and refine the paid-wave estimate before any paid token is spent — this is inside assess.sh, nothing to do here except let it run.

  5. Paid waves run at ≤5 concurrent handles, hard-stopping the instant spend reaches budget-usd; anything still pending at that point is marked skipped:budget and is safe to pick up later with --resume.

  6. On completion, assess.sh prints [assess] Council spend: $X (est $Y) across N models as its last log line, followed by the run directory path. The run dir now contains results.jsonl, roster.json, roster.md, diversity.json, and assessment-log.jsonl.

  7. Promote the run's roster to the project roster. Copy the three artifacts from the run dir into .sdlc/model-council/ (create the directory if it doesn't exist):

    mkdir -p .sdlc/model-council
    cp <run-dir>/roster.json .sdlc/model-council/roster.json
    cp <run-dir>/roster.md .sdlc/model-council/roster.md
    cp <run-dir>/diversity.json .sdlc/model-council/diversity.json
    

    Also append the run dir's assessment-log.jsonl line(s) onto .sdlc/model-council/assessment-log.jsonl (create if absent) — this is the greppable spend history, not just the latest snapshot.

Read the full file on GitHub · 95 lines

Changes

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.

  1. 10d ago First seen · 95 lines · 28 tokens per session scan A bfa4b485b29a

Subscribe to this mod's changes

council-assess is a command published in the GitHub repository SteveGJones/ai-first-sdlc-practices (41 stars, last pushed 1mo ago), licensed MIT. It adds 28 tokens to every session and 1,307 once invoked, about $0.0001 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 2026-09-03.