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.
/plugin marketplace add SteveGJones/ai-first-sdlc-practices/plugin install sdlc-model-councilWrote 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/stevegjones/ai-first-sdlc-practices/council-roster)<a href="https://agentmods.dev/commands/stevegjones/ai-first-sdlc-practices/council-roster"><img src="https://agentmods.dev/badge/commands/stevegjones/ai-first-sdlc-practices/council-roster/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/stevegjones/ai-first-sdlc-practices/council-roster"><img src="https://agentmods.dev/badge/commands/stevegjones/ai-first-sdlc-practices/council-roster.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 | $0.00028 | $0.00910 |
| Opus 5 | $0.00014 | $0.00455 |
| Sonnet 5 | $0.00006 | $0.00182 |
| Haiku 4.5 | $0.00003 | $0.00091 |
Grade A, and why
council-roster 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.
What it actually says
Show the project's current model-council roster. This is a read of
.sdlc/model-council/, not a new assessment — no adapter is called, no
tokens are spent, unless rebuild is given (still token-free, see step 3).
Arguments: $ARGUMENTS — optional literal rebuild.
Do this:
-
No roster yet? If
.sdlc/model-council/roster.jsondoesn't exist, say so plainly and stop: point at/sdlc-model-council:council-commission(full onboarding: discover, characterize, audition, roster, policy) or/sdlc-model-council:council-assess(just the assessment) as the two ways to produce one. Don't fabricate a roster from priors here — that's what commission'sskipaudition choice is for. -
Roster exists — present it. Show
.sdlc/model-council/roster.mdas-is (it already has the per-model table: grades, per-dimension posterior/n/ci95, roles, cost/item, p50 latency). Then read.sdlc/model-council/diversity.jsonand summarise itspairsarray:- Most decorrelated pair — lowest
both_wrong_rateamong pairs whereinsufficientis false — this is the best casting complement. - Most correlated pair — highest
both_wrong_rate(or any pair markedinsufficient, whichcast.pytreats as maximally correlated by design — call these out separately, they're a data gap not a diversity finding). Reference thecouncil-policyskill if the user asks what any of this means for routing — role assignment and casting rules live there, not in this command.
- Most decorrelated pair — lowest
-
rebuild— re-derive the roster and diversity map from the existing.sdlc/model-council/results.jsonlwithout dispatching any model. This is for after a manual edit toresults.jsonl, a pricing refresh, or a priors update — cheap and token-free because it only re-scores rows already on disk. Run:${CLAUDE_PLUGIN_ROOT}/scripts/council/roster.py \ --results .sdlc/model-council/results.jsonl \ --priors-dir ${CLAUDE_PLUGIN_ROOT}/scripts/council/priors \ --pricing ${CLAUDE_PLUGIN_ROOT}/scripts/council/pricing.json \ --out-json .sdlc/model-council/roster.json \ --out-md .sdlc/model-council/roster.md ${CLAUDE_PLUGIN_ROOT}/scripts/council/diversity.py \ --results .sdlc/model-council/results.jsonl \ --stack ${CLAUDE_PLUGIN_ROOT}/assessment/stack/v1/stack.json \ --out .sdlc/model-council/diversity.jsonThen present the rebuilt roster per step 2. If
.sdlc/model-council/results.jsonldoesn't exist either, there's nothing to rebuild from — say so and point atcouncil-assess/council-commissioninstead. -
Staleness check. Compare the roster's
generated_ts(.sdlc/model-council/roster.json) against the timestamps in.sdlc/model-council/assessment-log.jsonl— if any log entry'stsis newer thanroster.json'sgenerated_ts, the on-disk roster predates the latest assessment run. Flag this plainly and suggestrebuild(ifresults.jsonlalready reflects the newer run) or a fresh/sdlc-model-council:council-assess(if it doesn't).
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.
- 10d ago First seen · 66 lines · 28 tokens per session scan A fb62708e94aa
council-roster 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 910 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
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.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.