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 skills add SteveGJones/ai-first-sdlc-practices --skill commissiongit clone --depth 1 https://github.com/SteveGJones/ai-first-sdlc-practicesWrote 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/skills/stevegjones/ai-first-sdlc-practices/commission)<a href="https://agentmods.dev/skills/stevegjones/ai-first-sdlc-practices/commission"><img src="https://agentmods.dev/badge/skills/stevegjones/ai-first-sdlc-practices/commission/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/skills/stevegjones/ai-first-sdlc-practices/commission"><img src="https://agentmods.dev/badge/skills/stevegjones/ai-first-sdlc-practices/commission.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00049 | $0.01219 |
| Opus 5 | $0.00024 | $0.00609 |
| Sonnet 5 | $0.00010 | $0.00244 |
| Haiku 4.5 | $0.00005 | $0.00122 |
Grade A, and why
commission scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Commission an SDLC Option Bundle
Walk a project through commissioning to one of four SDLC options: solo (1-2 people, fast iteration), single-team (3-10, current default), programme (11-50, formal phase gates), assured (regulated industries with traceability).
Pre-flight
PROJECT_DIR=$(pwd)
TEAM_CONFIG="$PROJECT_DIR/.sdlc/team-config.json"
# Check if already commissioned
python3 -c "
from pathlib import Path
from sdlc_core_scripts.commission.recorder import is_commissioned
print('COMMISSIONED' if is_commissioned(Path('$TEAM_CONFIG')) else 'FRESH')
"
If COMMISSIONED, ask the user to confirm re-commissioning before
proceeding. Show the existing record:
python3 -c "
from pathlib import Path
from sdlc_core_scripts.commission.recorder import read_record
r = read_record(Path('$TEAM_CONFIG'))
print(f' Current option: {r.sdlc_option}')
print(f' Current level: {r.sdlc_level}')
print(f' Bundle version: {r.option_bundle_version}')
print(f' Commissioned: {r.commissioned_at} by {r.commissioned_by}')
"
Questions (ask one at a time, brief)
Skip any question whose answer is in arguments (--option, --level).
- Team size — 1-2 / 3-10 / 11-50 / 50+
- Blast radius of a defect — low / moderate / high / severe
- Regulatory burden — none / low / moderate / high
- Specification maturity — emergent / mixed / contract-first / formal
- Time-to-market pressure — very high / high / moderate / low
If user has set arguments overriding all of these, skip directly to recommendation.
Recommendation logic
team-size 1-2 + low blast radius → solo / prototype
team-size 3-10 + moderate blast radius → single-team / production
team-size 11-50 + formal spec → programme / production
any size + high regulatory burden → assured / enterprise
If two recommendations tie, prefer the simpler one (solo > single-team > programme > assured).
Show the recommendation with rationale (which questions drove it).
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 148 lines · 49 tokens per session scan A 8ff98da94e1c
commission is a skill published in the GitHub repository SteveGJones/ai-first-sdlc-practices (41 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 1,219 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 2026-08-30.
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