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/qwickapps/ai-sdlc-workflows/docsgit clone --depth 1 https://github.com/qwickapps/ai-sdlc-workflowsWhat 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 | $0.00003 | $0.00520 |
| Opus 5 | $0.00002 | $0.00260 |
| Sonnet 5 | $0.00001 | $0.00104 |
| Haiku 4.5 | $0.00000 | $0.00052 |
Grade A, and why
docs 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 yesterday.
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
Documentation Update Workflow
You are now in Documentation Mode. Use this when updating documentation only.
Input: $ARGUMENTS (what changed or needs documentation)
CRITICAL RULES
- Accurate and current - docs must reflect actual code state
- NEVER auto-commit - wait for user approval
- No attributions in commits
Interactive Setup
Check if $ARGUMENTS is provided.
If $ARGUMENTS is empty, ask:
"What documentation needs updating? Please describe:
- What code or feature changed
- Which docs might be affected
- Any specific sections to update"
Wait for response before proceeding.
Documentation Checklist
README.md
Update when:
- Installation steps changed
- Usage examples changed
- Configuration options changed
- Dependencies changed
CHANGELOG.md
Update when:
- New feature added
- Bug fixed
- Breaking change made
- Any releasable change
Format:
## [Unreleased]
### Added
- {new feature}
### Changed
- {changed behavior}
### Fixed
- {bug fix}
### Removed
- {removed feature}
ARCHITECTURE.md
Update when:
- System structure changed
- New components added
- Data flow changed
- Integration points changed
Code Comments
Add/update when:
- Complex logic needs explanation
- Non-obvious decisions made
- API contracts defined
Process
Step 1: Identify What Needs Updating
- What code changed?
- What docs reference that code?
- Are any docs now outdated?
Step 2: Make Updates
- Update each affected doc
- Keep changes minimal and focused
- Maintain existing style/format
Step 3: Review
Present changes to user:
## Documentation Updates
### Files Changed
- {file}: {what changed}
### Summary
{brief description}
Step 4: Commit (after approval)
docs(<scope>): update documentation
- {what was updated}
- {why}
Abort
User can say "abort" or "cancel" at any time.
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.
- yesterday First seen · 134 lines · 3 tokens per session scan A 17b29ad38f17
docs is a command published in the GitHub repository qwickapps/ai-sdlc-workflows (2 stars, last pushed 5mo ago), licensed MIT. It adds 3 tokens to every session and 520 once invoked, about $0.0000 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.
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.