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 jmagly/aiwg --skill project-awarenessgit clone --depth 1 https://github.com/jmagly/aiwgWrote 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/jmagly/aiwg/project-awareness)<a href="https://agentmods.dev/skills/jmagly/aiwg/project-awareness"><img src="https://agentmods.dev/badge/skills/jmagly/aiwg/project-awareness/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/jmagly/aiwg/project-awareness"><img src="https://agentmods.dev/badge/skills/jmagly/aiwg/project-awareness.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 | $0.00025 | $0.02267 |
| Opus 5.5 | $0.00010 | $0.00907 |
| Sonnet 5.5 | $0.00005 | $0.00453 |
| Haiku 4.5 | $0.00003 | $0.00227 |
Grade A, and why
project-awareness 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 1mo 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 — 364 lines — stays where its author put it; the contents beside it link to each section on GitHub.
project-awareness
Comprehensive project context detection and state awareness.
Triggers
Alternate expressions and non-obvious activations (primary phrases are matched automatically from the skill description):
- "where are we" / "what's next" → project state summary
- "what's blocking" in project context → status + blockers
- "sprint status" → iteration awareness
Purpose
This skill provides rich project context awareness including:
- Project type and technology stack detection
- AIWG framework state (installed frameworks, current phase)
- Team configuration and agent assignments
- Recent activity and artifact status
- Active work detection (branches, PRs, iterations)
- Recommendations for next actions
Behavior
When triggered, this skill:
-
Detects project type:
- Monorepo vs single project
- Library vs application vs service
- Web, API, CLI, mobile, etc.
- Programming languages and frameworks
-
Identifies AIWG state:
- Installed frameworks (SDLC, MMK, addons)
- Current lifecycle phase
- Active iteration (if applicable)
- Deployed agents and commands
-
Parses team configuration:
- Team roster from
.aiwg/team/ - Agent assignments
- Role responsibilities
- Team roster from
-
Loads recent activity:
- Git log (recent commits, active branches)
- Recent artifact changes
- Open PRs and issues
-
Builds context object:
- Structured data for other skills
- Summary for user display
- Recommendations for next actions
Trigger Phrase Mappings
| Natural Language | Action |
|---|---|
| "Where are we?" | Check phase status, recent activity |
| "What's next?" | Identify pending tasks, next milestone |
| "Project status" | Full status report |
| "Current phase" | Phase name + completion percentage |
| "Ready to transition?" | Gate criteria check |
| "What's blocking us?" | Risk register + blockers |
| "How long until..." | Milestone progress estimate |
| "Who owns..." | Team and agent assignments |
What ships with it
3 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.
- 1mo ago First seen · 364 lines · 25 tokens per session scan A 54902c2c893c
project-awareness is a skill published in the GitHub repository jmagly/aiwg (225 stars, last pushed today), licensed MIT. It adds 25 tokens to every session and 2,267 once invoked, about $0.0001 per session on Opus 5.5. 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 skills, from other repositories
launch
Planning releases and reporting delivery work from GitHub PR history. Use when versioning, CHANGELOGs, rollout or rollback plans, engineering metrics, retrospectives, or stakeholder reports are needed.
pdm
Navigating delivery status read-only: reconciles planned scope (specs/roadmap/PRD) against implemented code for what's built vs left. Not for priority scoring (Rank) or AC conformance (Attest).
sherpa
Guiding workflows by decomposing complex tasks (Epics) into Atomic Steps under 15 minutes each, with progress tracking and drift prevention. Use when complex decomposition is needed.
rank
Quantifying priority by scoring competing items with ICE/RICE/WSJF/MoSCoW/Cost of Delay/Kano. No code. Use to prioritize features/bugs/initiatives or arbitrate Must vs Should at MVP scoping.
spark
Proposing new features leveraging existing data/logic as Markdown specifications. Use when brainstorming new features, product planning, or feature proposals are needed. Does not write code.
missive
Invoke only when spawning a quest from a GitHub issue reference. Fetches GitHub issue context for quest spawning. Parses issue references, retrieves structured data via gh, and produces branch suggestions and PR keywords. Used standalone or as input to quest orchestration.