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 strikersam/autonomous-ai-agency --skill agentic-agilegit clone --depth 1 https://github.com/strikersam/autonomous-ai-agencyWrote 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/strikersam/autonomous-ai-agency/agentic-agile)<a href="https://agentmods.dev/skills/strikersam/autonomous-ai-agency/agentic-agile"><img src="https://agentmods.dev/badge/skills/strikersam/autonomous-ai-agency/agentic-agile/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/strikersam/autonomous-ai-agency/agentic-agile"><img src="https://agentmods.dev/badge/skills/strikersam/autonomous-ai-agency/agentic-agile.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Agent Snooping · line 54 Skill reads from agent configuration directories (.claude/, .codex/, .gemini/). These directories may contain API keys, personal settings, and other credentials that the skill has no legitimate need to access.Fix: Remove all code or instructions that access agent configuration directories (.claude/, .codex/, .gemini/). If configuration values are needed, pass them explicitly as parameters or environment variabl
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 | $0.00023 | $0.00861 |
| Opus 5 | $0.00012 | $0.00430 |
| Sonnet 5 | $0.00005 | $0.00172 |
| Haiku 4.5 | $0.00002 | $0.00086 |
Grade A, and why
agentic-agile 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 8d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Agentic Agile
Purpose
Agile sprint management (agents/agile_sprints.py) with velocity tracking,
burndown metrics, and multi-sprint orchestration.
Usage
from agents.agile_sprints import AgileManager, AgileSprint, UserStory
mgr = AgileManager()
sprint = mgr.create_sprint("Sprint 5", goal="Ship MVP")
sprint.add_story(UserStory(story_id="s1", title="Login", story_points=5))
sprint.add_story(UserStory(story_id="s2", title="Dashboard", story_points=8))
sprint.start(duration_days=14)
metrics = sprint.get_metrics()
print(f"On track: {metrics.is_on_track}")
Key Classes
- UserStory — story points, status, assignee
- SprintMetrics — velocity, burndown rate, completion percentage, track prediction, health signal
- SprintHealth — ON_TRACK / AT_RISK / OFF_TRACK / COMPLETE
- Retrospective — went_well / went_poorly / action_items
- AgileSprint — story management, start/complete/cancel, metrics, scope_added (creep), retrospective helpers
- AgileManager — multi-sprint registry, velocity prediction
Retrospective & health
m = sprint.get_metrics()
print(m.health) # SprintHealth.AT_RISK
print(sprint.scope_added) # points added since start()
sprint.add_retro_note(went_well="Good pairing", went_poorly="Flaky CI")
sprint.add_action_item("Stabilise CI")
Autonomous ceremonies (agents/agile_ceremonies.py)
Builds standups, retros, and sprint plans straight from
.claude/state/active-tasks.md and the WSJF portfolio — no human input needed.
from agents.agile_ceremonies import (
generate_standup, generate_sprint_retro, generate_backlog_retro,
plan_next_sprint, retrospective_to_markdown,
)
tasks_md = open(".claude/state/active-tasks.md").read()
# Daily standup: Completed / In progress / Planned / Blockers + active sprint health
report = generate_standup(tasks_md, agile_mgr=mgr)
print(report.to_markdown())
# Sprint retro: derived from SprintMetrics.health (complete/on-track/at-risk/
# off-track) plus scope-creep detection; mutates sprint.retrospective in place
retro = generate_sprint_retro(sprint)
# Backlog retro: mines DONE/BLOCKED/DEFERRED rows + the bug log for retro material
retro = generate_backlog_retro(tasks_md)
print(retrospective_to_markdown(retro, "Weekly Backlog Retro"))
# Next-sprint plan: WSJF-allocate portfolio capacity, create the sprint
# (left in PLANNING for a human to start) and add one UserStory per commit
plan = plan_next_sprint(portfolio_mgr, agile_mgr, name="Sprint 9", goal="Ship MVP", capacity=20)
print(plan.to_markdown())
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.
- 8d ago First seen · 89 lines · 23 tokens per session scan A dee4c8b624f0
agentic-agile is a skill published in the GitHub repository strikersam/autonomous-ai-agency (8 stars, last pushed today), licensed MIT. It adds 23 tokens to every session and 861 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.
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babysit-babysitter-issues
This skill should be used when the user asks to "babysit issues", "work on assigned issues", "check a5c-agent issues", "process babysitter issues", or wants to find and work on open GitHub issues assigned to a5c-agent in the babysitter repo.
cog-meeting-processing
Process meeting recordings and transcripts into decisions, action items, and team dynamics.
cog-team-intelligence
Cross-reference GitHub, Linear, Slack, and PostHog with bidirectional sync for team briefs.
spec-driven-development
Specification creation and management for the Pilot Shell methodology. Covers semantic search, clarifying questions, structured spec generation, and iterative refinement.
audit-trail
Full traceability from PRD to code commit through the CCPM spec-driven pipeline.