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 skills/sjarmak/agent-workflows/focusnpx skills add sjarmak/agent-workflows --skill focusgit clone --depth 1 https://github.com/sjarmak/agent-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 | $0.00000 | $0.05784 |
| Opus 5 | $0.00000 | $0.02892 |
| Sonnet 5 | $0.00000 | $0.01157 |
| Haiku 4.5 | $0.00000 | $0.00578 |
Grade A, and why
focus 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 3d 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 — 506 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Single-task execution loop with structured context handoff. Enforces the discipline of plan → execute → simplify → review → close. Uses Beads (bd) for task tracking so context survives across sessions without artifact clutter. A new session runs bd ready and picks up exactly where the last one left off. Supports parallel execution of independent beads via worktree-isolated subagents.
Arguments
$ARGUMENTS — format: [bead-id | "new task description" | "parallel"]
- If a bead ID is provided (e.g.,
agent-workflows-a3f2), work on that bead. - If a quoted or unquoted description is provided, create a new bead.
- If
parallelis provided, jump directly to parallel recommendation flow. - If no argument is provided, run
bd readyand let the user pick (includes parallel recommendations).
Parse Arguments
Extract:
- bead_id: an existing bead ID (matches pattern
*-<hex>) - task_description: free text describing new work
- parallel_mode: true if argument is "parallel"
If neither is provided, proceed to Phase 0 (Select).
Phase 0: Select
First, verify bd is available by running bd status.
If bd is not initialized, run bd init and inform the user that Beads has been set up.
Run these commands to gather full context:
bd ready --pretty
bd ready --json
bd graph --all --compact
Analysis: Priority & Parallelism
With the ready beads and dependency graph in hand, perform this analysis:
-
Sort by priority: Group ready beads by priority level (P0 > P1 > P2 > P3). Within the same priority, prefer beads with more dependents (unblocks more work).
-
Detect parallel sets: Using the
bd graph --all --compactoutput, identify beads that are in the same dependency layer (LAYER 0, LAYER 1, etc.). Beads in the same layer with no shared parent-child or blocking relationships can execute in parallel. Cross-reference withbd ready --jsonto check that candidate beads don't touch overlapping file paths (check descriptions and notes for file references).
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.
- 3d ago First seen · 506 lines · 0 tokens per session scan A c80304c0907f
focus is a skill published in the GitHub repository sjarmak/agent-workflows (9 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 5,784 tokens. 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 skills, from other repositories
speckit.specify
Skill "speckit.specify" from caipe-io/ai-platform-engineering, covering user input, outline, quick guidelines, section requirements and for ai generation.
speckit.clarify
Skill "speckit.clarify" from caipe-io/ai-platform-engineering, covering user input and outline.
local-integration-testing
Run end-to-end integration tests with all 15 agents and supervisor in local Docker Compose dev environment. Validates agent discovery, multi-agent routing, checkpoint persistence, and cross-agent follow-up conversations.
release-docs
Generate a combined release blog post for ai-platform-engineering. Produces a single docs/releases/YYYY-MM-DD-release-X-Y-Z.md file containing release notes and the upgrade guide (migration guide) inline. Use when cutting a release, when a user asks "what changed in 0.4.x", or when upgrading their values.yaml to a new…
update-docs
Audit and update all documentation moving parts for ai-platform-engineering. Checks release blog posts, features page, agent docs, homepage version strings, Docusaurus version config, and sidebar completeness. Fixes what is stale and reports what needs manual attention. Use after cutting a release, adding a new agent…
docker-compose-first-install
Validate and repair the OSS first-install Docker Compose path. Use when editing docker-compose.yaml, docker-compose.dev.yaml, .env.example, release image tags, Compose profiles, Keycloak/OpenFGA/RAG defaults, or first-launch UI behavior for local all-in-one installs.