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.
git clone --depth 1 https://github.com/dork-labs/dorkosWrote 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/commands/dork-labs/dorkos/from-spec)<a href="https://agentmods.dev/commands/dork-labs/dorkos/from-spec"><img src="https://agentmods.dev/badge/commands/dork-labs/dorkos/from-spec/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/commands/dork-labs/dorkos/from-spec"><img src="https://agentmods.dev/badge/commands/dork-labs/dorkos/from-spec.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00008 | $0.01069 |
| Opus 5.5 | $0.00003 | $0.00428 |
| Sonnet 5.5 | $0.00002 | $0.00214 |
| Haiku 4.5 | $0.00001 | $0.00107 |
Grade A, and why
from-spec 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Extract ADRs from Specification
Spec Slug: $ARGUMENTS
Extract the architecturally significant decisions from a spec into ADRs. Significance is judged at creation time — no ADR is ever created with status: draft, and there is no separate curation pass.
Steps
Step 1: Read the Specification
Read specs/$ARGUMENTS/01-ideation.md (exploration and research) and specs/$ARGUMENTS/02-specification.md (final decisions).
Step 2: Check for Existing ADRs
Read decisions/manifest.json and list any existing ADRs that already reference this spec slug — don't create duplicates.
Step 3: Identify Candidate Decisions
Scan the spec documents for decision signals: technology choices ("we chose X", "X instead of Y"), pattern adoption, trade-off resolutions, and rejected alternatives.
Step 4: Score Each Candidate Against the Significance Rubric
| # | Criterion | How to Assess |
|---|---|---|
| 1 | Chooses between alternatives | Does it describe selecting X over Y? Or just "we used X"? |
| 2 | Project-wide impact | Does this affect how future features are built, beyond the originating spec? |
| 3 | Would surprise a new team member | Is this a non-obvious choice that needs explanation? |
| 4 | Adopts a lasting pattern or technology | New library, architecture pattern, data model with long-term consequences? |
Score = number of criteria met (0-4).
- Criterion 2 (project-wide impact) met AND score >= 2 → write the ADR (Step 5)
- Otherwise → do not write it; list it in the Step 6 summary with its score so the judgment is auditable
Criterion 2 is mandatory, not one vote among four: the 2026-08 corpus audit
(research/20260806_adr-corpus-health-and-lifecycle.md) found the sub-2 bar had admitted
single-surface implementation details (component variants, indicator placement) that rot the
fastest and are visible from the code anyway. A decision confined to one feature belongs in the
spec, not the decision log.
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 · 79 lines · 8 tokens per session scan A fe1fdb4975ef
from-spec is a command published in the GitHub repository dork-labs/dorkos (12 stars, last pushed today), licensed MIT. It adds 8 tokens to every session and 1,069 once invoked, about $0.0000 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 2026-08-31.
Other commands, from other repositories
review-pr
Comprehensive PR review using specialized agents.
asana-setup
Set up the Asana V2 MCP server connection (one-time OAuth app + claude mcp add).
clean_gone
Cleans up all git branches marked as [gone] (branches that have been deleted on the remote but still exist locally), including removing associated worktrees.
revise-claude-md
Update CLAUDE.md with learnings from this session.
example-command
An example slash command that demonstrates command frontmatter options (legacy format).
new-sdk-app
Create and setup a new Claude Agent SDK application.