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/evgeny-birch/spec-driven-development-skill/spec-developmentnpx skills add evgeny-birch/spec-driven-development-skill --skill spec-developmentgit clone --depth 1 https://github.com/evgeny-birch/spec-driven-development-skillWrote 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/evgeny-birch/spec-driven-development-skill/spec-development)<a href="https://agentmods.dev/skills/evgeny-birch/spec-driven-development-skill/spec-development"><img src="https://agentmods.dev/badge/skills/evgeny-birch/spec-driven-development-skill/spec-development.svg" alt="Measured on agentmods" 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 | $0.00111 | $0.13122 |
| Opus 5 | $0.00056 | $0.06561 |
| Sonnet 5 | $0.00022 | $0.02624 |
| Haiku 4.5 | $0.00011 | $0.01312 |
Grade B, and why
spec-development scanned grade B with 1 finding 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.
Enumerates other installed skillsmediumAgent snooping
Other skills' SKILL.md files reveal prompts, capabilities and secrets that should be invisible to peers.
- `ls .claude/skills/` — for project-local skills How it starts
The opening of the file, as written. The whole thing — 585 lines — stays where its author put it; the contents beside it link to each section on GitHub.
spec-development
Writes and maintains specifications in three shapes, depending on the size and urgency of the work. All three live under docs/specs/ and share a single numbering sequence for regular work (SPEC-NNN), with hotfixes on a parallel HF-NNN sequence.
Which track?
Pick the track BEFORE writing anything. Mis-pick and you either over-document a tiny change or under-document a real feature.
| Situation | Track | Artifact |
|---|---|---|
| New feature, multi-surface, partner coordination, new data model / API, cross-cutting risk | Full triplet | epic.md + tasks.md + plan.md |
| Single well-understood change, 1–3 files of impact, low risk, one-sitting execution | Small spec | spec.md (one file) |
| Production is broken, user describes the problem, fix is needed now | Hotfix | hotfix.md (one file, written alongside the fix) |
When the user invokes the skill and the intent is ambiguous, ask which track before creating any file. Phrasing clues:
- "write a spec for …", "new feature …", "break this down" → full triplet candidate
- "small spec for …", "just a tiny change", "one-file fix" → small-spec candidate
- "hotfix: …", "prod is broken", "срочно фикс" → hotfix
If a small-spec starts to outgrow itself during execution (new API surface, cross-cutting concern emerges), stop and convert to a full triplet rather than cramming. Conversely, a full-triplet epic that shrank in scope during review can be demoted to a small-spec — one spec.md replaces the triplet.
Templates for all three shapes live under templates/.
Track 1 — Full triplet (epic + tasks + plan)
Three linked documents in docs/specs/SPEC-{NNN}-{slug}/:
epic.md— full description of the work: goals, context, requirements, UX, data model, architecture, acceptance criteria. Read by both an AI agent (as a prompt to decompose work) and a human (to validate intent).tasks.md— breakdown of the epic into concrete, actionable tasks. Agent-optimised — enough technical detail that any single task can be executed without reading its siblings.plan.md— execution plan: order, dependencies, parallelisable work.
What ships with it
14 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.
- templates/bug.md 3.8 KB
- templates/bugs-readme.md 6.4 KB
- templates/epic.md 14 KB
- templates/future-work.md 2.1 KB
- templates/hotfix.md 5.8 KB
- templates/plan.md 7.8 KB
- templates/prod-readiness.md 2.2 KB
- templates/recon.md 3.3 KB
- templates/review-log-template.md 1.5 KB
- templates/review-verdict-template.md 1.4 KB
- templates/reviewer-instructions.md 7.2 KB
- templates/small-spec.md 5.3 KB
- templates/tasks.md 7.0 KB
- templates/verification-checklist.md 19 KB
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 · 585 lines · 111 tokens per session scan B 20fc5ad44e1a
spec-development is a skill published in the GitHub repository evgeny-birch/spec-driven-development-skill (10 stars, last pushed 1mo ago), licensed MIT. It adds 111 tokens to every session and 13,122 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it B with 1 finding (enumerates other installed skills). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…