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 browoz/agentic-sdlc-skills --skill agentic-specgit clone --depth 1 https://github.com/browoz/agentic-sdlc-skillsWrote 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/browoz/agentic-sdlc-skills/agentic-spec)<a href="https://agentmods.dev/skills/browoz/agentic-sdlc-skills/agentic-spec"><img src="https://agentmods.dev/badge/skills/browoz/agentic-sdlc-skills/agentic-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/skills/browoz/agentic-sdlc-skills/agentic-spec"><img src="https://agentmods.dev/badge/skills/browoz/agentic-sdlc-skills/agentic-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 | $0.00091 | $0.00571 |
| Opus 5.5 | $0.00036 | $0.00228 |
| Sonnet 5.5 | $0.00018 | $0.00114 |
| Haiku 4.5 | $0.00009 | $0.00057 |
Grade A, and why
agentic-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 2mo 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentic Spec
Use this skill to convert intent into a compact engineering contract before implementation. Keep the result proportional; do not create ceremony for work that only needs a direct fix.
Workflow
- Classify rigor:
prototype: throwaway exploration, personal tool, or demo.internal: team feature, workflow automation, or shared tool.production: user-facing, security-sensitive, data-sensitive, or agentic system with tools/actions.
- Gather the six context types:
- Instructions: roles, constraints, repo rules, user priorities.
- Knowledge: APIs, schemas, business rules, source docs.
- Memory: relevant project state and previous decisions.
- Examples: existing patterns, input/output examples, nearby code.
- Tools: required APIs, MCP servers, CLIs, sandboxes, permissions.
- Guardrails: explicit boundaries, non-goals, unsafe actions.
- Generate
SPEC.mdfromassets/templates/SPEC.md. - For AGENTS.md work, apply the filter test: omit anything discoverable from the codebase, keep project-level rules under 200 lines, and push scoped rules into subdirectories.
- If requirements are materially ambiguous, ask only the blocking question. Otherwise record assumptions in the spec and continue.
Rigor Scaling
- Prototype: one-page spec, 1-2 edge cases, manual verification acceptable.
- Internal: focused spec, at least 3 edge cases, automated test plan expected.
- Production: full six-context spec, threat/data boundaries, measurable success criteria, eval/security/deploy follow-up links.
References
Read ../agentic-engineering-sdlc/references/day1_sdlc_and_context.md only
when deeper context-engineering or AGENTS.md guidance is needed.
Done Criteria
SPEC.mdhas goal, context, constraints, edge cases, out of scope, and success criteria.- Rigor level is explicit and justified.
- Assumptions and user decisions are separated.
- Next recommended skill is named only when useful:
agentic-evals,agentic-security-review, oragentic-production-readiness.
What ships with it
2 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.
- 2mo ago First seen · 59 lines · 91 tokens per session scan A 310568853d18
agentic-spec is a skill published in the GitHub repository browoz/agentic-sdlc-skills (4 stars, last pushed 4mo ago), licensed MIT. It adds 91 tokens to every session and 571 once invoked, about $0.0004 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.
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