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/joymin5655/agent/specnpx skills add joymin5655/Agent --skill specgit clone --depth 1 https://github.com/joymin5655/AgentWhat 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 | $0.00076 | $0.02168 |
| Opus 5 | $0.00038 | $0.01084 |
| Sonnet 5 | $0.00015 | $0.00434 |
| Haiku 4.5 | $0.00008 | $0.00217 |
Grade A, and why
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 2d 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 — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/spec
Goal
Turn a fuzzy request into two committed artifacts — a spec (what/why) and a
plan (how, in waves) — before any implementation edit, then hand the plan to
the user for approval. Approval is what unlocks editing: plan-gate.py writes the
plan-approval flag, and spec-gate.py (a PreToolUse gate) stops asking once that
flag exists.
This skill supplies the methodology; the enforcement is a tool boundary. You
cannot prompt your way past spec-gate — you produce the artifacts and get the
plan approved, or you flip the mode. That separation is the point.
Steps
1. Brainstorm
Explore the request before committing to a shape:
a. Restate the request in one sentence and name the success criterion. b. Surface the 2-3 plausible approaches and the tradeoff between them; pick one and say why. If the request is ambiguous, ask — don't guess. c. List what's explicitly out of scope (prevents scope creep later).
--interview — opt-in deep-interview submode
The default brainstorm is a single pass. /spec <slug> --interview replaces
step 1b's one-shot "ask if ambiguous" with a structured question loop for
requests fuzzy enough that a wrong guess commits the whole spec to the wrong
shape. It is opt-in by design: simple requests keep the single pass, and the
enforcement story is untouched — the spec-gate tool boundary neither knows nor
cares which submode produced the artifacts.
- Unknowns table. List every unknown in the request; mark each decision-changing (Y/N) — would different answers produce different specs? N-unknowns are implementation details; leave them to the plan.
- Batch-question the Y rows only — at most 4 questions per round, each naming the options and your recommended default.
- Re-score after the answers. Answers resolve rows and surface new unknowns; add those to the table and mark them Y/N. This is the decision-tree pruning: each round should shrink the Y set.
- Terminate on either condition (whichever comes first):
- the table has zero open decision-changing unknowns, or
- 3 rounds have run.
Unresolved rows carry into
spec.mdunder## Open questions— named and deferred beats silently guessed.
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.
- 2d ago First seen · 187 lines · 76 tokens per session scan A 839e145a22d2
spec is a skill published in the GitHub repository joymin5655/Agent (2 stars, last pushed 8d ago), licensed MIT. It adds 76 tokens to every session and 2,168 once invoked, about $0.0004 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 2026-08-31.
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