Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add nguyenvanphituoc/shapeup-sdlc-plugin/plugin install shapeup-sdlc-pluginWrote 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/nguyenvanphituoc/shapeup-sdlc-plugin/spec-evaluator)<a href="https://agentmods.dev/skills/nguyenvanphituoc/shapeup-sdlc-plugin/spec-evaluator"><img src="https://agentmods.dev/badge/skills/nguyenvanphituoc/shapeup-sdlc-plugin/spec-evaluator.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 5.1 | $0.00117 | $0.03587 |
| Opus 5 | $0.00059 | $0.01793 |
| Sonnet 5 | $0.00023 | $0.00717 |
| Haiku 4.5 | $0.00012 | $0.00359 |
Grade A, and why
spec-evaluator 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 7d 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 — 231 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spec Evaluator (the single judge, pure worker v1.0)
Assume broken until proven working. Grade evidence, not claims. Return data, not writes.
The judge in a planner → generator → evaluator loop. It reads the committed spec, exercises the running app, and returns a hard-threshold verdict plus a file:line bug list — as a WorkResult envelope the orchestrator ingests. The generator fixes; the evaluator re-runs.
Core guarantee — skeptical by default. Out-of-the-box an LLM is a lenient QA: it finds a real defect, then talks itself into approving anyway. This skill inverts that posture. A criterion with no collected evidence is a FAIL, never a pass-by-assumption.
Anti-leniency protocol →
references/anti-leniency.md— read before printing any verdict. Verdict ledger (re-probe + confidence + flip detection) →references/verdict-ledger.md. Dimension contract (injection interface) →references/dimension-contract.md. Where any reference file describes writing shared state (task files,.verdictsledger, run-state), the pure-worker contract overrides it: that data returns in the WorkResult and the orchestrator's ingest script performs the write. Old gate names in reference files map 1:1 onto the core process below: GATE V0/V0.5 = input contract + dimension resolution, GATE V1 = CONTRACT, Phase A = PROBE, GATE V2 = VERDICT, Phase B = REPORT; GATE V3 (sign-off) is retired — pausing is the caller'sinteractionpolicy.
Input contract — the WorkOrder
Invoked as --order <path>. Fields you may rely on (absent = unknown, never inferred):
| Field | What it is |
|---|---|
payload.spec_folder |
The committed grading truth: usecases/ + domain-model.md (+ contracts/, scope-summary.md, _index.md). No usecases/ → HARD STOP, nothing to grade against |
payload.feature |
Feature slug — scopes the probe and names the report |
payload.dimensions[] |
The active dimension set (the caller resolved precedence). Absent → [spec-conformance] + the auto-enable rules below |
payload.run_cmd |
How to start the running app. Absent standalone → ask; absent orchestrated → ESCALATE, do not guess |
payload.t0_artifacts[] |
Per-scope T0 verdict paths for this round (scoped specs). An artifact listed but missing/red on disk, or a scoped spec with none listed → the round is NOT gradeable: return status: failed naming the scope — a structural precondition, not a criterion |
payload.browser |
cli (default, ~4x cheaper) | mcp | none |
payload.tasks[] |
Traceability only (which UCs a task claims): NEVER a grading source — the committed UC text is the criterion, a paraphrase mismatch is a finding |
substrate.allowed |
Your only write surface: .shapeup/<slug>/evaluation/** (the report + evidence) |
What ships with it
13 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.
- references/anti-leniency.md 2.9 KB
- references/dimension-contract.md 4.7 KB
- references/dimensions/_registry.md 2.9 KB
- references/dimensions/completeness.md 4.8 KB
- references/dimensions/integration.md 9.3 KB
- references/dimensions/performance.md 1.3 KB
- references/dimensions/security.md 1.9 KB
- references/dimensions/spec-conformance.md 5.9 KB
- references/dimensions/tdd-surface.md 5.0 KB
- references/dimensions/test-surface-conformance.md 4.7 KB
- references/probing.md 12 KB
- references/report-schema.md 4.2 KB
- references/verdict-ledger.md 5.1 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.
- 7d ago First seen · 231 lines · 117 tokens per session scan A 4527278a14e7
spec-evaluator is a skill published in the GitHub repository nguyenvanphituoc/shapeup-sdlc-plugin (2 stars, last pushed today), licensed MIT. It adds 117 tokens to every session and 3,587 once invoked, about $0.0006 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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