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 ZhangHanDong/agent-spec --skill agent-spec-estimategit clone --depth 1 https://github.com/ZhangHanDong/agent-specWrote 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/zhanghandong/agent-spec/agent-spec-estimate)<a href="https://agentmods.dev/skills/zhanghandong/agent-spec/agent-spec-estimate"><img src="https://agentmods.dev/badge/skills/zhanghandong/agent-spec/agent-spec-estimate/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/zhanghandong/agent-spec/agent-spec-estimate"><img src="https://agentmods.dev/badge/skills/zhanghandong/agent-spec/agent-spec-estimate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00116 | $0.02500 |
| Opus 5.5 | $0.00046 | $0.01000 |
| Sonnet 5.5 | $0.00023 | $0.00500 |
| Haiku 4.5 | $0.00012 | $0.00250 |
Grade A, and why
agent-spec-estimate 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 — 283 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Spec Estimate
Version: 1.3.1 | Last Updated: 2026-08-14 | Tracks: agent-spec 1.4.0
You are an expert at estimating AI agent work effort from structured Task Contracts. Help users by:
- Estimating specs: Read a
.spec/.spec.mdfile and produce a round-based effort estimate - Comparing tasks: Rank multiple specs by effort for sprint planning
- Risk assessment: Identify which Contract elements drive uncertainty
- Calibrating: Adjust estimates based on actual lifecycle retry counts
IMPORTANT: CLI Prerequisite Check
Before running any agent-spec command, Claude MUST check:
command -v agent-spec || cargo install agent-spec
If agent-spec is not installed, inform the user:
agent-specCLI not found. Install with:cargo install agent-spec
Quick Reference
| Action | Command | Output |
|---|---|---|
| Estimate a spec | agent-spec contract <spec> then apply estimation |
Round-based breakdown table |
| Batch estimate | Run on all specs in specs/ |
Sorted effort ranking |
| Calibrate from history | agent-spec explain <spec> --history |
Compare predicted vs actual rounds |
| Library sizing signal (0.3.0) | agent-spec audit --spec-dir specs --format json |
Counts of rules/scenarios/unproven-rules/open-questions to weight remaining effort |
0.3.0 signal: audit gives a mechanical library-level view —
unproven_rules and open_questions are leading indicators of remaining work
(a Rule with no proving Example, or an open Discovery question, is unfinished).
Use it as input to risk coefficients, not as a substitute for round estimation.
Core Method
Contract → Rounds Mapping
A Task Contract has structured elements that map directly to estimation inputs:
| Contract Element | Estimation Input | How It Affects Estimate |
|---|---|---|
| Completion Criteria scenarios | Module decomposition | Each scenario ≈ 1 module (1-15 rounds) |
| Decisions (fixed tech choices) | Risk reduction | Known tech → risk 1.0; new tech → risk 1.3-1.5 |
| Boundaries: Allowed Changes | Scope breadth | More paths → more modules; fewer paths → focused |
| Boundaries: Forbidden | Constraint overhead | Each prohibition adds 0-1 verification rounds |
| Constraints: Must NOT | Structural checks | Pattern avoidance adds ~1 round per constraint |
| Out of Scope | Scope control | Reduces estimate (explicitly excluded work) |
| inherits: project/org | Inherited overhead | Inherited constraints add ~1-2 rounds for compliance |
| Exception scenario count | Quality indicator | More exceptions = better spec but more rounds |
What ships with it
1 file 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.
- 1mo ago First seen · 283 lines · 116 tokens per session scan A b913757e58e6
agent-spec-estimate is a skill published in the GitHub repository ZhangHanDong/agent-spec (459 stars, last pushed 1mo ago), licensed MIT. It adds 116 tokens to every session and 2,500 once invoked, about $0.0005 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 2026-08-30.
Other skills, from other repositories
managing-mcp-servers
Use when the user asks to add, change, remove, enable or disable an MCP server, or when an MCP tool is missing, needs sign-in, fails, or was blocked. ThinkRail runs pi in-process, so there is no pi command to call.
subagent-driven-development
Use when executing implementation plans with independent tasks in the current session.
comet
Comet — OpenSpec + Superpowers dual-star development workflow. Start with /comet for automatic phase detection and dispatch to subcommands. Five phases: open → design → build → verify → archive.
comet-verify
Comet Phase 4: Verify and Close. Invoke with /comet-verify. Verify implementation matches design, handle development branch.
todos
Use when the user asks for a shared plan, a task needs at least three substantive execution steps, or a loose TODO is pending. Without an explicit plan request, not for one-shot answers or checks, or one- to two-step work that is not already in the list.
comet-hotfix
Comet preset path: Bug fix / hotfix. Skip brainstorming, directly open → build → verify → archive. Applicable for behavior fixes, scenarios not involving new capability design.