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 commands/techtalk/ai-readiness-assessment/spec-implementgit clone --depth 1 https://github.com/techtalk/ai-readiness-assessmentWrote 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/commands/techtalk/ai-readiness-assessment/spec-implement)<a href="https://agentmods.dev/commands/techtalk/ai-readiness-assessment/spec-implement"><img src="https://agentmods.dev/badge/commands/techtalk/ai-readiness-assessment/spec-implement.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 | $0.00041 | $0.00344 |
| Opus 5 | $0.00020 | $0.00172 |
| Sonnet 5 | $0.00008 | $0.00069 |
| Haiku 4.5 | $0.00004 | $0.00034 |
Grade A, and why
spec-implement 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 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.
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.
What it actually says
/spec-implement
Project-local command. Orchestrates the spec-first workflow.
Usage
/spec-implement specs/NNNN-<slug>.md
Process
- Read the spec. Identify the "what", the "why", the acceptance criteria, and the explicit "out of scope" boundaries.
- Draft the plan. Write a plan under
specs/plans/NNNN-<slug>-plan.mdcovering: approach, steps, risks, verification. - Pause for adversarial review. Tell the human: "Plan is at
specs/plans/NNNN-<slug>-plan.md. Walk through the adversarial review and record dispositions underdocs/objections/NNNN-<slug>.md. Return here when all dispositions are resolved." - Wait for human confirmation that objections are resolved.
- Write the failing test described in the acceptance criteria.
- Confirm the test is red for the right reason.
- Write the minimal production code to make the test pass.
- Run the full test suite and the linter. Confirm green.
- Open the PR, referencing the spec by path in the PR body.
Do not proceed past step 3 until the human confirms the objection dispositions are resolved.
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 · 37 lines · 41 tokens per session scan A e6b87e3b5865
spec-implement is a command published in the GitHub repository techtalk/ai-readiness-assessment (9 stars, last pushed 16d ago), licensed Apache-2.0. It adds 41 tokens to every session and 344 once invoked, about $0.0002 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.
Other commands, from other repositories
score
Run bun-sticky on the current directory to check FAF score.
test
Run the championship-grade test suite and report results.
cost-estimate
Estimate a target's tokens, agent-compute time, and (when grounded) cost before it runs — the prospective sibling of /cost-capture. Dispatches the read-only cost-estimator agent, validates the returned record, and writes it after you dispose.
harness-sync
Detect drift across all push-direction control surfaces, present the full picture, and apply the user's selected fixes via the existing primitives — single human-instigated entry point for keeping convention files in sync with HARNESS.md. ONBOARDING.md staleness is surfaced but not auto-fixed; users run…
superpowers-status
Show the complete health of the project's AI Literacy habitat — harness enforcement, agent team, compound learning, model routing, and CI status.
assess
Run an AI literacy assessment — scan the repo for evidence, ask clarifying questions, produce a timestamped assessment document, apply immediate habitat fixes, recommend workflow changes, capture a reflection, and add a literacy level badge to the README.