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/terry-mao/aicodingflow/create-tech-specnpx skills add Terry-Mao/AICodingFlow --skill create-tech-specgit clone --depth 1 https://github.com/Terry-Mao/AICodingFlowWhat 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 | $0.00078 | $0.01072 |
| Opus 5 | $0.00039 | $0.00536 |
| Sonnet 5 | $0.00016 | $0.00214 |
| Haiku 4.5 | $0.00008 | $0.00107 |
Grade A, and why
create-tech-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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
create-tech-spec
Create a tech spec from a GitHub issue for this repository.
Overview
This skill is a wrapper around the local shared tech-spec workflow:
.agents/skills/write-tech-spec/SKILL.md
Use that shared local skill as the base behavior and structure unless this wrapper overrides it. Keep the same emphasis on grounding the plan in current code, documenting relevant files and data flow, explaining tradeoffs, and defining validation.
The differences are:
- the primary input is a GitHub issue, not a Linear issue
- the output path is
specs/issue-<issue-number>/tech.md - the workflow or prompt provides the issue context path; in CI this is often
issue_context.json, while local wrappers should provide a path in a system temporary directory - the workflow or prompt may provide an issue comments path; in CI this is
often
issue_comments.txt - a workflow may also request a structured PR metadata output path; in CI this
is often
pr-metadata.json - do not create or edit Linear issues as part of this workflow
Inputs
Expect issue details in the issue context file named by the prompt, including
the issue number, title, description, labels, assignees, triggering comment
when present, exact product_spec path, and exact tech_spec path. If the
prompt does not provide an explicit path, use issue_context.json in the
current workflow worktree.
When available, the product spec at the product_spec path from issue_context.json should be treated as the primary input for understanding the intended behavior. The tech spec translates that product intent into an implementation approach.
Workflow
- Start from the local shared
write-tech-specguidance and follow its structure and writing standards unless this wrapper says otherwise. - Read the prompt-provided issue context path carefully. Read the product spec
from the exact
product_specpath first to understand the intended behavior. If a prompt-provided issue comments path exists, review it for clarifications, prior decisions, and design nuance that should influence the tech plan. - Inspect the repository to understand the current implementation and the likely scope of the requested work before writing the spec. Do not guess about current architecture when the code can be inspected directly.
- Create or update the exact
tech_specpath fromissue_context.json. - Use the shared skill's structure as the baseline, adapted to this repository and issue format. At minimum, cover:
- problem
- relevant code
- current state
- proposed changes
- end-to-end flow when useful
- risks and mitigations
- testing and validation
- follow-ups or open technical questions
- Keep the tech spec concise, actionable, and grounded in actual code paths and ownership boundaries in this repository.
- Do not implement the feature or modify production code as part of this task. Limit changes to the tech spec artifact and any minimal repository metadata needed to support it. Treat temporary context and comments files as scratch input only and do not commit them.
- Do not include issue number references (e.g.
(#N),Refs #N) in commit messages. The issue is already linked in the PR. - If the prompt asks for PR metadata, write it to the exact metadata output
path named by the prompt. If no explicit path is provided, use
pr-metadata.jsonin the current workflow worktree. The file must contain a JSON object with the fieldsbranch_name,pr_title, andpr_summary. Thepr_summaryshould summarize the resulting spec changes, validation, and any reviewer-relevant assumptions or open questions. For spec-only PRs, include a non-closing reference to the source issue such asRefs #<issue-number>rather than closing keywords likeClosesorFixes. - Default behavior: do not stage files, create commits, push branches, open pull requests, or use the GitHub CLI.
- In your final response, provide a brief summary of the tech spec and call out any assumptions or open questions so the workflow can reuse that summary when creating the PR.
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 · 84 lines · 78 tokens per session scan A 8c7162630d85
create-tech-spec is a skill published in the GitHub repository Terry-Mao/AICodingFlow (165 stars, last pushed 4d ago), licensed MIT. It adds 78 tokens to every session and 1,072 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 2026-08-30.
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