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/dedupe-issuenpx skills add Terry-Mao/AICodingFlow --skill dedupe-issuegit 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 | $0.00027 | $0.00908 |
| Opus 5 | $0.00014 | $0.00454 |
| Sonnet 5 | $0.00005 | $0.00182 |
| Haiku 4.5 | $0.00003 | $0.00091 |
Grade A, and why
dedupe-issue 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Detect duplicate issues
Compare a newly filed GitHub issue against candidate issues provided by the workflow and identify likely duplicates by similarity of title and description.
Inputs
Expect the prompt to include:
- the incoming issue's number, title, and description
- candidate issues prepared by the outer workflow
- the repository owner/name as context
Duplicate detection procedure
- Use the candidate issues prepared by the outer workflow as the comparison set.
- If no candidate issues are available, report that duplicate checking could not be verified in
summaryorissue_body, leaveduplicate_ofempty, and continue triage from the available local inputs. - Normalize the incoming issue's title and description by lowercasing, stripping leading/trailing whitespace, and collapsing runs of whitespace into single spaces.
- For each candidate issue in the comparison set:
a. Compute title similarity: compare the incoming title to the candidate title. Consider them title-similar when they share the same core noun phrases or intent after stripping common prefixes like "bug:", "feature:", "[request]", emoji, and markdown formatting.
b. Compute description similarity: compare the key symptoms, error messages, reproduction steps, and requested behavior between the incoming and candidate descriptions. Ignore boilerplate template sections (e.g., "## Environment", "## Steps to Reproduce" headers with empty content) that do not carry diagnostic signal.
c. A candidate is a likely duplicate when both of the following hold:
- The titles convey the same problem, feature request, or question (not merely sharing a common keyword).
- The descriptions overlap on at least one substantive detail: a shared error message, the same failing behavior, the same requested capability, or an equivalent reproduction scenario.
- Rank candidates by overall similarity (title weight ≈ 40%, description weight ≈ 60%) and select the top matches.
- Only flag an issue as a duplicate when 2 or more existing issues are identified as likely duplicates. A single weak match is not sufficient — the evidence must be corroborated across multiple existing issues to reduce false positives.
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 · 73 lines · 27 tokens per session scan A 75ca811ac9ed
dedupe-issue is a skill published in the GitHub repository Terry-Mao/AICodingFlow (165 stars, last pushed 5d ago), licensed MIT. It adds 27 tokens to every session and 908 once invoked, about $0.0001 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.
Other skills, from other repositories
babysit
Same-session monitoring loop for PRs, CI runs, tickets, and deployments using the monitorstart / monitorupdate / autonudgestop MCP tools. The loop re-injects your check instructions into THIS session on an idle interval — same context, same tools — and works from dashboard chat, Slack threads, and Discord DMs. Use…
agile-product-owner
../../../product-team/agile-product-owner/skills/agile-product-owner/SKILL.md.
dot-ai-prd-create
Create documentation-first PRDs that guide development through user-facing content.
agent-upkeep
Perform one small, scoped maintenance improvement to the Elements monorepo and open a single reviewable pull request. Use this skill for scheduled or unattended upkeep runs that improve unit test coverage for one file, fix one behavioral bug in one module, or move one off ESLint rule toward enforcement to reduce…
organize-project
Organise documents into folders and move them between folders and modules with MOVE. Use when a module has grown unstructured, when restructuring a project, or when a document is in the wrong place.
smithers-supervise
Watch for stale running runs and auto-resume them. Run smithers supervise --help for usage details.