update-dedupe

A maintenance guide that learns from GitHub issues closed as duplicates and proposes updates to a repository's local duplicate-issue guidance. A duplicate issue is a report of a problem already tracked in another issue.

In plain words
What is it for?
Use it in GitHub Actions after duplicate-issue feedback has been collected, to identify repeated duplicate groups and write proposed changes under update-dedupe-output/.
Why use it?
It turns repeated maintainer decisions into written repository guidance, so future duplicate reports can be handled more consistently. It only proposes the guidance; the surrounding automation handles validation and publishing.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/terry-mao/aicodingflow/update-dedupe
Any agent
npx skills add Terry-Mao/AICodingFlow --skill update-dedupe
Clone the repo
git clone --depth 1 https://github.com/Terry-Mao/AICodingFlow

Made for: Claude Code, Codex.

Per session 27 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 988 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00027 $0.00988
Opus 5 $0.00014 $0.00494
Sonnet 5 $0.00005 $0.00198
Haiku 4.5 $0.00003 $0.00099

Measured 2d ago against content hash aaf95cfc6e32, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

update-dedupe 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.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/aggregate_dedupe_feedback.py, scripts/apply_guidance_output.py, scripts/validate_write_surface.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

.github/skills/update-dedupe/SKILL.md · 124 lines

How it starts

The opening of the file, as written. The whole thing — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.

update-dedupe

Use this skill to turn strong GitHub duplicate-closure evidence into concise updates to the repo-local dedupe-issue companion skill.

This skill owns only the self-evolution logic: how to interpret aggregated duplicate feedback and propose local guidance. The GitHub Actions runner owns data collection, write-surface validation, commits, pushes, and PR creation. When run inside GitHub Actions, .agents may be read-only. Write proposed changes only to update-dedupe-output/; the runner applies them.

Workflow

  1. Read the aggregated duplicate feedback JSON provided by the runner.
  2. Validate that the data contains only structured duplicate evidence.
  3. Identify repeated duplicate clusters where two or more independent issues were closed as duplicates of the same canonical issue.
  4. Compare the repeated clusters with the existing .github/skills/dedupe-issue-repo/SKILL.md content when available.
  5. Convert uncovered repeated clusters into concise repo-specific guidance.
  6. Write proposed local companion skill content to update-dedupe-output/.
  7. Stop; the runner validates and publishes the result.

Output Contract

Always write update-dedupe-output/status.json:

{
  "status": "changed",
  "reason": "Brief evidence summary.",
  "updated_files": [".github/skills/dedupe-issue-repo/SKILL.md"]
}

Allowed statuses:

  • changed when repeated maintainer duplicate evidence should update guidance
  • no_change when evidence is insufficient or already covered
  • error when the feedback cannot be interpreted safely

Use no_change when there is no repeated cluster. A single duplicate closure, comments that only suggest a duplicate, title similarity, or agent-only inference is not enough to update guidance.

For changed, write the complete replacement content for:

  • update-dedupe-output/dedupe-issue-repo/SKILL.md

Do not edit .agents directly.

Evidence Rules

Only learn from structured evidence supplied by the aggregation script:

Read the full file on GitHub · 124 lines

Files

What ships with it

3 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.

Changes

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.

  1. 2d ago First seen · 124 lines · 27 tokens per session scan A aaf95cfc6e32

Subscribe to this mod's changes

update-dedupe 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 988 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.

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