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/vincentkoc/dotskills/codex-goal-miningnpx skills add vincentkoc/dotskills --skill codex-goal-mininggit clone --depth 1 https://github.com/vincentkoc/dotskillsWrote 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/vincentkoc/dotskills/codex-goal-mining)<a href="https://agentmods.dev/skills/vincentkoc/dotskills/codex-goal-mining"><img src="https://agentmods.dev/badge/skills/vincentkoc/dotskills/codex-goal-mining.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 5.1 | $0.00094 | $0.00808 |
| Opus 5 | $0.00047 | $0.00404 |
| Sonnet 5 | $0.00019 | $0.00162 |
| Haiku 4.5 | $0.00009 | $0.00081 |
Grade A, and why
codex-goal-mining 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 5d 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codex Goal Mining
Purpose
Recover structured Codex goal history and convert free-form runs into evidence-backed, repeatable suites.
When to use
- The user asks what
/goalcommands ran locally or across the fleet. - Large QA, beta, release, localization, model, cleanup, or PR campaigns need retesting.
- The user wants active, paused, blocked, or long-running goals ranked.
- Repeated semantic runs need stable suite names and copy-paste prompts.
- Goal time, thread span, tokens, dates, machines, or reachability matter.
Workflow
- Collect structured data with
scripts/codex-goal-report.py.- Fleet report:
scripts/codex-goal-report.py --policy ~/.config/codex-goal-mining/fleet-policy.json --json --output ~/.codex/reports/codex-fleet-goals.json - Local only:
scripts/codex-goal-report.py --local --json - Recent window: add
--since YYYY-MM-DD. - Selected machines: repeat
--machine <fleet-alias>. - Start fleet configuration from
references/fleet-policy.example.json; never commit a real private inventory.
- Fleet report:
- Treat the data sources correctly.
- Prefer
goals_1.sqlitefor objective, status, tokens, and Codex-recorded active goal time. - Join
state_5.sqlitefor thread timestamps and rollout paths. - Use JSONL only as a fallback for older installations.
- Wall-clock thread span includes idle and resume gaps; never describe it as active labor.
- Prefer
- Report collection coverage first.
- List reached and unreachable machines.
- Give the date range, goal count, statuses, total active goal time, and median goal time.
- Preserve exact transport blockers instead of silently shrinking the fleet.
- Mine patterns semantically.
- Exact duplicate text is weak evidence because operators rephrase goals.
- Cluster by intended test surface, matrix, exit criteria, and repeated operating contract.
- Prioritize unfinished goals and recurring high-time campaigns.
- Separate product beta suites from operational queues such as contributor PR sweeps.
- Produce reusable reruns.
- Use
[suite:<name>] [baseline:<sha-or-date>] [matrix:<targets>] [exit:<criteria>]. - Include a fixed matrix, evidence requirements, blocker rules, and definition of done.
- Start from
references/goal-suite-patterns.md, then adapt to current evidence.
- Use
- Keep reports private.
- Default to
~/.codex/reports/or another private path. - Scrub secrets, private hosts, personal absolute paths, and credentials before creating a gist or sharing externally.
- Use a secret gist unless the user explicitly requests public visibility.
- Default to
What ships with it
6 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.
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.
- 5d ago First seen · 61 lines · 94 tokens per session scan A 5d8e2f97618b
codex-goal-mining is a skill published in the GitHub repository vincentkoc/dotskills (102 stars, last pushed yesterday), licensed MIT. It adds 94 tokens to every session and 808 once invoked, about $0.0005 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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