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Get Started Free →Turn a rough coding or repo idea into a spec, executable plan, and long-running Codex /goal contract. Use when the user wants to create a coding spec, spec out a repo feature, tighten a coding spec, turn an implementation idea into a /goal-ready plan, prepare for autonomous coding, forge a goal, start a /goal, generate GOAL.md, prepare measurable done_when criteria, or check Codex config readiness.
.claude/skills/michaelpersonal-goal-forge/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 56% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 256% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 72% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 378% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 44% | 0% |
Goal Forge converts a rough coding idea into an executable Codex /goal contract.
Default pipeline:
SPEC.md.SPEC.md until scope, scoring, feedback, and verification are concrete.SPEC.md into GOAL.md./goal.Use this skill for coding and repo work first. If the task is mostly exploratory, research-only, security-critical, or lacks verifiable completion criteria, keep the user in the loop instead of compiling a /goal.
Long-running goals need a runtime harness, not just a task description. Every /goal contract should make four things explicit:
Choose the smallest mode that satisfies the request.
SPEC.md and wants it challenged, clarified, or made executable.SPEC.md and wants a /goal prompt or GOAL.md.When the user gives a rough idea or asks to create a spec:
SPEC.md if present. Otherwise treat the rough idea as the interview seed; do not draft SPEC.md until step 6./goal.SPEC.md.Use structured user-question tooling when available. If it is not available, ask concise batches of questions in chat. Keep each batch focused on unresolved blockers.
Hard gate: do not compile a /goal prompt until done_when contains concrete, user-approved success criteria. After the interview has covered scope, architecture, scoring, and verification, Codex may propose candidate done_when criteria as a starting point. The user must confirm or edit product and acceptance criteria before the spec is considered complete.
For long-running or exploratory goals, also identify:
PLAN.md, ATTEMPTS.md, and NOTES.mdRead SPEC.md skeptically before any execution prompt is created.
For each ambiguity:
Do not add scope. Remove or mark anything that cannot be verified. Preserve clear existing decisions.
The tightened spec should include:
done_when criteriaCompile SPEC.md into GOAL.md using the block structure in references/goal_prompt_blocks.md. Load references/standard_execution_rules.md for default <execution_rules> content. If scaffolding long-run tracking files, load references/working_memory_templates.md. If the goal benefits from human steering during a long run, load references/control_surface_templates.md.
Before writing GOAL.md, reject weak specs that lack:
done_whenIf the spec is weak, route the user back to Interview or Tighten mode with a specific list of missing decisions. Do not write GOAL.md until the gaps are resolved.
Concrete gate: each done_when item must name a command, file artifact, or user-observable behavior. Otherwise mark it as weak and ask for clarification.
Scorecard gate: each scorecard must name the primary metric or checklist, the passing threshold, regression checks, the scoring command or inspection path, and the stop condition. If the score is model-judged, require a checklist or rubric that makes the judgment auditable.
Feedback-loop gate: each feedback_loop must name a fast check, expected runtime, run cadence, why the proxy is representative enough, and the slower escalation or final check. If the only available check is slow, say so explicitly and adjust the workflow cadence.
Working-memory gate: if the goal may run for hours, involves repeated experiments, or has high context churn, include a working_memory block. Prefer PLAN.md, ATTEMPTS.md, and NOTES.md unless the repo already has equivalent files. If no working-memory files are needed, state why the goal is short and linear enough to skip them.
Human-control gate: if a goal may run while the user or another Codex session reviews in parallel, may consume scarce resources, may require strategic pivots, or may benefit from human priority changes, include a human_control_surface block and scaffold a compact CONTROL.md. Keep CONTROL.md small: only include knobs relevant to this goal. Do not create a second GOAL.md full of static instructions.
Map the spec into the goal blocks:
<goal><context><constraints><scorecard><done_when><feedback_loop><workflow><working_memory><human_control_surface><verification_loop>references/standard_execution_rules.md plus repo-specific rules -> <execution_rules><output_contract>After writing GOAL.md, self-check it:
done_when is measurable and user-approvedscorecard tells Codex how to score progress and stopfeedback_loop is fast enough to run repeatedly, or its limits are explicitworking_memory is present for long-running or exploratory workhuman_control_surface is present when the user needs visibility or tuning knobs during a long run, and absent when it would be ceremony/goalUse scripts/inspect_codex_config.py for a read-only report:
bashpython3 ~/.codex/skills/goal-forge/scripts/inspect_codex_config.py --project-path /path/to/project
Run it from the target project root, or pass --project-path. The script reports the installed Codex version, selected config keys, trusted-project status, and gaps against the autonomous /goal configuration.
Before telling the user a long-running /goal is ready, verify the config against references/config_checklist.md. The important autonomous settings are:
model = "gpt-5.5"model_context_window = 1050000model_auto_compact_token_limit = 997500model_reasoning_effort = "high"plan_mode_reasoning_effort = "xhigh"approval_policy = "never"sandbox_mode = "danger-full-access"[features] goals = trueDo not edit ~/.codex/config.toml unless the user explicitly asks for config changes. If asked to change config, prefer applying the dangerous approval_policy = "never" and sandbox_mode = "danger-full-access" settings only for explicitly trusted project paths rather than globally.
Read references/config_checklist.md when explaining config tradeoffs.
Depending on the request, produce one or more of:
SPEC.mdGOAL.mdPLAN.md, ATTEMPTS.md, and NOTES.md scaffolds for long-running goalsCONTROL.md scaffold for long-running goals that need human visibility or tuning knobs/goal prompt body to paste into CodexWhen the user asks to run the goal, verify that their Codex version supports /goal first.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-14 | pass→fail | 17,871 | 3,675 | -79% | 1 | 1 | 0% | 3,066 | 2,662 | -13% | 0 | 0 | — |
case-01 | fail→fail | 26,938 | 4,492 | -83% | 1 | 1 | 0% | 4,147 | 2,591 | -38% | 0 | 0 | — |
case-02 | fail→fail | 22,231 | 3,713 | -83% | 1 | 1 | 0% | 3,950 | 2,501 | -37% | 0 | 0 | — |
case-03 | fail→fail | 9,901 | 6,148 | -38% | 1 | 1 | 0% | 1,521 | 2,631 | +73% | 0 | 0 | — |
case-04 | fail→pass | 14,682 | 13,212 | -10% | 1 | 1 | 0% | 2,482 | 3,877 | +56% | 0 | 0 | — |
case-05 | fail→pass | 6,085 | 6,376 | +5% | 1 | 1 | 0% | 929 | 3,306 | +256% | 0 | 0 | — |
case-06 | fail→fail | 3,242 | 4,452 | +37% | 1 | 1 | 0% | 525 | 2,530 | +382% | 0 | 0 | — |
case-07 | fail→pass | 11,411 | 5,515 | -52% | 1 | 1 | 0% | 1,841 | 3,173 | +72% | 0 | 0 | — |
case-08 | fail→fail | 14,620 | 6,840 | -53% | 1 | 1 | 0% | 2,317 | 3,376 | +46% | 0 | 0 | — |
case-09 | fail→pass | 5,202 | 11,699 | +125% | 1 | 1 | 0% | 865 | 4,131 | +378% | 0 | 0 | — |
case-10 | fail→fail | 15,350 | 6,396 | -58% | 1 | 1 | 0% | 2,540 | 2,593 | +2% | 0 | 0 | — |
case-11 | fail→fail | 6,469 | 5,527 | -15% | 1 | 1 | 0% | 373 | 2,565 | +588% | 0 | 0 | — |
case-12 | fail→pass | 32,021 | 10,401 | -68% | 1 | 1 | 0% | 2,672 | 3,853 | +44% | 0 | 0 | — |
case-13 | fail→pass | 15,952 | 26,931 | +69% | 1 | 1 | 0% | 2,512 | 6,973 | +178% | 0 | 0 | — |
case-15 | fail→fail | 6,493 | 13,590 | +109% | 1 | 1 | 0% | 968 | 4,762 | +392% | 0 | 0 | — |
case-16 | fail→fail | 7,061 | 3,621 | -49% | 1 | 1 | 0% | 1,070 | 2,934 | +174% | 0 | 0 | — |
case-17 | fail→pass | 7,169 | 5,903 | -18% | 1 | 1 | 0% | 1,110 | 3,061 | +176% | 0 | 0 | — |
case-18 | fail→fail | 12,909 | 5,838 | -55% | 1 | 1 | 0% | 1,998 | 2,726 | +36% | 0 | 0 | — |
case-19 | fail→pass | 14,404 | 7,751 | -46% | 1 | 1 | 0% | 2,552 | 3,496 | +37% | 0 | 0 | — |
case-20 | pass→fail | 6,437 | 12,404 | +93% | 1 | 1 | 0% | 1,171 | 4,479 | +282% | 0 | 0 | — |
case-21 | pass→fail | 6,146 | 14,025 | +128% | 1 | 1 | 0% | 1,090 | 4,855 | +345% | 0 | 0 | — |
case-22 | pass→fail | 9,848 | 13,391 | +36% | 1 | 1 | 0% | 1,712 | 4,421 | +158% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 16 counted toward the lift figure. The other 6 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +18 percentage points is the difference between those two pass rates over the 16 comparable cases. 4 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.